Lung-brain axis

Source: https://github.com/markziemann/lungbrain

Introduction

Eslam’s Summary of RNA seq

• Project title: Characterization any gene expression changes in the mice brain in response to peripheral inflammation in the lung (asthma).

• Methodology: Mice were divided into three groups (1 control group and 2 asthmatic groups OVA and LPS). At the end of the experiment, RNA was isolated from 5 different mice brain regions (Hypothalamus, Amygdala, Hippocampus, PAG, and NI) (n=7 per group) using (Qiagen RNAeasy Mini kit 250) (Cat# 74106, Hilden, Germany). In total 104 RNA samples. Afterward, RNA quality control was checked using Qubit4 and 4200 Tapestation. Then RNA sequencing was done using NEBNext® Ultra™ II Directional RNA Library Prep Kit for Illumina.

• Data form: FASTQ raw data (FYI, RNA-seq reads table is attached in a separate page).

• ID: (1-104)

• Aim: Detection of the differences in gene expression for each brain region between our 3 different groups?

Bioinformatics methods

Fastqc (v0.11.9) was used to inspect sequence quality[1].

The mouse transcriptome was downloaded from GENCODE version 28[2].

Skewer (v0.2.2) was used to trim low quality bases (qual<20) from the 3’ end of the read[3].

Kallisto (0.46.1) was used to map RNA-seq reads to the transcriptome [4].

Multiqc was used to tabulate sequence quality, trimming and mapping statistics [5].

Data were read into R v4.1.2 and duplicate lane data were aggregated, and transcript level counts were aggregated to gene level counts.

Genes with an average of less than 10 reads across samples were excluded from downstream analysis.

DESeq (1.32.0) was used with default settings to assay differential expression between control and treatment groups for all tissues [6].

Pathway analysis was performed with reactome gene sets obtained from MSigDB and converted to mouse gene identifiers with the msigdbr package (performed on 16-02-2022) [7,8,9].

Differential pathway analysis was performed with the “mitch” bioconductor package [10].

Genes and pathways with false discovery rate (FDR)<0.05 were considered significant.

suppressPackageStartupMessages({
    library("zoo")
#    library("tidyverse")
    library("reshape2")
    library("DESeq2")
    library("gplots")
    library("fgsea")
    library("MASS")
    library("mitch")
    library("eulerr")
    library("limma")
    library("topconfects")
    library("kableExtra")
    library("vioplot")
    library("pkgload")
    library("beeswarm")
})

Import read counts

Importing RNA-seq data

#tmp <- read.table("3col.tsv.gz",header=F)
#x <- as.matrix(acast(tmp, V2~V1, value.var="V3", fun.aggregate = sum))
#x <- as.data.frame(x)
#accession <- sapply((strsplit(rownames(x),"\\|")),"[[",2)
#symbol<-sapply((strsplit(rownames(x),"\\|")),"[[",6)
#x$geneid <- paste(accession,symbol)
#xx <- aggregate(. ~ geneid,x,sum)
#rownames(xx) <- xx$geneid
#xx$geneid = NULL
#xx <- round(xx)
#xx <- xx[,which(colnames(xx)!="test")]
#xx[1:6,1:6]
#dim(xx)
xx <- read.table("lungbrain_counts.tsv",sep="\t")

Fix the sample names.

They are duplicated for lane 1 and 2, which I will aggregate.

#txx <- as.data.frame(t(xx))
#txx$label <- sapply(strsplit(rownames(txx),"_"),"[[",1)
#txx[1:3,c(1:4,ncol(txx))]
#txx2 <- aggregate(. ~ label,txx,sum)
#txx2[1:3,1:4]
#rownames(txx2) <- txx2[,1]
#txx2[,1] = NULL
#xx <- as.data.frame(t(txx2))
#xx[1:4,1:5]
##write.table(xx,file="lungbrain_counts.tsv",sep="\t",quote=FALSE)
rxx <- apply(xx,2,function(x) { x / sum(x) * 1e6  } )
rxx[1:4,1:5]

##                                EA11.1     EA11.2     EA11.3    EA11.4    EA11.5
## ENSMUSG00000000001.5 Gnai3  32.468943 28.3596016 30.1481583 32.451175 30.395016
## ENSMUSG00000000003.16 Pbsn   0.000000  0.0000000  0.0000000  0.000000  0.000000
## ENSMUSG00000000028.16 Cdc45  1.835885  1.7466205  2.5951711  1.687236  2.638796
## ENSMUSG00000000031.17 H19    1.101531  0.2757822  0.4417313  1.799719  2.492196

head(colSums(rxx))

## EA11.1 EA11.2 EA11.3 EA11.4 EA11.5 EA12.1 
##  1e+06  1e+06  1e+06  1e+06  1e+06  1e+06

Samplesheet.

Need to delete “EA13-4”.

ss <- read.table("lungbrain_samplesheet.tsv",header=TRUE)

rownames(ss) <- paste("EA",as.character(ss$OriginalID),sep="")
ss <- ss[order(rownames(ss)),]

rownames(ss) == colnames(rxx)

##   [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
##  [16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
##  [31] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
##  [46] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
##  [61] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
##  [76] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
##  [91] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE

ss %>% kbl(caption = "Sample sheet") %>%
  kable_paper("hover", full_width = F)

Sample sheet

	Region	Treatment	SampleNum	OriginalID	InternalID	PercentReads	NumReads	Gbases
EA11.1	hyp	OVA	4	11.1	CSEA004	0.5566	40914716	4.091472
EA11.2	amy	OVA	28	11.2	CSEA028	0.6315	46420487	4.642049
EA11.3	hip	OVA	52	11.3	CSEA052	0.5436	39959108	3.995911
EA11.4	pag	OVA	76	11.4	CSEA076	0.5279	38805028	3.880503
EA11.5	NI	OVA	32	11.5	CSEA032	0.6059	44538674	4.453867
EA12.1	hyp	OVA	5	12.1	CSEA005	0.6140	45134092	4.513409
EA12.2	amy	OVA	29	12.2	CSEA029	0.6140	45134092	4.513409
EA12.3	hip	OVA	53	12.3	CSEA053	0.4697	34526845	3.452684
EA12.4	pag	OVA	77	12.4	CSEA077	0.6622	48677191	4.867719
EA12.5	NI	OVA	40	12.5	CSEA040	0.6281	46170558	4.617056
EA13.1	hyp	Control	20	13.1	CSEA020	0.6535	48037669	4.803767
EA13.2	amy	Control	43	13.2	CSEA043	0.6126	45031180	4.503118
EA13.3	hip	Control	68	13.3	CSEA068	0.6158	45266406	4.526641
EA13.4	pag	Control	92	13.4	CSEA092	0.5902	43384594	4.338459
EA13.5	NI	Control	100	13.5	CSEA100	0.5576	40988224	4.098822
EA14.1	hyp	Control	21	14.1	CSEA021	0.6006	44149080	4.414908
EA14.2	amy	Control	44	14.2	CSEA044	0.6354	46707169	4.670717
EA14.4	pag	Control	93	14.4	CSEA093	0.6229	45788315	4.578832
EA14.5	NI	Control	101	14.5	CSEA101	0.4591	33747657	3.374766
EA15.3	hip	Control	69	15.3	CSEA069	0.6063	44568078	4.456808
EA16.2	amy	Control	45	16.2	CSEA045	0.5070	37268704	3.726870
EA19.1	hyp	Control	22	19.1	CSEA022	0.5334	39209323	3.920932
EA19.2	amy	Control	46	19.2	CSEA046	0.5431	39922354	3.992235
EA19.3	hip	Control	70	19.3	CSEA070	0.4972	36548323	3.654832
EA19.4	pag	Control	94	19.4	CSEA094	0.6218	45707456	4.570746
EA19.5	NI	Control	102	19.5	CSEA102	0.5271	38746221	3.874622
EA20.1	hyp	Control	23	20.1	CSEA023	0.5896	43340489	4.334049
EA20.2	amy	Control	47	20.2	CSEA047	0.5473	40231088	4.023109
EA20.3	hip	Control	71	20.3	CSEA071	0.5420	39841494	3.984149
EA20.4	pag	Control	95	20.4	CSEA095	0.6251	45950034	4.595003
EA20.5	NI	Control	103	20.5	CSEA103	0.5385	39584215	3.958422
EA22.5	NI	LPS	56	22.5	CSEA056	0.5462	40150229	4.015023
EA23.5	NI	LPS	64	23.5	CSEA064	0.7148	52543727	5.254373
EA24.1	hyp	LPS	9	24.1	CSEA009	0.6135	45097337	4.509734
EA24.2	amy	LPS	33	24.2	CSEA033	0.6361	46758625	4.675862
EA24.3	hip	LPS	57	24.3	CSEA057	0.5850	43002351	4.300235
EA24.4	pag	LPS	81	24.4	CSEA081	0.5860	43075859	4.307586
EA24.5	NI	LPS	72	24.5	CSEA072	0.5553	40819155	4.081915
EA25.1	hyp	LPS	10	25.1	CSEA010	0.6531	48008266	4.800827
EA25.2	amy	LPS	34	25.2	CSEA034	0.5554	40826506	4.082651
EA25.3	hip	LPS	58	25.3	CSEA058	0.5951	43744785	4.374478
EA25.4	pag	LPS	82	25.4	CSEA082	0.5224	38400732	3.840073
EA25.5	NI	LPS	80	25.5	CSEA080	0.5595	41127890	4.112789
EA26.1	hyp	LPS	11	26.1	CSEA011	0.5547	40775050	4.077505
EA26.2	amy	LPS	35	26.2	CSEA035	0.5051	37129038	3.712904
EA26.3	hip	LPS	59	26.3	CSEA059	0.5320	39106412	3.910641
EA26.4	pag	LPS	83	26.4	CSEA083	0.5903	43391945	4.339195
EA26.5	NI	LPS	88	26.5	CSEA088	0.5439	39981160	3.998116
EA28.1	hyp	LPS	12	28.1	CSEA012	0.5116	37606842	3.760684
EA28.2	amy	LPS	36	28.2	CSEA036	0.6100	44840058	4.484006
EA28.3	hip	LPS	60	28.3	CSEA060	0.5066	37239301	3.723930
EA28.4	pag	LPS	84	28.4	CSEA084	0.6240	45869174	4.586917
EA29.1	hyp	LPS	13	29.1	CSEA013	0.5781	42495144	4.249514
EA29.2	amy	LPS	37	29.2	CSEA037	0.5550	40797102	4.079710
EA29.3	hip	LPS	61	29.3	CSEA061	0.6357	46729221	4.672922
EA29.4	pag	LPS	85	29.4	CSEA085	0.5473	40231088	4.023109
EA29.5	NI	LPS	96	29.5	CSEA096	0.6012	44193185	4.419319
EA3.1	hyp	OVA	1	3.1	CSEA001	0.7106	52234993	5.223499
EA3.2	amy	OVA	25	3.2	CSEA025	0.5306	39003500	3.900350
EA3.3	hip	OVA	49	3.3	CSEA049	0.6483	47655426	4.765543
EA3.4	pag	OVA	73	3.4	CSEA073	0.5320	39106412	3.910641
EA3.5	NI	OVA	8	3.5	CSEA008	0.6426	47236429	4.723643
EA30.1	hyp	LPS	14	30.1	CSEA014	0.5787	42539249	4.253925
EA30.2	amy	LPS	38	30.2	CSEA038	0.5688	41811517	4.181152
EA30.3	hip	LPS	62	30.3	CSEA062	0.6359	46743923	4.674392
EA30.4	pag	LPS	86	30.4	CSEA086	0.6063	44568078	4.456808
EA30.5	NI	LPS	104	30.5	CSEA104	0.5692	41840920	4.184092
EA31.1	hyp	LPS	15	31.1	CSEA015	0.5738	42179058	4.217906
EA31.2	amy	LPS	39	31.2	CSEA039	0.5634	41414572	4.141457
EA31.3	hip	LPS	63	31.3	CSEA063	0.6144	45163495	4.516349
EA31.4	pag	LPS	87	31.4	CSEA087	0.5989	44024116	4.402412
EA34.1	hyp	OVA	6	34.1	CSEA006	0.6620	48662490	4.866249
EA34.2	amy	OVA	30	34.2	CSEA030	0.5227	38422784	3.842278
EA34.3	hip	OVA	54	34.3	CSEA054	0.6550	48147932	4.814793
EA34.4	pag	OVA	78	34.4	CSEA078	0.6248	45927981	4.592798
EA35.1	hyp	OVA	7	35.1	CSEA007	0.5053	37143740	3.714374
EA35.2	amy	OVA	31	35.2	CSEA031	0.5840	42928843	4.292884
EA35.3	hip	OVA	55	35.3	CSEA055	0.5617	41289608	4.128961
EA35.4	pag	OVA	79	35.4	CSEA079	0.6147	45185547	4.518555
EA35.5	NI	OVA	48	35.5	CSEA048	0.6017	44229940	4.422994
EA4.1	hyp	OVA	2	4.1	CSEA002	0.5883	43244928	4.324493
EA4.2	amy	OVA	26	4.2	CSEA026	0.5446	40032616	4.003262
EA4.3	hip	OVA	50	4.3	CSEA050	0.5873	43171420	4.317142
EA4.4	pag	OVA	74	4.4	CSEA074	0.5389	39613619	3.961362
EA4.5	NI	OVA	16	4.5	CSEA016	0.6581	48375807	4.837581
EA5.1	hyp	Control	17	5.1	CSEA017	0.5628	41370467	4.137047
EA5.2	amy	Control	41	5.2	CSEA041	0.5405	39731232	3.973123
EA5.3	hip	Control	65	5.3	CSEA065	0.6693	49199100	4.919910
EA5.4	pag	Control	89	5.4	CSEA089	0.5372	39488655	3.948865
EA5.5	NI	Control	97	5.5	CSEA097	0.6432	47280534	4.728053
EA6.1	hyp	Control	18	6.1	CSEA018	0.6405	47082061	4.708206
EA6.2	amy	Control	42	6.2	CSEA042	0.5692	41840920	4.184092
EA6.3	hip	Control	66	6.3	CSEA066	0.6563	48243492	4.824349
EA6.4	pag	Control	90	6.4	CSEA090	0.5525	40613332	4.061333
EA6.5	NI	Control	98	6.5	CSEA098	0.5549	40789751	4.078975
EA7.1	hyp	Control	19	7.1	CSEA019	0.5891	43303735	4.330374
EA7.3	hip	Control	67	7.3	CSEA067	0.6897	50698669	5.069867
EA7.4	pag	Control	91	7.4	CSEA091	0.5014	36857058	3.685706
EA7.5	NI	Control	99	7.5	CSEA099	0.6789	49904780	4.990478
EA9.1	hyp	OVA	3	9.1	CSEA003	0.5619	41304309	4.130431
EA9.2	amy	OVA	27	9.2	CSEA027	0.4965	36496867	3.649687
EA9.3	hip	OVA	51	9.3	CSEA051	0.4721	34703265	3.470326
EA9.4	pag	OVA	75	9.4	CSEA075	0.6358	46736572	4.673657
EA9.5	NI	OVA	24	9.5	CSEA024	0.5248	38577152	3.857715

QC analysis

Here I’ll look at a few different quality control measures.

The simplest is the number and proportion of reads that are assigned to genes.

par(mar=c(5,8,3,1))
barplot(colSums(xx),horiz=TRUE,las=1,xlab="num reads",col=ss$cols)

sums <- colSums(xx)
sums <- sums[order(sums)]
barplot(sums,horiz=TRUE,las=1,xlab="num reads",cex.names=0.8)
abline(v=15000000,col="red")

which(sums<15000000)

## EA9.5 
##     1

barplot(head(sums,6),horiz=TRUE,las=1,xlab="num reads",cex.names=1)
abline(v=15000000,col="red")

which(sums<15000000)

## EA9.5 
##     1

pdf("plot01_qc01.pdf")
par(mar=c(5,8,3,1))
barplot(colSums(xx),horiz=TRUE,las=1,xlab="num reads",col=ss$cols)
sums <- colSums(xx)
sums <- sums[order(sums)]
barplot(sums,horiz=TRUE,las=1,xlab="num reads",cex.names=0.8)
abline(v=15000000,col="red")
which(sums<15000000)

## EA9.5 
##     1

barplot(head(sums,6),horiz=TRUE,las=1,xlab="num reads",cex.names=1)
abline(v=15000000,col="red")
which(sums<15000000)

## EA9.5 
##     1

dev.off()

## X11cairo 
##        2

Now to look at the rRNA content.

rrna <- read.table("https://raw.githubusercontent.com/markziemann/lung_brain/main/rrna_res.tsv")
myrrna <- rrna$V2/10000
names(myrrna) <- rrna$V1
myrrna <- myrrna[order(myrrna)]

barplot(myrrna,horiz=TRUE,las=1,xlab="percent rRNA reads",cex.names=0.8)
abline(v=10,col="red")

barplot(tail(myrrna),horiz=TRUE,las=1,xlab="percent rRNA reads",cex.names=0.8)
abline(v=10,col="red")

pdf("plot02_rrna01.pdf")
barplot(myrrna,horiz=TRUE,las=1,xlab="percent rRNA reads",cex.names=0.8)
abline(v=10,col="red")

barplot(tail(myrrna),horiz=TRUE,las=1,xlab="percent rRNA reads",cex.names=0.8)
abline(v=10,col="red")
dev.off()

## X11cairo 
##        2

myrrna[which(myrrna>10)]

##  EA29-5  EA23-5   EA3-1   EA9-5 
## 10.1406 15.5685 18.0218 25.9348

MDS plot for all samples

Multidimensional scaling plot to show the variation between all samples, very similar to PCA.

par(mar=c(5.1,4.1,4.1,2.1))

mds <- cmdscale(dist(t(xx)))

cols <- as.numeric(as.factor(ss$Region))+1
pchs <- as.numeric(factor(ss$Treatment))+14

plot(mds, xlab="Coordinate 1", ylab="Coordinate 2",
  type = "p",bty="n",pch=pchs, cex=4 ,col=cols )
text(mds, labels=rownames(mds) ,col="black")

legend("bottomleft", inset=.02, title="tissue",
   legend=unique(as.factor(ss$Region)) , fill=unique(cols),  cex=1.2)

legend("left", inset=.02, title="treatment",
   legend=unique(as.factor(ss$Treatment)) , pch=unique(pchs),  cex=1.2)

pdf("plot03_mds01.pdf")
plot(mds, xlab="Coordinate 1", ylab="Coordinate 2",
  type = "p",bty="n",pch=pchs, cex=4 ,col=cols )
text(mds, labels=rownames(mds) ,col="black")
legend("bottomleft", inset=.02, title="tissue",
   legend=unique(as.factor(ss$Region)) , fill=unique(cols),  cex=1.2)
legend("left", inset=.02, title="treatment",
   legend=unique(as.factor(ss$Treatment)) , pch=unique(pchs),  cex=1.2)
dev.off()

## X11cairo 
##        2

Correlation heatmap

heatmap.2(cor(xx),trace="n",main="Pearson correlation heatmap",margin=c(8,8),cexRow=0.7,cexCol=0.7)

heatmap.2(cor(xx),trace="n",main="Pearson correlation heatmap",margin=c(8,8),cexRow=0.7,cexCol=0.7)

pdf("plot04_cor01.pdf")
heatmap.2(cor(xx),trace="n",main="Pearson correlation heatmap",margin=c(8,8),cexRow=0.7,cexCol=0.7)
heatmap.2(cor(xx),trace="n",main="Pearson correlation heatmap",margin=c(8,8),cexRow=0.7,cexCol=0.7)
dev.off()

## X11cairo 
##        2

Set up the different datasets for differential expression analysis

Here, I’ll give an example on how to separate the data matrix by tissue and then evaluate differential expression.

Don’t forget to remove poorly detected genes from the matrix with a threshold of 10 reads per sample on average.

There are 5 contrasts to set up, one for each tissue.

The separate sample sheets are called s1, s2, etc.

The separate counts tables are called x1, x2, etc.

I will begin with hypothalamus and leave the rest to Craig’s team.

# hyp 1
ss1 <- ss[which(ss$Region=="hyp"),]
xx1 <- xx[which(colnames(xx) %in% rownames(ss1))]
xx1 <- xx1[which(rowMeans(xx1)>=10),]
rpm1 <- apply(xx1,2,function(x) { x / sum(x) * 1e6  } )
ss1_l <- ss[which(ss$Region=="hyp" & ss$Treatment != "OVA"),]
xx1_l <- xx[which(colnames(xx) %in% rownames(ss1_l))]
xx1_l <- xx1_l[which(rowMeans(xx1_l)>=10),]
rpm1_l <- apply(xx1_l,2,function(x) { x / sum(x) * 1e6  } )
ss1_o <- ss[which(ss$Region=="hyp" & ss$Treatment != "LPS"),]
xx1_o <- xx[which(colnames(xx) %in% rownames(ss1_o))]
xx1_o <- xx1_o[which(rowMeans(xx1_o)>=10),]
rpm1_o <- apply(xx1_o,2,function(x) { x / sum(x) * 1e6  } )

# amy 2
ss2 <- ss[which(ss$Region=="amy"),]
xx2 <- xx[which(colnames(xx) %in% rownames(ss2))]
xx2 <- xx2[which(rowMeans(xx2)>=10),]
rpm2 <- apply(xx2,2,function(x) { x / sum(x) * 1e6  } )
ss2_l <- ss[which(ss$Region=="amy" & ss$Treatment != "OVA"),]
xx2_l <- xx[which(colnames(xx) %in% rownames(ss2_l))]
xx2_l <- xx2_l[which(rowMeans(xx2_l)>=10),]
rpm2_l <- apply(xx2_l,2,function(x) { x / sum(x) * 1e6  } )
ss2_o <- ss[which(ss$Region=="amy" & ss$Treatment != "LPS"),]
xx2_o <- xx[which(colnames(xx) %in% rownames(ss2_o))]
xx2_o <- xx2_o[which(rowMeans(xx2_o)>=10),]
rpm2_o <- apply(xx2_o,2,function(x) { x / sum(x) * 1e6  } )

# hip 3
ss3 <- ss[which(ss$Region=="hip"),]
xx3 <- xx[which(colnames(xx) %in% rownames(ss3))]
xx3 <- xx3[which(rowMeans(xx3)>=10),]
rpm3 <- apply(xx3,2,function(x) { x / sum(x) * 1e6  } )
ss3_l <- ss[which(ss$Region=="hip" & ss$Treatment != "OVA"),]
xx3_l <- xx[which(colnames(xx) %in% rownames(ss3_l))]
xx3_l <- xx3_l[which(rowMeans(xx3_l)>=10),]
rpm3_l <- apply(xx3_l,2,function(x) { x / sum(x) * 1e6  } )
ss3_o <- ss[which(ss$Region=="hip" & ss$Treatment != "LPS"),]
xx3_o <- xx[which(colnames(xx) %in% rownames(ss3_o))]
xx3_o <- xx3_o[which(rowMeans(xx3_o)>=10),]
rpm3_o <- apply(xx3_o,2,function(x) { x / sum(x) * 1e6  } )

# pag 4
ss4 <- ss[which(ss$Region=="pag"),]
xx4 <- xx[which(colnames(xx) %in% rownames(ss4))]
xx4 <- xx4[which(rowMeans(xx4)>=10),]
rpm4 <- apply(xx4,2,function(x) { x / sum(x) * 1e6  } )
ss4_l <- ss[which(ss$Region=="pag" & ss$Treatment != "OVA"),]
xx4_l <- xx[which(colnames(xx) %in% rownames(ss4_l))]
xx4_l <- xx4_l[which(rowMeans(xx4_l)>=10),]
rpm4_l <- apply(xx4_l,2,function(x) { x / sum(x) * 1e6  } )
ss4_o <- ss[which(ss$Region=="pag" & ss$Treatment != "LPS"),]
xx4_o <- xx[which(colnames(xx) %in% rownames(ss4_o))]
xx4_o <- xx4_o[which(rowMeans(xx4_o)>=10),]
rpm4_o <- apply(xx4_o,2,function(x) { x / sum(x) * 1e6  } )

# ni 5
ss5 <- ss[which(ss$Region=="NI"),]
xx5 <- xx[which(colnames(xx) %in% rownames(ss5))]
xx5 <- xx5[which(rowMeans(xx5)>=10),]
rpm5 <- apply(xx5,2,function(x) { x / sum(x) * 1e6  } )
ss5_l <- ss[which(ss$Region=="NI" & ss$Treatment != "OVA"),]
xx5_l <- xx[which(colnames(xx) %in% rownames(ss5_l))]
xx5_l <- xx5_l[which(rowMeans(xx5_l)>=10),]
rpm5_l <- apply(xx5,2,function(x) { x / sum(x) * 1e6  } )
ss5_o <- ss[which(ss$Region=="NI" & ss$Treatment != "LPS"),]
xx5_o <- xx[which(colnames(xx) %in% rownames(ss5_o))]
xx5_o <- xx5_o[which(rowMeans(xx5_o)>=10),]
rpm5 <- apply(xx5,2,function(x) { x / sum(x) * 1e6  } )

message("dimensions of objects: n genes, n samples")

## dimensions of objects: n genes, n samples

l <- list("xx"=xx,"xx1"=xx1,"xx1 l"= xx1_l,"xx1 o"=xx1_o,
  "xx2"=xx2,"xx2 l"=xx2_l,"xx2 o"=xx2_o,
  "xx3"=xx3,"xx3 l"=xx3_l,"xx3 o"=xx3_o,
  "xx4"=xx4,"xx4 l"=xx4_l,"xx4 o"=xx5_o,
  "xx5"=xx5,"xx5 l"=xx5_l,"xx5 o"=xx5_o)

lapply(l,dim)

## $xx
## [1] 55357   104
## 
## $xx1
## [1] 17091    21
## 
## $`xx1 l`
## [1] 17095    14
## 
## $`xx1 o`
## [1] 17114    14
## 
## $xx2
## [1] 16681    21
## 
## $`xx2 l`
## [1] 16818    14
## 
## $`xx2 o`
## [1] 16624    14
## 
## $xx3
## [1] 16897    21
## 
## $`xx3 l`
## [1] 16927    14
## 
## $`xx3 o`
## [1] 16953    14
## 
## $xx4
## [1] 17570    21
## 
## $`xx4 l`
## [1] 17612    14
## 
## $`xx4 o`
## [1] 17391    13
## 
## $xx5
## [1] 17474    20
## 
## $`xx5 l`
## [1] 17490    14
## 
## $`xx5 o`
## [1] 17391    13

par(mar=c(5.1,4.1,4.1,2.1))

# hyp 1
mds <- cmdscale(dist(t(xx1)))
cols <- as.numeric(factor(ss1$Treatment))+1

plot(mds, xlab="Coordinate 1", ylab="Coordinate 2",
  type = "p",bty="n",pch=19, cex=4 ,col=cols , main = "hyp")
text(mds, labels=rownames(mds) ,col="black")

legend("bottomleft", inset=.02, title="treatment",
   legend=unique(as.factor(ss$Treatment)) , pch=19,col=unique(cols),  cex=1.2)

pdf("plot05_mds02.pdf")
plot(mds, xlab="Coordinate 1", ylab="Coordinate 2",
  type = "p",bty="n",pch=19, cex=4 ,col=cols , main = "hyp")
text(mds, labels=rownames(mds) ,col="black")
legend("bottomleft", inset=.02, title="treatment",
   legend=unique(as.factor(ss$Treatment)) , pch=19,col=unique(cols),  cex=1.2)
dev.off()

## X11cairo 
##        2

# Sample 3.1à has high rRNA content
# Sample 9.1à no problem in its QC but will look for other explanation
rpm1m <- rowMeans(rpm1)
de1_inv <- data.frame(rpm1[,"EA9.1"] , rpm1m )
colnames(de1_inv) <- c("ea9.1","mean")
de1_inv$ratio <- de1_inv$ea9.1 / de1_inv$mean
de1_inv <- de1_inv[order(-de1_inv$ratio),]
head(de1_inv,20)

##                                          ea9.1         mean    ratio
## ENSMUSG00000114436.2 Gm48283          6.910956    0.8194787 8.433356
## ENSMUSG00000098975.8 Gm27177          7.913309    0.9571406 8.267655
## ENSMUSG00000099702.2 Gm29013         91.055804   11.9345178 7.629617
## ENSMUSG00000117748.2 Derpc           14.085689    2.0219406 6.966421
## ENSMUSG00000092006.2 Gm17139          4.220431    0.6200184 6.806945
## ENSMUSG00000070343.4 Gm10288         28.171378    5.5647018 5.062514
## ENSMUSG00000098650.2 Commd1b          6.066870    1.2624669 4.805567
## ENSMUSG00000090207.3 4930524O07Rik    5.117273    1.2855768 3.980527
## ENSMUSG00000085543.2 Gm13568          9.126683    2.3771039 3.839413
## ENSMUSG00000109168.2 Gm44709          4.220431    1.1013228 3.832147
## ENSMUSG00000042045.7 Sln              1.846439    0.5259509 3.510667
## ENSMUSG00000028459.12 Cd72            2.690525    0.7868600 3.419318
## ENSMUSG00000067038.7 Rps12-ps3       36.559485   11.0831020 3.298669
## ENSMUSG00000102275.2 Gm37144         32.075277    9.8096302 3.269774
## ENSMUSG00000104208.2 Gm37401          3.165323    1.0720356 2.952629
## ENSMUSG00000118181.2 Gm53015          8.124330    2.8270970 2.873736
## ENSMUSG00000107659.2 Gm44170          2.373993    0.8401805 2.825574
## ENSMUSG00000105373.2 Gm42429          8.493618    3.0166338 2.815595
## ENSMUSG00000001014.7 Icam4            1.477151    0.5279512 2.797893
## ENSMUSG00000064337.1 mt-Rnr1       7662.034620 2745.5643499 2.790696

Remove 3.1 as it has high rRNA content. Sample 9.1 appears as possible outlier, but no obvious reason to exclude it from the analysis. EG no problem with QC and no gene expression profile features that indicate tissue contamination.

Then repeat the MDS.

ss1 <- ss1[rownames(ss1)!="EA3.1",]
xx1 <- xx1[,colnames(xx1) %in% rownames(ss1)]

# hyp 1
mds <- cmdscale(dist(t(xx1)),k=4)
cols <- as.numeric(factor(ss1$Treatment))+1

# 1 vs 2
plot(mds, xlab="Coordinate 1", ylab="Coordinate 2",
  type = "p",bty="n",pch=19, cex=4 ,col=cols , main = "hyp")
text(mds, labels=rownames(mds) ,col="black")

legend("bottomleft", inset=.02, title="treatment",
  legend=unique(as.factor(ss$Treatment)) , pch=19,col=unique(cols),  cex=1.2)

# 1 vs 3
plot(mds[,c(1,3)], xlab="Coordinate 1", ylab="Coordinate 3",
  type = "p",bty="n",pch=19, cex=4 ,col=cols , main = "hyp")
text(mds[,c(1,3)], labels=rownames(mds[,c(1,3)]) ,col="black")
legend("bottomleft", inset=.02, title="treatment",
   legend=unique(as.factor(ss$Treatment)) , pch=19,col=unique(cols),  cex=1.2)

# 2 vs 3
plot(mds[,c(2,3)], xlab="Coordinate 2", ylab="Coordinate 3",
  type = "p",bty="n",pch=19, cex=4 ,col=cols , main = "hyp")
text(mds[,c(2,3)], labels=rownames(mds[,c(2,3)]) ,col="black")
legend("bottomleft", inset=.02, title="treatment",
  legend=unique(as.factor(ss$Treatment)) , pch=19,col=unique(cols),  cex=1.2)

# 1 vs 4
plot(mds[,c(1,4)], xlab="Coordinate 1", ylab="Coordinate 4",
  type = "p",bty="n",pch=19, cex=4 ,col=cols , main = "hyp")
text(mds[,c(1,4)], labels=rownames(mds[,c(1,4)]) ,col="black")
legend("bottomleft", inset=.02, title="treatment",
  legend=unique(as.factor(ss$Treatment)) , pch=19,col=unique(cols),  cex=1.2)

# 2 vs 4
plot(mds[,c(2,4)], xlab="Coordinate 2", ylab="Coordinate 4",
  type = "p",bty="n",pch=19, cex=4 ,col=cols , main = "hyp")
text(mds[,c(2,4)], labels=rownames(mds[,c(2,4)]) ,col="black")
legend("bottomleft", inset=.02, title="treatment",
  legend=unique(as.factor(ss$Treatment)) , pch=19,col=unique(cols),  cex=1.2)

# 3 vs 4
plot(mds[,c(3,4)], xlab="Coordinate 3", ylab="Coordinate 4",
  type = "p",bty="n",pch=19, cex=4 ,col=cols , main = "hyp")
text(mds[,c(3,4)], labels=rownames(mds[,c(3,4)]) ,col="black")
legend("topright", inset=.02, title="treatment",
  legend=unique(as.factor(ss$Treatment)) , pch=19,col=unique(cols),  cex=1.2)

pdf("plot06_mds03.pdf")
plot(mds, xlab="Coordinate 1", ylab="Coordinate 2",
  type = "p",bty="n",pch=19, cex=4 ,col=cols , main = "hyp")
text(mds, labels=rownames(mds) ,col="black")
legend("bottomleft", inset=.02, title="treatment",
   legend=unique(as.factor(ss$Treatment)) , pch=19,col=unique(cols),  cex=1.2)

# 1 vs 3
plot(mds[,c(1,3)], xlab="Coordinate 1", ylab="Coordinate 3",
  type = "p",bty="n",pch=19, cex=4 ,col=cols , main = "hyp")
text(mds[,c(1,3)], labels=rownames(mds[,c(1,3)]) ,col="black")
legend("bottomleft", inset=.02, title="treatment",
   legend=unique(as.factor(ss$Treatment)) , pch=19,col=unique(cols),  cex=1.2)

# 2 vs 3
plot(mds[,c(2,3)], xlab="Coordinate 2", ylab="Coordinate 3",
  type = "p",bty="n",pch=19, cex=4 ,col=cols , main = "hyp")
text(mds[,c(2,3)], labels=rownames(mds[,c(2,3)]) ,col="black")
legend("bottomleft", inset=.02, title="treatment",
  legend=unique(as.factor(ss$Treatment)) , pch=19,col=unique(cols),  cex=1.2)

# 1 vs 4
plot(mds[,c(1,4)], xlab="Coordinate 1", ylab="Coordinate 4",
  type = "p",bty="n",pch=19, cex=4 ,col=cols , main = "hyp")
text(mds[,c(1,4)], labels=rownames(mds[,c(1,4)]) ,col="black")
legend("bottomleft", inset=.02, title="treatment",
  legend=unique(as.factor(ss$Treatment)) , pch=19,col=unique(cols),  cex=1.2)

# 2 vs 4
plot(mds[,c(2,4)], xlab="Coordinate 2", ylab="Coordinate 4",
  type = "p",bty="n",pch=19, cex=4 ,col=cols , main = "hyp")
text(mds[,c(2,4)], labels=rownames(mds[,c(2,4)]) ,col="black")
legend("bottomleft", inset=.02, title="treatment",
  legend=unique(as.factor(ss$Treatment)) , pch=19,col=unique(cols),  cex=1.2)

# 3 vs 4
plot(mds[,c(3,4)], xlab="Coordinate 3", ylab="Coordinate 4",
  type = "p",bty="n",pch=19, cex=4 ,col=cols , main = "hyp")
text(mds[,c(3,4)], labels=rownames(mds[,c(3,4)]) ,col="black")
legend("topright", inset=.02, title="treatment",
  legend=unique(as.factor(ss$Treatment)) , pch=19,col=unique(cols),  cex=1.2)
dev.off()

## X11cairo 
##        2

# amy 2
mds <- cmdscale(dist(t(xx2)))
cols <- as.numeric(factor(ss2$Treatment))+1

plot(mds, xlab="Coordinate 1", ylab="Coordinate 2",
  type = "p",bty="n",pch=19, cex=4 ,col=cols , main = "amy")
text(mds, labels=rownames(mds) ,col="black")

legend("bottomleft", inset=.02, title="treatment",
   legend=unique(as.factor(ss$Treatment)) , pch=19,col=unique(cols),  cex=1.2)

pdf("plot07_mds04.pdf")
plot(mds, xlab="Coordinate 1", ylab="Coordinate 2",
  type = "p",bty="n",pch=19, cex=4 ,col=cols , main = "amy")
text(mds, labels=rownames(mds) ,col="black")
legend("bottomleft", inset=.02, title="treatment",
   legend=unique(as.factor(ss$Treatment)) , pch=19,col=unique(cols),  cex=1.2)
dev.off()

## X11cairo 
##        2

# hip 3
mds <- cmdscale(dist(t(xx3)))
cols <- as.numeric(factor(ss3$Treatment))+1

plot(mds, xlab="Coordinate 1", ylab="Coordinate 2",
  type = "p",bty="n",pch=19, cex=4 ,col=cols , main = "hip")
text(mds, labels=rownames(mds) ,col="black")

legend("bottomleft", inset=.02, title="treatment",
   legend=unique(as.factor(ss$Treatment)) , pch=19,col=unique(cols),  cex=1.2)

pdf("plot08_mds05.pdf")
plot(mds, xlab="Coordinate 1", ylab="Coordinate 2",
  type = "p",bty="n",pch=19, cex=4 ,col=cols , main = "hip")
text(mds, labels=rownames(mds) ,col="black")
legend("bottomleft", inset=.02, title="treatment",
   legend=unique(as.factor(ss$Treatment)) , pch=19,col=unique(cols),  cex=1.2)
dev.off()

## X11cairo 
##        2

# pag 4
mds <- cmdscale(dist(t(xx4)))
cols <- as.numeric(factor(ss4$Treatment))+1

plot(mds, xlab="Coordinate 1", ylab="Coordinate 2",
  type = "p",bty="n",pch=19, cex=4 ,col=cols , main = "pag")
text(mds, labels=rownames(mds) ,col="black")

legend("bottomleft", inset=.02, title="treatment",
   legend=unique(as.factor(ss$Treatment)) , pch=19,col=unique(cols),  cex=1.2)

pdf("plot09_mds06.pdf")
plot(mds, xlab="Coordinate 1", ylab="Coordinate 2",
  type = "p",bty="n",pch=19, cex=4 ,col=cols , main = "pag")
text(mds, labels=rownames(mds) ,col="black")
legend("bottomleft", inset=.02, title="treatment",
   legend=unique(as.factor(ss$Treatment)) , pch=19,col=unique(cols),  cex=1.2)
dev.off()

## X11cairo 
##        2

# ni 5
mds <- cmdscale(dist(t(xx5)))
cols <- as.numeric(factor(ss5$Treatment))+1

plot(mds, xlab="Coordinate 1", ylab="Coordinate 2",
  type = "p",bty="n",pch=19, cex=4 ,col=cols , main = "ni")
text(mds, labels=rownames(mds) ,col="black")

legend("bottomleft", inset=.02, title="treatment",
   legend=unique(as.factor(ss$Treatment)) , pch=19,col=unique(cols),  cex=1.2)

pdf("plot10_mds07.pdf")
plot(mds, xlab="Coordinate 1", ylab="Coordinate 2",
  type = "p",bty="n",pch=19, cex=4 ,col=cols , main = "ni")
text(mds, labels=rownames(mds) ,col="black")
legend("bottomleft", inset=.02, title="treatment",
   legend=unique(as.factor(ss$Treatment)) , pch=19,col=unique(cols),  cex=1.2)
dev.off()

## X11cairo 
##        2

Differential expression with DESeq2

For each brain region, there are two treatments, and so two contrasts are performed.

Before we do that, let’s look at the MDS plot of the hypothalamus data.

par(mar=c(5.1,4.1,4.1,2.1))

mds <- cmdscale(dist(t(xx1)))

cols <- as.numeric(factor(ss1$Treatment))+1

plot(mds, xlab="Coordinate 1", ylab="Coordinate 2",
  type = "p",bty="n", pch=19, cex=4 ,col=cols )
text(mds, labels=rownames(mds) ,col="black")

legend("bottomleft", inset=.02, title="treatment",
   legend=unique(as.factor(ss1$Treatment)) , col=unique(cols), pch=19,  cex=1.2)

pdf("plot11_mds08.pdf")
plot(mds, xlab="Coordinate 1", ylab="Coordinate 2",
  type = "p",bty="n", pch=19, cex=4 ,col=cols )
text(mds, labels=rownames(mds) ,col="black")
legend("bottomleft", inset=.02, title="treatment",
   legend=unique(as.factor(ss1$Treatment)) , col=unique(cols), pch=19,  cex=1.2)
dev.off()

## X11cairo 
##        2

Now run DESeq2 for all contrasts.

# 1 hyp
dds <- DESeqDataSetFromMatrix(countData = xx1_l , colData = ss1_l , design = ~ Treatment )

## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors

res <- DESeq(dds)

## estimating size factors

## estimating dispersions

## gene-wise dispersion estimates

## mean-dispersion relationship

## final dispersion estimates

## fitting model and testing

## -- replacing outliers and refitting for 7 genes
## -- DESeq argument 'minReplicatesForReplace' = 7 
## -- original counts are preserved in counts(dds)

## estimating dispersions

## fitting model and testing

z <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
dge1_l <- dge
d1up_l <- rownames(subset(dge1_l,padj <= 0.05 & log2FoldChange > 0))
d1dn_l <- rownames(subset(dge1_l,padj <= 0.05 & log2FoldChange < 0))
write.table(dge1_l,file="dge1_l.tsv",quote=FALSE,sep="\t")

dds <- DESeqDataSetFromMatrix(countData = xx1_o , colData = ss1_o , design = ~ Treatment )

## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors

res <- DESeq(dds)

## estimating size factors

## estimating dispersions

## gene-wise dispersion estimates

## mean-dispersion relationship

## final dispersion estimates

## fitting model and testing

## -- replacing outliers and refitting for 10 genes
## -- DESeq argument 'minReplicatesForReplace' = 7 
## -- original counts are preserved in counts(dds)

## estimating dispersions

## fitting model and testing

z <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
dge1_o <- dge
d1up_o <- rownames(subset(dge1_o,padj <= 0.05 & log2FoldChange > 0))
d1dn_o <- rownames(subset(dge1_o,padj <= 0.05 & log2FoldChange < 0))
write.table(dge1_o,file="dge1_o.tsv",quote=FALSE,sep="\t")

# 2 amy
dds <- DESeqDataSetFromMatrix(countData = xx2_l , colData = ss2_l , design = ~ Treatment )

## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors

res <- DESeq(dds)

## estimating size factors

## estimating dispersions

## gene-wise dispersion estimates

## mean-dispersion relationship

## final dispersion estimates

## fitting model and testing

## -- replacing outliers and refitting for 5 genes
## -- DESeq argument 'minReplicatesForReplace' = 7 
## -- original counts are preserved in counts(dds)

## estimating dispersions

## fitting model and testing

z <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
dge2_l <- dge
d2up_l <- rownames(subset(dge2_l,padj <= 0.05 & log2FoldChange > 0))
d2dn_l <- rownames(subset(dge2_l,padj <= 0.05 & log2FoldChange < 0))
write.table(dge2_l,file="dge2_l.tsv",quote=FALSE,sep="\t")

dds <- DESeqDataSetFromMatrix(countData = xx2_o , colData = ss2_o , design = ~ Treatment )

## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors

res <- DESeq(dds)

## estimating size factors

## estimating dispersions

## gene-wise dispersion estimates

## mean-dispersion relationship

## final dispersion estimates

## fitting model and testing

## -- replacing outliers and refitting for 19 genes
## -- DESeq argument 'minReplicatesForReplace' = 7 
## -- original counts are preserved in counts(dds)

## estimating dispersions

## fitting model and testing

z <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
dge2_o <- dge
d2up_o <- rownames(subset(dge2_o,padj <= 0.05 & log2FoldChange > 0))
d2dn_o <- rownames(subset(dge2_o,padj <= 0.05 & log2FoldChange < 0))
write.table(dge2_o,file="dge2_o.tsv",quote=FALSE,sep="\t")

# 3 hip
dds <- DESeqDataSetFromMatrix(countData = xx3_l , colData = ss3_l , design = ~ Treatment )

## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors

res <- DESeq(dds)

## estimating size factors

## estimating dispersions

## gene-wise dispersion estimates

## mean-dispersion relationship

## final dispersion estimates

## fitting model and testing

## -- replacing outliers and refitting for 2 genes
## -- DESeq argument 'minReplicatesForReplace' = 7 
## -- original counts are preserved in counts(dds)

## estimating dispersions

## fitting model and testing

z <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
dge3_l <- dge
d3up_l <- rownames(subset(dge3_l,padj <= 0.05 & log2FoldChange > 0))
d3dn_l <- rownames(subset(dge3_l,padj <= 0.05 & log2FoldChange < 0))
write.table(dge3_l,file="dge3_l.tsv",quote=FALSE,sep="\t")

dds <- DESeqDataSetFromMatrix(countData = xx3_o , colData = ss3_o , design = ~ Treatment )

## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors

res <- DESeq(dds)

## estimating size factors

## estimating dispersions

## gene-wise dispersion estimates

## mean-dispersion relationship

## final dispersion estimates

## fitting model and testing

## -- replacing outliers and refitting for 6 genes
## -- DESeq argument 'minReplicatesForReplace' = 7 
## -- original counts are preserved in counts(dds)

## estimating dispersions

## fitting model and testing

z <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
dge3_o <- dge
d3up_o <- rownames(subset(dge3_o,padj <= 0.05 & log2FoldChange > 0))
d3dn_o <- rownames(subset(dge3_o,padj <= 0.05 & log2FoldChange < 0))
write.table(dge3_o,file="dge3_o.tsv",quote=FALSE,sep="\t")

# 4 pag
dds <- DESeqDataSetFromMatrix(countData = xx4_l , colData = ss4_l , design = ~ Treatment )

## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors

res <- DESeq(dds)

## estimating size factors

## estimating dispersions

## gene-wise dispersion estimates

## mean-dispersion relationship

## final dispersion estimates

## fitting model and testing

## -- replacing outliers and refitting for 6 genes
## -- DESeq argument 'minReplicatesForReplace' = 7 
## -- original counts are preserved in counts(dds)

## estimating dispersions

## fitting model and testing

z <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
dge4_l <- dge
d4up_l <- rownames(subset(dge4_l,padj <= 0.05 & log2FoldChange > 0))
d4dn_l <- rownames(subset(dge4_l,padj <= 0.05 & log2FoldChange < 0))
write.table(dge4_l,file="dge4_l.tsv",quote=FALSE,sep="\t")

dds <- DESeqDataSetFromMatrix(countData = xx4_o , colData = ss4_o , design = ~ Treatment )

## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors

res <- DESeq(dds)

## estimating size factors

## estimating dispersions

## gene-wise dispersion estimates

## mean-dispersion relationship

## final dispersion estimates

## fitting model and testing

## -- replacing outliers and refitting for 4 genes
## -- DESeq argument 'minReplicatesForReplace' = 7 
## -- original counts are preserved in counts(dds)

## estimating dispersions

## fitting model and testing

z <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
dge4_o <- dge
d4up_o <- rownames(subset(dge4_o,padj <= 0.05 & log2FoldChange > 0))
d4dn_o <- rownames(subset(dge4_o,padj <= 0.05 & log2FoldChange < 0))
write.table(dge4_o,file="dge4_o.tsv",quote=FALSE,sep="\t")

# 5 NI
dds <- DESeqDataSetFromMatrix(countData = xx5_l , colData = ss5_l , design = ~ Treatment )

## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors

res <- DESeq(dds)

## estimating size factors

## estimating dispersions

## gene-wise dispersion estimates

## mean-dispersion relationship

## final dispersion estimates

## fitting model and testing

## -- replacing outliers and refitting for 15 genes
## -- DESeq argument 'minReplicatesForReplace' = 7 
## -- original counts are preserved in counts(dds)

## estimating dispersions

## fitting model and testing

z <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
dge5_l <- dge
d5up_l <- rownames(subset(dge5_l,padj <= 0.05 & log2FoldChange > 0))
d5dn_l <- rownames(subset(dge5_l,padj <= 0.05 & log2FoldChange < 0))
write.table(dge5_l,file="dge5_l.tsv",quote=FALSE,sep="\t")

dds <- DESeqDataSetFromMatrix(countData = xx5_o , colData = ss5_o , design = ~ Treatment )

## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors

res <- DESeq(dds)

## estimating size factors

## estimating dispersions

## gene-wise dispersion estimates

## mean-dispersion relationship

## final dispersion estimates

## fitting model and testing

## -- replacing outliers and refitting for 41 genes
## -- DESeq argument 'minReplicatesForReplace' = 7 
## -- original counts are preserved in counts(dds)

## estimating dispersions

## fitting model and testing

z <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
dge5_o <- dge
d5up_o <- rownames(subset(dge5_o,padj <= 0.05 & log2FoldChange > 0))
d5dn_o <- rownames(subset(dge5_o,padj <= 0.05 & log2FoldChange < 0))
write.table(dge5_o,file="dge5_o.tsv",quote=FALSE,sep="\t")

Here let’s look at some plots.

MA plot shows the average level and fold change of all detected genes. Volcano plot shows the fold change and the significance, as measured by -log(p-value). Significant genes are shown as red points.

There are heatmaps of the top ranked genes by p-value. Above the gene expression values there is a bar in orange/gray colours. Control is shown in orange and treatment in grey.

maplot <- function(de,contrast_name) {
  de <- de[which(!is.na(de$padj)),]
  sig <-subset(de, padj < 0.05 )
  up <-rownames(subset(de, padj < 0.05 & log2FoldChange > 0))
  dn <-rownames(subset(de, padj < 0.05 & log2FoldChange < 0))
  GENESUP <- length(up)
  GENESDN <- length(dn)
  DET=nrow(de)
  SUBHEADER = paste(GENESUP, "up, ", GENESDN, "down", DET, "detected")
  ns <-subset(de, padj > 0.05 )
  plot(log2(de$baseMean),de$log2FoldChange,
       xlab="log2 basemean", ylab="log2 foldchange",
       pch=19, cex=0.5, col="dark gray",
       main=contrast_name, cex.main=1)
  points(log2(sig$baseMean),sig$log2FoldChange,
         pch=19, cex=0.5, col="red")
  mtext(SUBHEADER,cex = 1)
}

make_volcano_old <- function(de,name) {
    de <- de[which(!is.na(de$padj)),]
    de$pvalue[which(de$pvalue==0)] <- 1e-320
    sig <- subset(de,padj<0.05)
    N_SIG=nrow(sig)
    N_UP=nrow(subset(sig,log2FoldChange>0))
    N_DN=nrow(subset(sig,log2FoldChange<0))
    DET=nrow(de)
    HEADER=paste( DET, "detected,", N_SIG,"@5%FDR:", N_DN, "dn", N_UP, "up")
    plot(de$log2FoldChange,-log10(de$pval),cex=0.5,pch=19,col="darkgray",
        main=name, xlab="log2 FC", ylab="-log10 pval")
    mtext(HEADER)
    grid()
    points(sig$log2FoldChange,-log10(sig$pval),cex=0.5,pch=19,col="red")
}

make_volcano <- function(de,name) {
    de <- de[which(!is.na(de$padj)),]
    de$pvalue[which(de$pvalue==0)] <- 1e-320
    sig <- subset(de,padj<0.05)
    # get 5% FDR thresh
    myp = tail(sig,1)$pvalue
    myfdr = tail(sig,1)$padj
    fdrthresh = -log10(myp / myfdr * 0.05)
    N_SIG=nrow(sig)
    N_UP=nrow(subset(sig,log2FoldChange>0))
    N_DN=nrow(subset(sig,log2FoldChange<0))
    DET=nrow(de)
    HEADER=paste( DET, "detected,", N_SIG,"@5%FDR:", N_DN, "dn", N_UP, "up")
    plot(de$log2FoldChange,-log10(de$pval),cex=0.5,pch=19,col="darkgray",
        main=name, xlab="log2 FC", ylab="-log10 pval")
    mtext(HEADER)
    abline(h=fdrthresh,col="red",lty=2)
    abline(v=0,col="black",lty=2)
    grid()
    points(sig$log2FoldChange,-log10(sig$pval),cex=0.5,pch=19,col="red")
}

make_heatmap <- function(de,name,myss,mx,n=30){
  colfunc <- colorRampPalette(c("blue", "white", "red"))
  csc <- myss$Treatment
  csc <- gsub("Control","orange",csc)
  csc <- gsub("LPS","gray",csc)
  csc <- gsub("OVA","yellow",csc)
  mxn <- mx/rowSums(mx)*1000000
  x <- mxn[which(rownames(mxn) %in% rownames(head(de,n))),]
  heatmap.2(as.matrix(x),trace="none",col=colfunc(25),scale="row", margins = c(10,15), cexRow=0.7,
    main=paste("Top ranked",n,"genes in",name) , ColSideColors = csc  )
  mtext("ctrl=orange, LPS=gray, OVA=yellow")
}

make_heatmap2 <- function(de,name,myss,mx,n=30){
  colfunc <- colorRampPalette(c("blue", "white", "red"))
  csc <- myss$Treatment
  csc <- gsub("Control","orange",csc)
  csc <- gsub("LPS","gray",csc)
  csc <- gsub("OVA","yellow",csc)
  mxn <- mx/rowSums(mx)*1000000
  x <- mxn[which(rownames(mxn) %in% rownames(head(de,n))),]
  rownames(x) <- sapply(strsplit(rownames(x)," "),"[[",2)
  heatmap.2(as.matrix(x),trace="none",col=colfunc(25),scale="row", margins = c(10,15), cexRow=0.7,
    main=paste("Top ranked",n,"genes in",name) ,  ColSideColors = csc   )
  mtext("ctrl=orange, LPS=gray, OVA=yellow")
}

mymds <- function(de,name,myss,mx) {
  mds <- cmdscale(dist(t(mx)))
  csc <-  myss$Treatment
  csc <- gsub("Control","orange",csc)
  csc <- gsub("LPS","gray",csc)
  csc <- gsub("OVA","yellow",csc)
  plot(mds, xlab="Coordinate 1", ylab="Coordinate 2", main = name ,
    type = "p",bty="n",pch=19, cex=4 ,col=csc )
  text(mds, labels=rownames(mds) ,col="black")
  legend("topright", inset=.02, title="treatment",
    legend=unique(as.factor(myss$Treatment)) , pch=19, col=unique(csc),  cex=1.4)
}

Results for hypothalamus

We show an MDS plot, MA plot, volcano plot, heatmap and table of top results for each contrast.

mymds(de=dge1_l,name="Cont1: Effect of LPS treatment in hypothalamus",myss=ss1_l,mx=xx1_l)

maplot(dge1_l,"Cont1: Effect of LPS treatment in hypothalamus")

make_volcano(dge1_l,"Cont1: Effect of LPS treatment in hypothalamus")

make_heatmap(de=dge1_l,name="Cont1: Effect of LPS treatment in hypothalamus",myss=ss1_l,mx=xx1_l,n=50)

make_heatmap2(de=dge1_l,name="Cont1: Effect of LPS treatment in hypothalamus",myss=ss1_l,mx=xx1_l,n=50)

dge1_l[1:20,1:6] %>% kbl(caption = "Top gene expression differences for contrast 1: Effect of LPS treatment in hypothalamus") %>%
  kable_paper("hover", full_width = F)

Top gene expression differences for contrast 1: Effect of LPS treatment in hypothalamus

	baseMean	log2FoldChange	lfcSE	stat	pvalue	padj
ENSMUSG00000054252.19 Fgfr3	2892.62590	0.3328185	0.0609055	5.464504	0.00e+00	0.0006909
ENSMUSG00000022715.4 Tmem114	100.67950	0.8836136	0.1650244	5.354442	1.00e-07	0.0006909
ENSMUSG00000025498.16 Irf7	77.47802	-0.8571473	0.1692562	-5.064199	4.00e-07	0.0017092
ENSMUSG00000027875.13 Hmgcs2	180.91714	-0.6653837	0.1315622	-5.057562	4.00e-07	0.0017092
ENSMUSG00000028820.14 Sfpq	2569.85119	0.2618911	0.0527559	4.964201	7.00e-07	0.0022213
ENSMUSG00000041926.16 Rnpep	654.35913	0.2935820	0.0598784	4.902971	9.00e-07	0.0025330
ENSMUSG00000079363.8 Gbp4	72.55881	-1.1002191	0.2263856	-4.859934	1.20e-06	0.0027008
ENSMUSG00000018822.8 Sfrp5	659.14853	0.4883485	0.1033004	4.727461	2.30e-06	0.0041077
ENSMUSG00000026222.17 Sp100	55.19664	-0.7479415	0.1582800	-4.725433	2.30e-06	0.0041077
ENSMUSG00000028655.12 Mfsd2a	443.11911	0.5572282	0.1205845	4.621059	3.80e-06	0.0061467
ENSMUSG00000040253.16 Gbp7	103.19267	-0.6006230	0.1353212	-4.438500	9.10e-06	0.0132588
ENSMUSG00000004891.17 Nes	206.29657	0.4958473	0.1122069	4.419045	9.90e-06	0.0133010
ENSMUSG00000035578.17 Iqcg	381.27559	0.4608992	0.1047871	4.398437	1.09e-05	0.0135034
ENSMUSG00000108852.2 Gm44911	34.74705	0.9903775	0.2263409	4.375601	1.21e-05	0.0139263
ENSMUSG00000024907.8 Gal	1601.34229	0.3959929	0.0914229	4.331441	1.48e-05	0.0153554
ENSMUSG00000020717.20 Pecam1	340.34994	-0.3114213	0.0720066	-4.324902	1.53e-05	0.0153554
ENSMUSG00000051159.17 Cited1	338.53219	0.6483567	0.1506781	4.302925	1.69e-05	0.0159634
ENSMUSG00000051427.15 Ccdc157	914.73405	0.2973665	0.0699150	4.253256	2.11e-05	0.0188445
ENSMUSG00000028519.17 Dab1	925.88165	-0.2341632	0.0557089	-4.203335	2.63e-05	0.0222867
ENSMUSG00000064247.15 Plcxd1	838.55340	0.2157020	0.0515714	4.182586	2.88e-05	0.0232011

mymds(de=dge1_o,name="Cont1: Effect of OVA treatment in hypothalamus",myss=ss1_o,mx=xx1_o)

maplot(dge1_o,"Cont1: Effect of OVA treatment in hypothalamus")

make_volcano(dge1_o,"Cont1: Effect of OVA treatment in hypothalamus")

make_heatmap(de=dge1_o,name="Cont1: Effect of OVA treatment in hypothalamus",myss=ss1_o,mx=xx1_o,n=50)

make_heatmap2(de=dge1_o,name="Cont1: Effect of OVA treatment in hypothalamus",myss=ss1_o,mx=xx1_o,n=50)

dge1_o[1:20,1:6] %>% kbl(caption = "Top gene expression differences for contrast 1: Effect of treatment in hypothalamus") %>%
  kable_paper("hover", full_width = F)

Top gene expression differences for contrast 1: Effect of treatment in hypothalamus

	baseMean	log2FoldChange	lfcSE	stat	pvalue	padj
ENSMUSG00000078880.11 Gm14308	92.882830	-0.9356691	0.2192963	-4.266689	0.0000198	0.3395346
ENSMUSG00000036915.18 Kirrel2	169.164941	0.8427760	0.2297223	3.668673	0.0002438	0.9998282
ENSMUSG00000044285.9 Ubb-ps	2336.340595	-0.3680306	0.1025092	-3.590221	0.0003304	0.9998282
ENSMUSG00000085328.3 Gm17131	141.467376	-0.6729148	0.1891178	-3.558178	0.0003734	0.9998282
ENSMUSG00000030413.8 Pglyrp1	106.131949	0.6503273	0.1833759	3.546416	0.0003905	0.9998282
ENSMUSG00000033502.16 Cdc14a	100.974101	0.4596276	0.1301010	3.532853	0.0004111	0.9998282
ENSMUSG00000108601.2 Gm44645	94.446899	0.6303080	0.1803630	3.494664	0.0004747	0.9998282
ENSMUSG00000075014.2 Gm10800	33.751721	3.2629734	0.9463915	3.447805	0.0005652	0.9998282
ENSMUSG00000054252.19 Fgfr3	2730.435705	0.2147599	0.0630824	3.404437	0.0006630	0.9998282
ENSMUSG00000059970.8 Hspa2	641.897572	0.3132088	0.0939489	3.333822	0.0008566	0.9998282
ENSMUSG00000092483.2 Gm20421	22.954083	0.7081938	0.2204315	3.212761	0.0013147	0.9998282
ENSMUSG00000021892.15 Sh3bp5	762.980552	-0.1810008	0.0565574	-3.200302	0.0013728	0.9998282
ENSMUSG00000041079.13 Rwdd2b	175.004412	0.2696634	0.0866861	3.110802	0.0018658	0.9998282
ENSMUSG00000044707.8 Ccnjl	31.491228	-0.6302744	0.2031683	-3.102228	0.0019207	0.9998282
ENSMUSG00000053279.9 Aldh1a1	339.445293	0.4826510	0.1581715	3.051441	0.0022775	0.9998282
ENSMUSG00000020123.7 Avpr1a	98.069219	0.4957189	0.1626906	3.047004	0.0023113	0.9998282
ENSMUSG00000020374.17 Rasgef1c	258.064242	-0.2698852	0.0889452	-3.034285	0.0024111	0.9998282
ENSMUSG00000091318.3 Gm5415	9.042075	1.4281893	0.4797236	2.977109	0.0029098	0.9998282
ENSMUSG00000021750.17 Fam107a	3483.985160	0.2995835	0.1007207	2.974398	0.0029356	0.9998282
ENSMUSG00000013089.16 Etv5	587.848238	0.2785646	0.0939914	2.963723	0.0030394	0.9998282

pdf("plot12_de01.pdf")
mymds(de=dge1_l,name="Cont1: Effect of LPS treatment in hypothalamus",myss=ss1_l,mx=xx1_l)
maplot(dge1_l,"Cont1: Effect of LPS treatment in hypothalamus")
make_volcano(dge1_l,"Cont1: Effect of LPS treatment in hypothalamus")
make_heatmap(de=dge1_l,name="Cont1: Effect of LPS treatment in hypothalamus",myss=ss1_l,mx=xx1_l,n=50)
make_heatmap2(de=dge1_l,name="Cont1: Effect of LPS treatment in hypothalamus",myss=ss1_l,mx=xx1_l,n=50)
mymds(de=dge1_o,name="Cont1: Effect of OVA treatment in hypothalamus",myss=ss1_o,mx=xx1_o)
maplot(dge1_o,"Cont1: Effect of OVA treatment in hypothalamus")
make_volcano(dge1_o,"Cont1: Effect of OVA treatment in hypothalamus")
make_heatmap(de=dge1_o,name="Cont1: Effect of OVA treatment in hypothalamus",myss=ss1_o,mx=xx1_o,n=50)
make_heatmap2(de=dge1_o,name="Cont1: Effect of OVA treatment in hypothalamus",myss=ss1_o,mx=xx1_o,n=50)
dev.off()

## X11cairo 
##        2

Results for amygdala

mymds(de=dge2_l,name="Cont2: Effect of LPS treatment in amygdala",myss=ss2_l,mx=xx2_l)

maplot(dge2_l,"Cont2: Effect of LPS treatment in amygdala")

make_volcano(dge2_l,"Cont2: Effect of LPS treatment in amygdala")

make_heatmap(de=dge2_l,name="Cont2: Effect of LPS treatment in amygdala",myss=ss2_l,mx=xx2_l,n=50)

make_heatmap2(de=dge2_l,name="Cont2: Effect of LPS treatment in amygdala",myss=ss2_l,mx=xx2_l,n=50)

dge2_l[1:20,1:6] %>% kbl(caption = "Top gene expression differences for contrast 2: Effect of LPS treatment in amygdala") %>%
  kable_paper("hover", full_width = F)

Top gene expression differences for contrast 2: Effect of LPS treatment in amygdala

	baseMean	log2FoldChange	lfcSE	stat	pvalue	padj
ENSMUSG00000025498.16 Irf7	54.87672	-1.2294874	0.2232120	-5.508159	0.0000000	0.0006098
ENSMUSG00000079363.8 Gbp4	62.26653	-0.9118851	0.1718895	-5.305066	0.0000001	0.0006560
ENSMUSG00000036915.18 Kirrel2	254.72910	0.6575428	0.1241095	5.298088	0.0000001	0.0006560
ENSMUSG00000032902.2 Slc16a1	429.58840	-0.4275636	0.0835198	-5.119305	0.0000003	0.0011124
ENSMUSG00000018822.8 Sfrp5	179.17105	0.6336687	0.1241260	5.105042	0.0000003	0.0011124
ENSMUSG00000078921.4 Tgtp2	10.75353	-2.2904610	0.4554595	-5.028902	0.0000005	0.0013827
ENSMUSG00000027875.13 Hmgcs2	130.58206	-0.7567441	0.1547343	-4.890602	0.0000010	0.0024153
ENSMUSG00000007097.15 Atp1a2	13121.54535	0.2852928	0.0589693	4.837987	0.0000013	0.0027573
ENSMUSG00000027333.19 Smox	993.08881	0.3229665	0.0686244	4.706291	0.0000025	0.0047140
ENSMUSG00000004891.17 Nes	147.63914	0.5842943	0.1253589	4.660972	0.0000031	0.0052929
ENSMUSG00000102275.2 Gm37144	83.14632	-1.1552394	0.2515488	-4.592506	0.0000044	0.0066959
ENSMUSG00000089824.11 Rbm12	379.55172	-0.3020121	0.0696401	-4.336755	0.0000145	0.0202659
ENSMUSG00000060862.11 Zbtb40	209.30249	0.4768500	0.1107158	4.306972	0.0000166	0.0214112
ENSMUSG00000044712.16 Slc38a6	1014.61207	-0.4381471	0.1034282	-4.236245	0.0000227	0.0273039
ENSMUSG00000020680.16 Taf15	956.83934	0.3203512	0.0800847	4.000154	0.0000633	0.0709734
ENSMUSG00000034173.14 Zbed5	124.05394	-0.4521479	0.1157090	-3.907630	0.0000932	0.0979709
ENSMUSG00000006390.16 Elovl1	217.76918	0.3244737	0.0834422	3.888604	0.0001008	0.0997431
ENSMUSG00000052911.10 Lamb2	645.20070	0.3401842	0.0883340	3.851114	0.0001176	0.1098606
ENSMUSG00000031465.7 Angpt2	32.01754	-0.7723745	0.2036123	-3.793359	0.0001486	0.1315547
ENSMUSG00000100750.2 Gm29084	513.48571	-0.3339886	0.0899518	-3.712974	0.0002048	0.1722483

mymds(de=dge2_o,name="Cont2: Effect of OVA treatment in amygdala",myss=ss2_o,mx=xx2_o)

maplot(dge2_o,"Cont2: Effect of OVA treatment in amygdala")

make_volcano(dge2_o,"Cont2: Effect of OVA treatment in amygdala")

make_heatmap(de=dge2_o,name="Cont2: Effect of OVA treatment in amygdala",myss=ss2_o,mx=xx2_o,n=50)

make_heatmap2(de=dge2_o,name="Cont2: Effect of OVA treatment in amygdala",myss=ss2_o,mx=xx2_o,n=50)

dge2_o[1:20,1:6] %>% kbl(caption = "Top gene expression differences for contrast 2: Effect of treatment in amygdala") %>%
  kable_paper("hover", full_width = F)

Top gene expression differences for contrast 2: Effect of treatment in amygdala

	baseMean	log2FoldChange	lfcSE	stat	pvalue	padj
ENSMUSG00000107676.2 Gm44178	185.4223	-0.8648437	0.1423153	-6.076956	0.0e+00	0.0000164
ENSMUSG00000025791.19 Pgm1	1679.6378	0.2939654	0.0506255	5.806664	0.0e+00	0.0000315
ENSMUSG00000030889.15 Vwa3a	101.9254	-0.7011680	0.1211103	-5.789499	0.0e+00	0.0000315
ENSMUSG00000059149.18 Mfsd4a	841.7077	-0.3390365	0.0621451	-5.455561	0.0e+00	0.0001636
ENSMUSG00000036915.18 Kirrel2	266.3206	0.8297410	0.1549163	5.356062	1.0e-07	0.0002019
ENSMUSG00000024782.8 Ak3	1076.8616	0.3287706	0.0615097	5.345017	1.0e-07	0.0002019
ENSMUSG00000018822.8 Sfrp5	171.4710	0.6007642	0.1168718	5.140370	3.0e-07	0.0005249
ENSMUSG00000032507.16 Fbxl2	973.7927	-0.3006599	0.0592058	-5.078214	4.0e-07	0.0006064
ENSMUSG00000053279.9 Aldh1a1	232.1522	0.5728101	0.1130798	5.065538	4.0e-07	0.0006064
ENSMUSG00000034413.15 Neurl1b	998.7317	-0.4392775	0.0892047	-4.924376	8.0e-07	0.0010672
ENSMUSG00000038633.6 Degs1	1216.6775	0.3005384	0.0611145	4.917627	9.0e-07	0.0010672
ENSMUSG00000030256.12 Bhlhe41	1281.0403	0.3288133	0.0684756	4.801908	1.6e-06	0.0017551
ENSMUSG00000030499.10 Kctd15	178.8979	-0.4463070	0.0942096	-4.737384	2.2e-06	0.0022317
ENSMUSG00000030209.15 Grin2b	4116.6194	-0.4866258	0.1043231	-4.664600	3.1e-06	0.0029599
ENSMUSG00000038738.16 Shank1	6687.6830	-0.3929989	0.0868324	-4.525946	6.0e-06	0.0053717
ENSMUSG00000006390.16 Elovl1	227.2895	0.5070433	0.1126133	4.502516	6.7e-06	0.0054351
ENSMUSG00000038260.11 Trpm4	233.7264	-0.4194385	0.0935316	-4.484457	7.3e-06	0.0054351
ENSMUSG00000034818.18 Celf5	4451.3635	-0.3143020	0.0701827	-4.478343	7.5e-06	0.0054351
ENSMUSG00000079018.11 Ly6c1	374.3433	0.4597376	0.1027940	4.472418	7.7e-06	0.0054351
ENSMUSG00000031513.9 Leprotl1	1221.1671	0.2195997	0.0492131	4.462220	8.1e-06	0.0054351

pdf("plot13_de02.pdf")
mymds(de=dge2_l,name="Cont2: Effect of LPS treatment in amygdala",myss=ss2_l,mx=xx2_l)
maplot(dge2_l,"Cont2: Effect of LPS treatment in amygdala")
make_volcano(dge2_l,"Cont2: Effect of LPS treatment in amygdala")
make_heatmap(de=dge2_l,name="Cont2: Effect of LPS treatment in amygdala",myss=ss2_l,mx=xx2_l,n=50)
make_heatmap2(de=dge2_l,name="Cont2: Effect of LPS treatment in amygdala",myss=ss2_l,mx=xx2_l,n=50)
mymds(de=dge2_o,name="Cont2: Effect of OVA treatment in amygdala",myss=ss2_o,mx=xx2_o)
maplot(dge2_o,"Cont2: Effect of OVA treatment in amygdala")
make_volcano(dge2_o,"Cont2: Effect of OVA treatment in amygdala")
make_heatmap(de=dge2_o,name="Cont2: Effect of OVA treatment in amygdala",myss=ss2_o,mx=xx2_o,n=50)
make_heatmap2(de=dge2_o,name="Cont2: Effect of OVA treatment in amygdala",myss=ss2_o,mx=xx2_o,n=50)
dev.off()

## X11cairo 
##        2

Results for hippocampus

mymds(de=dge3_l,name="Cont3: Effect of LPS treatment in hippocampus",myss=ss3_l,mx=xx3_l)

maplot(dge3_l,"Cont3: Effect of LPS treatment in hippocampus")

make_volcano(dge3_l,"Cont3: Effect of LPS treatment in hippocampus")

make_heatmap(de=dge3_l,name="Cont3: Effect of LPS treatment in hippocampus",myss=ss3_l,mx=xx3_l,n=50)

make_heatmap2(de=dge3_l,name="Cont3: Effect of LPS treatment in hippocampus",myss=ss3_l,mx=xx3_l,n=50)

dge3_l[1:20,1:6] %>% kbl(caption = "Top gene expression differences for contrast 3: Effect of LPS treatment in hippocampus") %>%
  kable_paper("hover", full_width = F)

Top gene expression differences for contrast 3: Effect of LPS treatment in hippocampus

	baseMean	log2FoldChange	lfcSE	stat	pvalue	padj
ENSMUSG00000100862.2 Gm10925	4975.55176	7.1868883	0.6335748	11.343393	0	0
ENSMUSG00000070713.6 Gm10282	208.77183	-1.0025619	0.0983370	-10.195161	0	0
ENSMUSG00000040447.16 Spns2	1554.46939	0.5308742	0.0527511	10.063750	0	0
ENSMUSG00000081603.2 Gm14681	107.33155	-1.1109529	0.1168663	-9.506187	0	0
ENSMUSG00000068882.14 Ssb	1815.27129	-0.5934983	0.0624491	-9.503708	0	0
ENSMUSG00000096006.2 Gm21596	253.23289	-0.7510200	0.0804176	-9.338997	0	0
ENSMUSG00000024261.7 Syt4	2094.97545	-0.5610059	0.0603557	-9.294993	0	0
ENSMUSG00000025104.14 Hdgfl3	2080.41389	-0.4435812	0.0489446	-9.062924	0	0
ENSMUSG00000017831.9 Rab5a	2217.40639	-0.4345348	0.0480838	-9.037037	0	0
ENSMUSG00000047879.18 Usp14	2271.47824	-0.4272710	0.0481656	-8.870869	0	0
ENSMUSG00000022884.17 Eif4a2	11561.93282	-0.4719840	0.0534312	-8.833493	0	0
ENSMUSG00000001891.17 Ugp2	1364.63153	-0.5993403	0.0678779	-8.829678	0	0
ENSMUSG00000084838.4 Gm10241	29.45661	8.3716581	0.9510624	8.802428	0	0
ENSMUSG00000026775.10 Yme1l1	1252.73281	-0.4786876	0.0544396	-8.792996	0	0
ENSMUSG00000020650.16 Bcap29	663.57029	-0.7001171	0.0803572	-8.712561	0	0
ENSMUSG00000042303.20 Sgsm3	1281.38919	0.3735305	0.0430203	8.682650	0	0
ENSMUSG00000071533.12 Pcnp	2553.37357	-0.4279186	0.0500775	-8.545132	0	0
ENSMUSG00000020238.15 Ncln	1446.32940	0.4261658	0.0499872	8.525490	0	0
ENSMUSG00000014905.5 Dnajb9	663.84669	-0.6632246	0.0778235	-8.522161	0	0
ENSMUSG00000057963.10 Itpk1	1451.17452	0.4522885	0.0532812	8.488701	0	0

mymds(de=dge3_o,name="Cont3: Effect of OVA treatment in hippocampus",myss=ss3_o,mx=xx3_o)

maplot(dge3_o,"Cont3: Effect of OVA treatment in hippocampus")

make_volcano(dge3_o,"Cont3: Effect of OVA treatment in hippocampus")

make_heatmap(de=dge3_o,name="Cont3: Effect of OVA treatment in hippocampus",myss=ss3_o,mx=xx3_o,n=50)

make_heatmap2(de=dge3_o,name="Cont3: Effect of OVA treatment in hippocampus",myss=ss3_o,mx=xx3_o,n=50)

dge3_o[1:20,1:6] %>% kbl(caption = "Top gene expression differences for contrast 3: Effect of treatment in hippocampus") %>%
  kable_paper("hover", full_width = F)

Top gene expression differences for contrast 3: Effect of treatment in hippocampus

	baseMean	log2FoldChange	lfcSE	stat	pvalue	padj
ENSMUSG00000008668.16 Rps18	286.52172	-1.2451711	0.1674818	-7.434667	0.0e+00	0.0000000
ENSMUSG00000069008.4 Gm5537	14.35881	21.8370339	2.9884331	7.307185	0.0e+00	NA
ENSMUSG00000103034.2 Gm8797	107.80783	-1.1733731	0.1762296	-6.658206	0.0e+00	NA
ENSMUSG00000100862.2 Gm10925	1945.80382	5.8231351	0.9533584	6.108023	0.0e+00	0.0000059
ENSMUSG00000040447.16 Spns2	1527.35914	0.4988942	0.0886928	5.624970	0.0e+00	0.0000723
ENSMUSG00000081603.2 Gm14681	113.88393	-0.8342655	0.1522504	-5.479560	0.0e+00	0.0001247
ENSMUSG00000031858.17 Mau2	1851.99608	0.4053541	0.0759055	5.340248	1.0e-07	0.0002171
ENSMUSG00000084838.4 Gm10241	17.62940	7.6366750	1.4333637	5.327800	1.0e-07	NA
ENSMUSG00000021270.14 Hsp90aa1	19731.71056	-0.4150097	0.0812580	-5.107311	3.0e-07	0.0006369
ENSMUSG00000024883.9 Rin1	1730.36849	0.3961802	0.0785513	5.043585	5.0e-07	0.0007633
ENSMUSG00000001467.6 Cyp51	914.60992	-0.3266949	0.0652770	-5.004751	6.0e-07	0.0008176
ENSMUSG00000102627.2 Snurf	22.95625	8.0174386	1.6395147	4.890129	1.0e-06	NA
ENSMUSG00000044285.9 Ubb-ps	2010.40856	-0.9247134	0.1896289	-4.876438	1.1e-06	0.0014035
ENSMUSG00000107499.2 Ccdc142	44.66779	1.0467235	0.2150830	4.866603	1.1e-06	NA
ENSMUSG00000060862.11 Zbtb40	211.85127	0.6799751	0.1415661	4.803233	1.6e-06	0.0018257
ENSMUSG00000053550.14 Shisa7	2819.07578	0.6097051	0.1286592	4.738915	2.1e-06	0.0021791
ENSMUSG00000048154.18 Kmt2d	2495.07018	0.7784121	0.1651030	4.714707	2.4e-06	0.0021791
ENSMUSG00000032855.7 Pkd1	2513.84342	0.7124951	0.1511267	4.714553	2.4e-06	0.0021791
ENSMUSG00000078881.11 Gm14434	265.26689	-1.1328384	0.2439904	-4.642963	3.4e-06	0.0028688
ENSMUSG00000094437.3 Gm9830	43.94751	1.7576897	0.3813574	4.609036	4.0e-06	NA

pdf("plot14_de03.pdf")
mymds(de=dge3_l,name="Cont3: Effect of LPS treatment in hippocampus",myss=ss3_l,mx=xx3_l)
maplot(dge3_l,"Cont3: Effect of LPS treatment in hippocampus")
make_volcano(dge3_l,"Cont3: Effect of LPS treatment in hippocampus")
make_heatmap(de=dge3_l,name="Cont3: Effect of LPS treatment in hippocampus",myss=ss3_l,mx=xx3_l,n=50)
make_heatmap2(de=dge3_l,name="Cont3: Effect of LPS treatment in hippocampus",myss=ss3_l,mx=xx3_l,n=50)
mymds(de=dge3_o,name="Cont3: Effect of OVA treatment in hippocampus",myss=ss3_o,mx=xx3_o)
maplot(dge3_o,"Cont3: Effect of OVA treatment in hippocampus")
make_volcano(dge3_o,"Cont3: Effect of OVA treatment in hippocampus")
make_heatmap(de=dge3_o,name="Cont3: Effect of OVA treatment in hippocampus",myss=ss3_o,mx=xx3_o,n=50)
make_heatmap2(de=dge3_o,name="Cont3: Effect of OVA treatment in hippocampus",myss=ss3_o,mx=xx3_o,n=50)
dev.off()

## X11cairo 
##        2

Results for PAG

mymds(de=dge4_l,name="Cont4: Effect of LPS treatment in PAG",myss=ss4_l,mx=xx4_l)

maplot(dge4_l,"Cont4: Effect of LPS treatment in PAG")

make_volcano(dge4_l,"Cont4: Effect of LPS treatment in PAG")

make_heatmap(de=dge4_l,name="Cont4: Effect of LPS treatment in PAG",myss=ss4_l,mx=xx4_l,n=50)

make_heatmap2(de=dge4_l,name="Cont3: Effect of LPS treatment in PAG",myss=ss4_l,mx=xx4_l,n=50)

dge4_l[1:20,1:6] %>% kbl(caption = "Top gene expression differences for contrast 4: Effect of LPS treatment in PAG") %>%
  kable_paper("hover", full_width = F)

Top gene expression differences for contrast 4: Effect of LPS treatment in PAG

	baseMean	log2FoldChange	lfcSE	stat	pvalue	padj
ENSMUSG00000101111.2 Gm28437	1489.59991	-28.4058070	2.9866471	-9.510935	0.0000000	0.0000000
ENSMUSG00000034459.9 Ifit1	55.64874	-1.1808759	0.1686328	-7.002649	0.0000000	0.0000000
ENSMUSG00000079363.8 Gbp4	95.91494	-1.2292487	0.1854179	-6.629613	0.0000000	0.0000002
ENSMUSG00000025498.16 Irf7	84.22641	-1.0483087	0.1595935	-6.568618	0.0000000	0.0000002
ENSMUSG00000078922.10 Tgtp1	21.82329	-2.6093058	0.4065209	-6.418626	0.0000000	0.0000005
ENSMUSG00000102070.2 Gm28661	10196.94116	-14.7763443	2.9540986	-5.001981	0.0000006	0.0016656
ENSMUSG00000028820.14 Sfpq	2198.16793	0.2648449	0.0535496	4.945791	0.0000008	0.0019080
ENSMUSG00000036915.18 Kirrel2	82.56463	0.9736571	0.2004918	4.856344	0.0000012	0.0026324
ENSMUSG00000026222.17 Sp100	58.86066	-0.8315047	0.1797559	-4.625743	0.0000037	0.0073042
ENSMUSG00000075602.11 Ly6a	107.28664	-0.8323082	0.1822514	-4.566814	0.0000050	0.0087213
ENSMUSG00000024079.5 Eif2ak2	182.29278	-0.3940654	0.0893055	-4.412555	0.0000102	0.0155853
ENSMUSG00000078921.4 Tgtp2	20.19923	-1.2810520	0.2908727	-4.404167	0.0000106	0.0155853
ENSMUSG00000052776.11 Oas1a	19.16469	-1.2619796	0.2896101	-4.357512	0.0000132	0.0178219
ENSMUSG00000028268.15 Gbp3	138.45727	-0.5635390	0.1327639	-4.244671	0.0000219	0.0275394
ENSMUSG00000054072.13 Iigp1	23.29794	-1.4762784	0.3644390	-4.050824	0.0000510	0.0599248
ENSMUSG00000110862.2 Gm35835	17.20473	-1.2465344	0.3123628	-3.990663	0.0000659	0.0685953
ENSMUSG00000027875.13 Hmgcs2	123.00353	-0.7999616	0.2005166	-3.989504	0.0000662	0.0685953
ENSMUSG00000002289.17 Angptl4	67.16713	-0.6692079	0.1702196	-3.931438	0.0000844	0.0826191
ENSMUSG00000015085.9 Entpd2	359.15806	0.3576188	0.0921084	3.882584	0.0001034	0.0954024
ENSMUSG00000030107.11 Usp18	27.77358	-0.9172425	0.2372305	-3.866461	0.0001104	0.0954024

mymds(de=dge4_o,name="Cont4: Effect of OVA treatment in PAG",myss=ss4_o,mx=xx4_o)

maplot(dge4_o,"Cont4: Effect of OVA treatment in PAG")

make_volcano(dge4_o,"Cont4: Effect of OVA treatment in PAG")

make_heatmap(de=dge4_o,name="Cont4: Effect of OVA treatment in PAG",myss=ss4_o,mx=xx4_o,n=50)

make_heatmap2(de=dge4_o,name="Cont4: Effect of OVA treatment in PAG",myss=ss4_o,mx=xx4_o,n=50)

dge4_o[1:20,1:6] %>% kbl(caption = "Top gene expression differences for contrast 4: Effect of treatment in PAG") %>%
  kable_paper("hover", full_width = F)

Top gene expression differences for contrast 4: Effect of treatment in PAG

	baseMean	log2FoldChange	lfcSE	stat	pvalue	padj
ENSMUSG00000101111.2 Gm28437	1504.44501	-28.8708740	2.9866471	-9.666651	0.0000000	0.0000000
ENSMUSG00000036915.18 Kirrel2	88.57030	1.1004532	0.2077438	5.297165	0.0000001	0.0010387
ENSMUSG00000106604.3 Gm42535	45.90967	-1.3651174	0.2757149	-4.951192	0.0000007	0.0043425
ENSMUSG00000102070.2 Gm28661	10298.72248	-14.1414027	2.9291777	-4.827772	0.0000014	0.0060964
ENSMUSG00000085328.3 Gm17131	231.90596	-0.6897420	0.1730516	-3.985758	0.0000673	0.2376070
ENSMUSG00000118642.2 AY036118	813.47892	-0.9127923	0.2465059	-3.702922	0.0002131	0.6273842
ENSMUSG00000102289.2 Gm31258	14.36741	-1.5814526	0.4320332	-3.660488	0.0002517	0.6351635
ENSMUSG00000021906.15 Oxnad1	298.24353	0.2627823	0.0731645	3.591666	0.0003286	0.7254015
ENSMUSG00000028820.14 Sfpq	2221.70651	0.2671342	0.0763080	3.500736	0.0004640	0.9105264
ENSMUSG00000094672.3 Vmn2r25	14.38564	-1.6452102	0.4753780	-3.460847	0.0005385	0.9510627
ENSMUSG00000054252.19 Fgfr3	2567.78162	0.3560764	0.1040765	3.421295	0.0006232	0.9754406
ENSMUSG00000024998.18 Plce1	525.21275	0.3861609	0.1134704	3.403185	0.0006661	0.9754406
ENSMUSG00000013089.16 Etv5	501.24250	0.2879667	0.0851978	3.379980	0.0007249	0.9754406
ENSMUSG00000047216.9 Cdh19	172.79548	0.5469923	0.1627061	3.361844	0.0007742	0.9754406
ENSMUSG00000115632.2 Gm17922	12.17839	-2.0478089	0.6150693	-3.329396	0.0008703	0.9754406
ENSMUSG00000067220.13 Cnga1	22.07693	-1.3574772	0.4082491	-3.325120	0.0008838	0.9754406
ENSMUSG00000046678.7 Olfr981	15.67008	-1.2921922	0.3905994	-3.308229	0.0009389	0.9754406
ENSMUSG00000007682.8 Dio2	395.46383	0.4716641	0.1435644	3.285383	0.0010184	0.9904947
ENSMUSG00000108827.4 Olfr1310	28.09654	-1.3088894	0.4003901	-3.269035	0.0010791	0.9904947
ENSMUSG00000002831.15 Plin4	203.85756	1.4385797	0.4416960	3.256946	0.0011262	0.9904947

pdf("plot15_de04.pdf")
mymds(de=dge4_l,name="Cont4: Effect of LPS treatment in PAG",myss=ss4_l,mx=xx4_l)
maplot(dge4_l,"Cont4: Effect of LPS treatment in PAG")
make_volcano(dge4_l,"Cont4: Effect of LPS treatment in PAG")
make_heatmap(de=dge4_l,name="Cont4: Effect of LPS treatment in PAG",myss=ss4_l,mx=xx4_l,n=50)
make_heatmap2(de=dge4_l,name="Cont3: Effect of LPS treatment in PAG",myss=ss4_l,mx=xx4_l,n=50)
mymds(de=dge4_o,name="Cont4: Effect of OVA treatment in PAG",myss=ss4_o,mx=xx4_o)
maplot(dge4_o,"Cont4: Effect of OVA treatment in PAG")
make_volcano(dge4_o,"Cont4: Effect of OVA treatment in PAG")
make_heatmap(de=dge4_o,name="Cont4: Effect of OVA treatment in PAG",myss=ss4_o,mx=xx4_o,n=50)
make_heatmap2(de=dge4_o,name="Cont4: Effect of OVA treatment in PAG",myss=ss4_o,mx=xx4_o,n=50)
dev.off()

## X11cairo 
##        2

Results for NI

mymds(de=dge5_l,name="Cont5: Effect of LPS treatment in NI",myss=ss5_l,mx=xx5_l)

maplot(dge5_l,"Cont5: Effect of LPS treatment in NI")

make_volcano(dge5_l,"Cont5: Effect of LPS treatment in NI")

make_heatmap(de=dge5_l,name="Cont5: Effect of LPS treatment in NI",myss=ss5_l,mx=xx5_l,n=50)

make_heatmap2(de=dge5_l,name="Cont3: Effect of LPS treatment in NI",myss=ss5_l,mx=xx5_l,n=50)

dge5_l[1:20,1:6] %>% kbl(caption = "Top gene expression differences for contrast 5: Effect of LPS treatment in NI") %>%
  kable_paper("hover", full_width = F)

Top gene expression differences for contrast 5: Effect of LPS treatment in NI

	baseMean	log2FoldChange	lfcSE	stat	pvalue	padj
ENSMUSG00000004655.6 Aqp1	64.33666	-2.5245691	0.4256671	-5.930853	0.0e+00	0.0000404
ENSMUSG00000002831.15 Plin4	250.84384	1.5543801	0.2773464	5.604472	0.0e+00	0.0001368
ENSMUSG00000026822.15 Lcn2	16.22880	-2.6059016	0.4734932	-5.503567	0.0e+00	NA
ENSMUSG00000028919.12 Arhgef19	395.89057	0.4100393	0.0746763	5.490892	0.0e+00	0.0001368
ENSMUSG00000022425.17 Enpp2	8280.82063	-1.1048917	0.2013475	-5.487486	0.0e+00	0.0001368
ENSMUSG00000068699.13 Flnc	125.76677	0.8058438	0.1494388	5.392467	1.0e-07	0.0001573
ENSMUSG00000053646.14 Plxnb1	2389.38786	0.3753821	0.0697524	5.381640	1.0e-07	0.0001573
ENSMUSG00000071267.12 Zfp942	226.23109	-0.5520131	0.1029389	-5.362533	1.0e-07	0.0001573
ENSMUSG00000036890.14 Gtdc1	411.57310	-0.4107206	0.0781476	-5.255700	1.0e-07	0.0002474
ENSMUSG00000078922.10 Tgtp1	37.06696	-2.2129060	0.4225152	-5.237459	2.0e-07	NA
ENSMUSG00000039959.14 Hip1	856.67943	0.5239050	0.1013262	5.170477	2.0e-07	0.0003482
ENSMUSG00000068323.13 Slc4a5	92.61322	-1.8988675	0.3702782	-5.128219	3.0e-07	0.0003771
ENSMUSG00000114133.2 Gm20075	468.65132	-0.3716223	0.0726130	-5.117845	3.0e-07	0.0003771
ENSMUSG00000021696.10 Elovl7	524.66657	-0.4441318	0.0873935	-5.081979	4.0e-07	0.0004178
ENSMUSG00000027875.13 Hmgcs2	132.26542	-1.1687659	0.2334476	-5.006545	6.0e-07	0.0005721
ENSMUSG00000060862.11 Zbtb40	245.30142	0.6788979	0.1360226	4.991069	6.0e-07	0.0005756
ENSMUSG00000113337.2 Gm19220	2324.88228	1.0465161	0.2131860	4.908936	9.0e-07	0.0008193
ENSMUSG00000045039.10 Megf8	3979.39321	0.3979746	0.0822716	4.837328	1.3e-06	0.0011038
ENSMUSG00000069833.14 Ahnak	814.46670	0.6652302	0.1391700	4.779984	1.8e-06	0.0013840
ENSMUSG00000015652.10 Steap1	18.65542	-2.4798774	0.5203028	-4.766219	1.9e-06	NA

mymds(de=dge5_o,name="Cont5: Effect of OVA treatment in NI",myss=ss5_o,mx=xx5_o)

maplot(dge5_o,"Cont4: Effect of OVA treatment in NI")

make_volcano(dge5_o,"Cont5: Effect of OVA treatment in NI")

make_heatmap(de=dge5_o,name="Cont5: Effect of OVA treatment in NI",myss=ss5_o,mx=xx5_o,n=50)

make_heatmap2(de=dge5_o,name="Cont5: Effect of OVA treatment in NI",myss=ss5_o,mx=xx5_o,n=50)

dge5_o[1:20,1:6] %>% kbl(caption = "Top gene expression differences for contrast 5: Effect of treatment in NI") %>%
  kable_paper("hover", full_width = F)

Top gene expression differences for contrast 5: Effect of treatment in NI

	baseMean	log2FoldChange	lfcSE	stat	pvalue	padj
ENSMUSG00000056260.16 Lrif1	211.98587	-0.7336887	0.1100875	-6.664595	0.00e+00	0.0000003
ENSMUSG00000002831.15 Plin4	258.40488	1.6709594	0.2980839	5.605668	0.00e+00	0.0001173
ENSMUSG00000055322.17 Tns1	1466.90156	0.3981360	0.0721780	5.516031	0.00e+00	0.0001307
ENSMUSG00000015944.10 Castor2	3213.72353	0.3932529	0.0758616	5.183820	2.00e-07	0.0006144
ENSMUSG00000024175.3 Tekt4	67.10906	0.9393058	0.1822044	5.155233	3.00e-07	NA
ENSMUSG00000020116.8 Pno1	507.57144	-0.3892492	0.0798403	-4.875347	1.10e-06	0.0024561
ENSMUSG00000000194.14 Gpr107	1069.00316	0.3266125	0.0685118	4.767246	1.90e-06	0.0035192
ENSMUSG00000055782.10 Abcd2	665.20587	-0.4696031	0.0996124	-4.714301	2.40e-06	0.0035595
ENSMUSG00000117284.2 Gm7072	208.57068	-0.5445045	0.1156892	-4.706613	2.50e-06	0.0035595
ENSMUSG00000074505.7 Fat3	548.94128	0.5489851	0.1177974	4.660416	3.20e-06	0.0039643
ENSMUSG00000060862.11 Zbtb40	233.67044	0.5799773	0.1251034	4.635981	3.60e-06	0.0040164
ENSMUSG00000069833.14 Ahnak	857.16074	0.8065828	0.1763222	4.574483	4.80e-06	0.0046838
ENSMUSG00000067038.7 Rps12-ps3	447.47971	-1.9169288	0.4198274	-4.565993	5.00e-06	0.0046838
ENSMUSG00000027708.15 Dcun1d1	788.59922	-0.3874162	0.0869258	-4.456861	8.30e-06	0.0072331
ENSMUSG00000045039.10 Megf8	3981.85021	0.4012817	0.0919057	4.366235	1.26e-05	0.0101992
ENSMUSG00000043733.15 Ptpn11	4319.80686	0.3173738	0.0730162	4.346623	1.38e-05	0.0101992
ENSMUSG00000019232.15 Etnppl	1154.63560	0.7040907	0.1623392	4.337157	1.44e-05	0.0101992
ENSMUSG00000068798.11 Rap1a	1471.01059	-0.4872519	0.1137471	-4.283643	1.84e-05	0.0115374
ENSMUSG00000027206.14 Cops2	2031.84090	-0.3788991	0.0885072	-4.280998	1.86e-05	0.0115374
ENSMUSG00000025903.15 Lypla1	715.48002	-0.5016851	0.1174408	-4.271815	1.94e-05	0.0115374

pdf("plot16_de05.pdf")
mymds(de=dge5_l,name="Cont5: Effect of LPS treatment in NI",myss=ss5_l,mx=xx5_l)
maplot(dge5_l,"Cont5: Effect of LPS treatment in NI")
make_volcano(dge5_l,"Cont5: Effect of LPS treatment in NI")
make_heatmap(de=dge5_l,name="Cont5: Effect of LPS treatment in NI",myss=ss5_l,mx=xx5_l,n=50)
make_heatmap2(de=dge5_l,name="Cont3: Effect of LPS treatment in NI",myss=ss5_l,mx=xx5_l,n=50)
mymds(de=dge5_o,name="Cont5: Effect of OVA treatment in NI",myss=ss5_o,mx=xx5_o)
maplot(dge5_o,"Cont4: Effect of OVA treatment in NI")
make_volcano(dge5_o,"Cont5: Effect of OVA treatment in NI")
make_heatmap(de=dge5_o,name="Cont5: Effect of OVA treatment in NI",myss=ss5_o,mx=xx5_o,n=50)
make_heatmap2(de=dge5_o,name="Cont5: Effect of OVA treatment in NI",myss=ss5_o,mx=xx5_o,n=50)
dev.off()

## X11cairo 
##        2

Euler diagrams

Below we’re comparing the effect of the two treatments on the number of genes found, using an Euler diagram.

par(mar=c(2,2,2,2))

v1 <- list("hyp LPS up"=d1up_l,
  "hyp LPS dn"=d1dn_l,
  "hyp OVA up"=d1up_o,
  "hyp OVA dn"=d1dn_o)
plot(euler(v1),quantities = TRUE, edges = "gray", main="effect of treatments on hyp")

v2 <- list("amy LPS up"=d2up_l,
  "amy LPS dn"=d2dn_l,
  "amy OVA up"=d2up_o,
  "amy OVA dn"=d2dn_o)
plot(euler(v2),quantities = TRUE, edges = "gray", main="effect of treatments on amy")

v3 <- list("hip LPS up"=d3up_l,
  "hip LPS dn"=d3dn_l,
  "hip OVA up"=d3up_o,
  "hip OVA dn"=d3dn_o)
plot(euler(v3),quantities = TRUE, edges = "gray", main="effect of treatments on hip")

v4 <- list("pag LPS up"=d4up_l,
  "pag LPS dn"=d4dn_l,
  "pag OVA up"=d4up_o,
  "pag OVA dn"=d4dn_o)
plot(euler(v4),quantities = TRUE, edges = "gray", main="effect of treatments on pag")

v5 <- list("NI LPS up"=d5up_l,
  "NI LPS dn"=d5dn_l,
  "NI OVA up"=d5up_o,
  "NI OVA dn"=d5dn_o)
plot(euler(v5),quantities = TRUE, edges = "gray", main="effect of treatments on ni")

pdf("plot17_euler01.pdf")
plot(euler(v1),quantities = TRUE, edges = "gray", main="effect of treatments on hyp")
plot(euler(v2),quantities = TRUE, edges = "gray", main="effect of treatments on amy")
plot(euler(v3),quantities = TRUE, edges = "gray", main="effect of treatments on hip")
plot(euler(v4),quantities = TRUE, edges = "gray", main="effect of treatments on pag")
plot(euler(v5),quantities = TRUE, edges = "gray", main="effect of treatments on ni")
dev.off()

## X11cairo 
##        2

Mitch pathway analysis in hyp

Gene sets are obtained from Reactome.

Firstly need to conduct mitch enrichment analysis for each contrast separately.

if  (!file.exists("mouse_msigdb_reactome_2022-02-16.gmt")) {
  download.file("http://ziemann-lab.net/public/msigdb_mouse/mouse_msigdb_reactome_2022-02-16.gmt",
    destfile="mouse_msigdb_reactome_2022-02-16.gmt")
}
genesets <- gmt_import("mouse_msigdb_reactome_2022-02-16.gmt")
names(genesets) <- gsub("REACTOME_","",names(genesets))
names(genesets) <- gsub("_"," ",names(genesets))

# gene table
gt <- as.data.frame(rownames(xx))
gt$gene <- sapply(strsplit(gt[,1]," "),"[[",2)

Mitch for LPS in hyp

m1_l <- mitch_import(dge1_l, DEtype="deseq2",geneTable=gt)

## The input is a single dataframe; one contrast only. Converting
##         it to a list for you.

## Note: Mean no. genes in input = 17095

## Note: no. genes in output = 17078

## Note: estimated proportion of input genes in output = 0.999

mres1_l <- mitch_calc(m1_l, genesets, priority="effect")

## Note: Enrichments with large effect sizes may not be
##             statistically significant.

head(mres1_l$enrichment_result,20) %>% 
  kbl(caption = "Top gene pathway differences in hypothalamus for LPS") %>%
  kable_paper("hover", full_width = F)

Top gene pathway differences in hypothalamus for LPS

	set	setSize	pANOVA	s.dist	p.adjustANOVA
726	POST CHAPERONIN TUBULIN FOLDING PATHWAY	17	0.0000074	0.6276475	0.0005672
1138	TRANSPORT OF CONNEXONS TO THE PLASMA MEMBRANE	13	0.0002276	0.5904167	0.0059636
337	FORMATION OF TUBULIN FOLDING INTERMEDIATES BY CCT TRIC	19	0.0000123	0.5793608	0.0007239
143	CGMP EFFECTS	15	0.0001518	-0.5648596	0.0046087
785	REDUCTION OF CYTOSOLIC CA LEVELS	11	0.0013299	-0.5588296	0.0211886
629	NITRIC OXIDE STIMULATES GUANYLATE CYCLASE	20	0.0000597	-0.5184078	0.0022044
910	SEALING OF THE NUCLEAR ENVELOPE NE BY ESCRT III	23	0.0000223	0.5107644	0.0010947
679	P75NTR REGULATES AXONOGENESIS	10	0.0068532	-0.4937895	0.0658661
66	APOPTOSIS INDUCED DNA FRAGMENTATION	10	0.0090864	-0.4764237	0.0799470
1173	WNT5A DEPENDENT INTERNALIZATION OF FZD2 FZD5 AND ROR2	13	0.0033668	0.4696658	0.0400954
161	CLEC7A DECTIN 1 INDUCES NFAT ACTIVATION	11	0.0108122	-0.4438070	0.0912014
368	GAP JUNCTION ASSEMBLY	21	0.0005679	0.4344397	0.0111598
498	IRE1ALPHA ACTIVATES CHAPERONES	50	0.0000004	0.4142965	0.0000789
388	GLYCOGEN STORAGE DISEASES	12	0.0142109	0.4087855	0.1069793
201	CYTOSOLIC IRON SULFUR CLUSTER ASSEMBLY	13	0.0111223	0.4066848	0.0930013
1170	VOLTAGE GATED POTASSIUM CHANNELS	39	0.0000142	-0.4016592	0.0007980
112	CARBOXYTERMINAL POST TRANSLATIONAL MODIFICATIONS OF TUBULIN	36	0.0000414	0.3947991	0.0018099
43	AGGREPHAGY	35	0.0000579	0.3927561	0.0022044
1072	THE ROLE OF GTSE1 IN G2 M PROGRESSION AFTER G2 CHECKPOINT	66	0.0000001	0.3869996	0.0000213
1006	SODIUM CALCIUM EXCHANGERS	10	0.0352725	-0.3844739	0.1970914

m1_l_top <- subset(mres1_l$enrichment_result,p.adjustANOVA<0.05)
m1_l_up <- subset(m1_l_top,s.dist>0)$set
m1_l_dn <- subset(m1_l_top,s.dist<0)$set
mitch_report(mres1_l,outfile="mitch1_l.html",overwrite=TRUE)

## Note: overwriting existing report

## Dataset saved as " /tmp/RtmpXoIrRA/mitch1_l.rds ".

## 
## 
## processing file: mitch.Rmd

## 1/34                             
## 2/34 [checklibraries]            
## 3/34                             
## 4/34 [peek]                      
## 5/34                             
## 6/34 [metrics]                   
## 7/34                             
## 8/34 [scatterplot]

## 9/34                             
## 10/34 [contourplot]               
## 11/34                             
## 12/34 [input_geneset_metrics1]    
## 13/34                             
## 14/34 [input_geneset_metrics2]

## 15/34                             
## 16/34 [input_geneset_metrics3]

## 17/34                             
## 18/34 [echart1d]                  
## 19/34 [echart2d]                  
## 20/34                             
## 21/34 [heatmap]                   
## 22/34                             
## 23/34 [effectsize]                
## 24/34                             
## 25/34 [results_table]             
## 26/34                             
## 27/34 [results_table_complete]    
## 28/34                             
## 29/34 [detailed_geneset_reports1d]

## 30/34                             
## 31/34 [detailed_geneset_reports2d]
## 32/34                             
## 33/34 [session_info]              
## 34/34

## output file: /home/mark.ziemann@domain.internal.burnet.edu.au/projects/lung_brain/mitch.knit.md

## /usr/bin/pandoc +RTS -K512m -RTS /home/mark.ziemann@domain.internal.burnet.edu.au/projects/lung_brain/mitch.knit.md --to html4 --from markdown+autolink_bare_uris+tex_math_single_backslash --output /tmp/RtmpXoIrRA/mitch_report.html --lua-filter /usr/local/lib/R/site-library/rmarkdown/rmarkdown/lua/pagebreak.lua --lua-filter /usr/local/lib/R/site-library/rmarkdown/rmarkdown/lua/latex-div.lua --self-contained --variable bs3=TRUE --section-divs --template /usr/local/lib/R/site-library/rmarkdown/rmd/h/default.html --no-highlight --variable highlightjs=1 --variable theme=bootstrap --mathjax --variable 'mathjax-url=https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML' --include-in-header /tmp/RtmpXoIrRA/rmarkdown-str2d120558663da5.html

## 
## Output created: /tmp/RtmpXoIrRA/mitch_report.html

## [1] TRUE

write.table(mres1_l$enrichment_result,file="mitch1_l.tsv",quote=FALSE,sep="\t",row.names=FALSE)

m1_l_top_up <- head(subset(m1_l_top,s.dist>0),10)[,"s.dist"]
names(m1_l_top_up) <- head(subset(m1_l_top,s.dist>0),10)[,"set"]
m1_l_top_dn <- head(subset(m1_l_top,s.dist<0),10)[,"s.dist"]
names(m1_l_top_dn) <- head(subset(m1_l_top,s.dist<0),10)[,"set"]
m1_l_top_updn <- c(m1_l_top_up,m1_l_top_dn)
m1_l_top_updn <- m1_l_top_updn[order(m1_l_top_updn)]

par(mar=c(5,25,3,1))
barplot(m1_l_top_updn,horiz=TRUE,las=1,col="darkgray",
  xlab="Enrichment score",cex.names=0.8,xlim=c(-1,1),
  main="Effect of LPS in hyp")
grid()

pdf("plot18_mitch1lps01.pdf")
par(mar=c(5,25,3,1))
barplot(m1_l_top_updn,horiz=TRUE,las=1,col="darkgray",
  xlab="Enrichment score",cex.names=0.8,xlim=c(-1,1),
  main="Effect of LPS in hyp")
grid()
dev.off()

## X11cairo 
##        2

Mitch for OVA in hyp

m1_o <- mitch_import(dge1_o, DEtype="deseq2",geneTable=gt)

## The input is a single dataframe; one contrast only. Converting
##         it to a list for you.

## Note: Mean no. genes in input = 17114

## Note: no. genes in output = 17098

## Note: estimated proportion of input genes in output = 0.999

mres1_o <- mitch_calc(m1_o, genesets, priority="effect")

## Note: Enrichments with large effect sizes may not be
##             statistically significant.

head(mres1_o$enrichment_result,20) %>%
  kbl(caption = "Top gene pathway differences in hypothalamus for OVA") %>%
  kable_paper("hover", full_width = F)

Top gene pathway differences in hypothalamus for OVA

	set	setSize	pANOVA	s.dist	p.adjustANOVA
304	EUKARYOTIC TRANSLATION ELONGATION	87	0.0000000	0.7530699	0.0000000
1011	SRP DEPENDENT COTRANSLATIONAL PROTEIN TARGETING TO MEMBRANE	106	0.0000000	0.6729904	0.0000000
838	RESPONSE OF EIF2AK4 GCN2 TO AMINO ACID DEFICIENCY	95	0.0000000	0.6490378	0.0000000
305	EUKARYOTIC TRANSLATION INITIATION	114	0.0000000	0.6267498	0.0000000
790	REGULATION OF COMMISSURAL AXON PATHFINDING BY SLIT AND ROBO	10	0.0006045	-0.6262757	0.0066551
1175	YAP1 AND WWTR1 TAZ STIMULATED GENE EXPRESSION	11	0.0004322	0.6128157	0.0050408
28	ACTIVATION OF THE MRNA UPON BINDING OF THE CAP BINDING COMPLEX AND EIFS AND SUBSEQUENT BINDING TO 43S	59	0.0000000	0.5830522	0.0000000
912	SELENOAMINO ACID METABOLISM	109	0.0000000	0.5768390	0.0000000
27	ACTIVATION OF THE AP 1 FAMILY OF TRANSCRIPTION FACTORS	10	0.0024467	-0.5533006	0.0208860
328	FORMATION OF ATP BY CHEMIOSMOTIC COUPLING	18	0.0000761	0.5385896	0.0013381
631	NONSENSE MEDIATED DECAY NMD	109	0.0000000	0.5243355	0.0000000
171	COMPLEX I BIOGENESIS	56	0.0000000	0.5181778	0.0000000
157	CLASS C 3 METABOTROPIC GLUTAMATE PHEROMONE RECEPTORS	12	0.0019736	-0.5158414	0.0174809
923	SEROTONIN NEUROTRANSMITTER RELEASE CYCLE	18	0.0001951	-0.5071494	0.0028248
8	ACETYLCHOLINE BINDING AND DOWNSTREAM EVENTS	10	0.0061111	-0.5007022	0.0449932
419	HIGHLY CALCIUM PERMEABLE POSTSYNAPTIC NICOTINIC ACETYLCHOLINE RECEPTORS	10	0.0061111	-0.5007022	0.0449932
283	ENDOSOMAL VACUOLAR PATHWAY	11	0.0048329	0.4906814	0.0377032
1049	SYNTHESIS OF PROSTAGLANDINS PG AND THROMBOXANES TX	13	0.0031618	0.4727809	0.0265201
791	REGULATION OF EXPRESSION OF SLITS AND ROBOS	160	0.0000000	0.4703396	0.0000000
836	RESPIRATORY ELECTRON TRANSPORT ATP SYNTHESIS BY CHEMIOSMOTIC COUPLING AND HEAT PRODUCTION BY UNCOUPLING PROTEINS	125	0.0000000	0.4603881	0.0000000

m1_o_top <- subset(mres1_o$enrichment_result,p.adjustANOVA<0.05)
m1_o_up <- subset(m1_o_top,s.dist>0)$set
m1_o_dn <- subset(m1_o_top,s.dist<0)$set
mitch_report(mres1_o,outfile="mitch1_o.html",overwrite=TRUE)

## Note: overwriting existing report

## Dataset saved as " /tmp/RtmpXoIrRA/mitch1_o.rds ".

## 
## 
## processing file: mitch.Rmd

## 1/34                             
## 2/34 [checklibraries]            
## 3/34                             
## 4/34 [peek]                      
## 5/34                             
## 6/34 [metrics]                   
## 7/34                             
## 8/34 [scatterplot]

## 9/34                             
## 10/34 [contourplot]               
## 11/34                             
## 12/34 [input_geneset_metrics1]    
## 13/34                             
## 14/34 [input_geneset_metrics2]

## 15/34                             
## 16/34 [input_geneset_metrics3]

## 17/34                             
## 18/34 [echart1d]                  
## 19/34 [echart2d]                  
## 20/34                             
## 21/34 [heatmap]                   
## 22/34                             
## 23/34 [effectsize]                
## 24/34                             
## 25/34 [results_table]             
## 26/34                             
## 27/34 [results_table_complete]    
## 28/34                             
## 29/34 [detailed_geneset_reports1d]

## 30/34                             
## 31/34 [detailed_geneset_reports2d]
## 32/34                             
## 33/34 [session_info]              
## 34/34

## output file: /home/mark.ziemann@domain.internal.burnet.edu.au/projects/lung_brain/mitch.knit.md

## /usr/bin/pandoc +RTS -K512m -RTS /home/mark.ziemann@domain.internal.burnet.edu.au/projects/lung_brain/mitch.knit.md --to html4 --from markdown+autolink_bare_uris+tex_math_single_backslash --output /tmp/RtmpXoIrRA/mitch_report.html --lua-filter /usr/local/lib/R/site-library/rmarkdown/rmarkdown/lua/pagebreak.lua --lua-filter /usr/local/lib/R/site-library/rmarkdown/rmarkdown/lua/latex-div.lua --self-contained --variable bs3=TRUE --section-divs --template /usr/local/lib/R/site-library/rmarkdown/rmd/h/default.html --no-highlight --variable highlightjs=1 --variable theme=bootstrap --mathjax --variable 'mathjax-url=https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML' --include-in-header /tmp/RtmpXoIrRA/rmarkdown-str2d1205566a47c8.html

## 
## Output created: /tmp/RtmpXoIrRA/mitch_report.html

## [1] TRUE

write.table(mres1_o$enrichment_result,file="mitch1_o.tsv",quote=FALSE,sep="\t",row.names=FALSE)

m1_o_top_up <- head(subset(m1_o_top,s.dist>0),10)[,"s.dist"]
names(m1_o_top_up) <- head(subset(m1_o_top,s.dist>0),10)[,"set"]
m1_o_top_dn <- head(subset(m1_o_top,s.dist<0),10)[,"s.dist"]
names(m1_o_top_dn) <- head(subset(m1_o_top,s.dist<0),10)[,"set"]
m1_o_top_updn <- c(m1_o_top_up,m1_o_top_dn)
m1_o_top_updn <- m1_o_top_updn[order(m1_o_top_updn)]

par(mar=c(5,25,3,1))
barplot(m1_o_top_updn,horiz=TRUE,las=1,col="darkgray",
  xlab="Enrichment score",cex.names=0.8,xlim=c(-1,1),
  main="Effect of LPS in hyp")
grid()

pdf("plot19_mitch1ova01.pdf")
par(mar=c(5,25,3,1))
barplot(m1_o_top_updn,horiz=TRUE,las=1,col="darkgray",
  xlab="Enrichment score",cex.names=0.8,xlim=c(-1,1),
  main="Effect of LPS in hyp")
grid()
dev.off()

## X11cairo 
##        2

m1_l_up <- subset(m1_l_top,s.dist>0)$set
m1_l_dn <- subset(m1_l_top,s.dist<0)$set
m1_o_up <- subset(m1_o_top,s.dist>0)$set
m1_o_dn <- subset(m1_o_top,s.dist<0)$set

par(mar=c(2,2,2,2))

v0 <- list("LPS up"=m1_l_up,
  "LPS dn"=m1_l_dn,
  "OVA up"=m1_o_up,
  "OVA dn"=m1_o_dn)
plot(euler(v0),quantities = TRUE, edges = "gray", main="effect of treatments on hyp")

pdf("plot20_mitch1lpsovaeuler01.pdf")
plot(euler(v0),quantities = TRUE, edges = "gray", main="effect of treatments on hyp")
dev.off()

## X11cairo 
##        2

Two dimensional mitch analysis in hyp.

ml <- list("LPS"=dge1_l,"OVA"=dge1_o)
m1 <- mitch_import(ml, DEtype="deseq2",geneTable=gt)

## Note: Mean no. genes in input = 17104.5

## Note: no. genes in output = 16956

## Note: estimated proportion of input genes in output = 0.991

head(m1)

##                      LPS         OVA
## 0610009B22Rik -0.3304181  0.09722066
## 0610009E02Rik  0.7596333  1.26326253
## 0610009L18Rik -1.2521412 -0.66374969
## 0610010K14Rik  0.4193627 -0.45368981
## 0610012G03Rik  1.4462155  0.12236243
## 0610030E20Rik -0.2426628 -0.04562294

mres1 <- mitch_calc(m1, genesets, priority="effect")

## Note: Enrichments with large effect sizes may not be 
##             statistically significant.

head(mres1$enrichment_result,20) %>%
  kbl(caption = "Top gene pathway differences in hypothalamus for LPS and OVA") %>%
  kable_paper("hover", full_width = F)

Top gene pathway differences in hypothalamus for LPS and OVA

	set	setSize	pMANOVA	s.LPS	s.OVA	p.LPS	p.OVA	s.dist	SD	p.adjustMANOVA
304	EUKARYOTIC TRANSLATION ELONGATION	87	0.0000000	0.3005016	0.7571632	0.0000013	0.0000000	0.8146148	0.3229085	0.0000000
1008	SRP DEPENDENT COTRANSLATIONAL PROTEIN TARGETING TO MEMBRANE	106	0.0000000	0.2541257	0.6771379	0.0000062	0.0000000	0.7232535	0.2991148	0.0000000
837	RESPONSE OF EIF2AK4 GCN2 TO AMINO ACID DEFICIENCY	95	0.0000000	0.2223137	0.6530942	0.0001818	0.0000000	0.6898952	0.3046078	0.0000000
789	REGULATION OF COMMISSURAL AXON PATHFINDING BY SLIT AND ROBO	10	0.0029071	-0.2596719	-0.6241001	0.1550844	0.0006316	0.6759663	0.2576896	0.0208948
305	EUKARYOTIC TRANSLATION INITIATION	114	0.0000000	0.2044179	0.6309008	0.0001645	0.0000000	0.6631912	0.3015689	0.0000000
783	REDUCTION OF CYTOSOLIC CA LEVELS	11	0.0047378	-0.5570697	-0.3357011	0.0013775	0.0538808	0.6504013	0.1565312	0.0307566
1172	YAP1 AND WWTR1 TAZ STIMULATED GENE EXPRESSION	11	0.0015116	0.1440006	0.6164919	0.4082939	0.0003990	0.6330864	0.3341017	0.0126863
724	POST CHAPERONIN TUBULIN FOLDING PATHWAY	17	0.0000053	0.6289245	-0.0037748	0.0000071	0.9785061	0.6289359	0.4473860	0.0001302
910	SELENOAMINO ACID METABOLISM	109	0.0000000	0.2407970	0.5807448	0.0000142	0.0000000	0.6286873	0.2403794	0.0000000
337	FORMATION OF TUBULIN FOLDING INTERMEDIATES BY CCT TRIC	19	0.0000642	0.5807870	0.1993860	0.0000117	0.1324715	0.6140589	0.2696913	0.0010059
28	ACTIVATION OF THE MRNA UPON BINDING OF THE CAP BINDING COMPLEX AND EIFS AND SUBSEQUENT BINDING TO 43S	59	0.0000000	0.1620035	0.5870293	0.0314327	0.0000000	0.6089733	0.3005386	0.0000000
143	CGMP EFFECTS	15	0.0008013	-0.5629853	-0.2197627	0.0001597	0.1406164	0.6043576	0.2426950	0.0079791
1135	TRANSPORT OF CONNEXONS TO THE PLASMA MEMBRANE	13	0.0007834	0.5915627	0.1217340	0.0002213	0.4473220	0.6039582	0.3322191	0.0078672
677	P75NTR REGULATES AXONOGENESIS	10	0.0196686	-0.4918683	-0.3298241	0.0070734	0.0709274	0.5922148	0.1145825	0.0917086
27	ACTIVATION OF THE AP 1 FAMILY OF TRANSCRIPTION FACTORS	10	0.0104349	-0.2020772	-0.5511153	0.2685386	0.0025455	0.5869951	0.2468072	0.0581091
618	NEUROTOXICITY OF CLOSTRIDIUM TOXINS	10	0.0247523	-0.3757583	-0.4496990	0.0396398	0.0138005	0.5860235	0.0522840	0.1062004
410	HDR THROUGH MMEJ ALT NHEJ	10	0.0286875	-0.3549982	-0.4486486	0.0519199	0.0140234	0.5721096	0.0662208	0.1182731
283	ENDOSOMAL VACUOLAR PATHWAY	11	0.0001919	-0.2757209	0.4955551	0.1133476	0.0044279	0.5670952	0.5453745	0.0023985
157	CLASS C 3 METABOTROPIC GLUTAMATE PHEROMONE RECEPTORS	12	0.0086329	-0.2343799	-0.5132200	0.1598075	0.0020809	0.5642063	0.1971697	0.0494811
631	NONSENSE MEDIATED DECAY NMD	109	0.0000000	0.1972774	0.5282464	0.0003761	0.0000000	0.5638817	0.2340304	0.0000000

m1_top <- subset(mres1$enrichment_result,p.adjustMANOVA<0.05)
m1_up <- subset(m1_top,s.dist>0)$set
m1_dn <- subset(m1_top,s.dist<0)$set
mitch_report(mres1,outfile="mitch1.html",overwrite=TRUE)

## Note: overwriting existing report

## Dataset saved as " /tmp/RtmpXoIrRA/mitch1.rds ".

## 
## 
## processing file: mitch.Rmd

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## 21/34 [heatmap]

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## output file: /home/mark.ziemann@domain.internal.burnet.edu.au/projects/lung_brain/mitch.knit.md

## /usr/bin/pandoc +RTS -K512m -RTS /home/mark.ziemann@domain.internal.burnet.edu.au/projects/lung_brain/mitch.knit.md --to html4 --from markdown+autolink_bare_uris+tex_math_single_backslash --output /tmp/RtmpXoIrRA/mitch_report.html --lua-filter /usr/local/lib/R/site-library/rmarkdown/rmarkdown/lua/pagebreak.lua --lua-filter /usr/local/lib/R/site-library/rmarkdown/rmarkdown/lua/latex-div.lua --self-contained --variable bs3=TRUE --section-divs --template /usr/local/lib/R/site-library/rmarkdown/rmd/h/default.html --no-highlight --variable highlightjs=1 --variable theme=bootstrap --mathjax --variable 'mathjax-url=https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML' --include-in-header /tmp/RtmpXoIrRA/rmarkdown-str2d120544767d3.html

## 
## Output created: /tmp/RtmpXoIrRA/mitch_report.html

## [1] TRUE

write.table(mres1$enrichment_result,file="mitch1.tsv",quote=FALSE,sep="\t",row.names=FALSE)

m1_top_up <- head(subset(m1_top,s.dist>0),10)[,"s.dist"]
names(m1_top_up) <- head(subset(m1_top,s.dist>0),10)[,"set"]
m1_top_dn <- head(subset(m1_top,s.dist<0),10)[,"s.dist"]
names(m1_top_dn) <- head(subset(m1_top,s.dist<0),10)[,"set"]
m1_top_updn <- c(m1_top_up,m1_top_dn)
m1_top_updn <- m1_top_updn[order(m1_top_updn)]

Need to make a scatterplot to highlight the following:

• Serotonin Neurotransmitter Release Cycle (DN)
• Class C3 metabotropic glutamate pheromone receptors (DN)
• Regulation of commissural axon pathfinding by SLIT AND robo (DN)
• Reduction of Cytosolic Ca (DN)
• YAP1 and WWTR1 TAZ STIMULATED GENE EXPRESSION (UP)
• Activation of the mRNA upon binding (UP)
• Regulation of expression of slits and robs (UP)

par(mar=c(4,4,2,1))

a <- mres1$enrichment_result
plot(a$s.LPS , a$s.OVA,pch=19,col="gray")
abline(h=0,lty=2)
abline(v=0,lty=2)
as <- subset(a,p.adjustMANOVA<0.05)
points(as$s.LPS , as$s.OVA,pch=19,col="red")

abline(h=0.2,lty=2,col="red")
abline(v=0.2,lty=2,col="red")
abline(h=-0.2,lty=2,col="red")
abline(v=-0.2,lty=2,col="red")

mysets <- c("SEROTONIN NEUROTRANSMITTER RELEASE CYCLE",
  "CLASS C 3 METABOTROPIC GLUTAMATE PHEROMONE RECEPTORS",
  "REGULATION OF COMMISSURAL AXON PATHFINDING BY SLIT AND ROBO",
  "REDUCTION OF CYTOSOLIC CA LEVELS",
  "YAP1 AND WWTR1 TAZ STIMULATED GENE EXPRESSION",
  "ACTIVATION OF THE MRNA UPON BINDING OF THE CAP BINDING COMPLEX AND EIFS AND SUBSEQUENT BINDING TO 43S",
  "REGULATION OF EXPRESSION OF SLITS AND ROBOS")

a2 <- a[which(a$set %in% mysets),]

points(a2$s.LPS , a2$s.OVA+0.025,pch=25,col="red")

pdf("plot21_mitch1_scatter01.pdf")

a <- mres1$enrichment_result
plot(a$s.LPS , a$s.OVA,pch=19,col="gray")
abline(h=0,lty=2)
abline(v=0,lty=2)
as <- subset(a,p.adjustMANOVA<0.05)
points(as$s.LPS , as$s.OVA,pch=19,col="red")

abline(h=0.2,lty=2,col="red")
abline(v=0.2,lty=2,col="red")
abline(h=-0.2,lty=2,col="red")
abline(v=-0.2,lty=2,col="red")

mysets <- c("SEROTONIN NEUROTRANSMITTER RELEASE CYCLE",
  "CLASS C 3 METABOTROPIC GLUTAMATE PHEROMONE RECEPTORS",
  "REGULATION OF COMMISSURAL AXON PATHFINDING BY SLIT AND ROBO",
  "REDUCTION OF CYTOSOLIC CA LEVELS",
  "YAP1 AND WWTR1 TAZ STIMULATED GENE EXPRESSION",
  "ACTIVATION OF THE MRNA UPON BINDING OF THE CAP BINDING COMPLEX AND EIFS AND SUBSEQUENT BINDING TO 43S",
  "REGULATION OF EXPRESSION OF SLITS AND ROBOS")

a2 <- a[which(a$set %in% mysets),]

points(a2$s.LPS , a2$s.OVA+0.025,pch=25,col="red")

dev.off()

## X11cairo 
##        2

subset(as,s.LPS>0.2 & s.OVA>0.2)  %>%
  kbl(caption = "Pathways upregulated in LPS and OVA") %>%
  kable_paper("hover", full_width = F)

Pathways upregulated in LPS and OVA

	set	setSize	pMANOVA	s.LPS	s.OVA	p.LPS	p.OVA	s.dist	SD	p.adjustMANOVA
304	EUKARYOTIC TRANSLATION ELONGATION	87	0.0000000	0.3005016	0.7571632	0.0000013	0.0000000	0.8146148	0.3229085	0.0000000
1008	SRP DEPENDENT COTRANSLATIONAL PROTEIN TARGETING TO MEMBRANE	106	0.0000000	0.2541257	0.6771379	0.0000062	0.0000000	0.7232535	0.2991148	0.0000000
837	RESPONSE OF EIF2AK4 GCN2 TO AMINO ACID DEFICIENCY	95	0.0000000	0.2223137	0.6530942	0.0001818	0.0000000	0.6898952	0.3046078	0.0000000
305	EUKARYOTIC TRANSLATION INITIATION	114	0.0000000	0.2044179	0.6309008	0.0001645	0.0000000	0.6631912	0.3015689	0.0000000
910	SELENOAMINO ACID METABOLISM	109	0.0000000	0.2407970	0.5807448	0.0000142	0.0000000	0.6286873	0.2403794	0.0000000
190	CROSS PRESENTATION OF SOLUBLE EXOGENOUS ANTIGENS ENDOSOMES	46	0.0000008	0.3238732	0.4183478	0.0001447	0.0000009	0.5290640	0.0668036	0.0000309
790	REGULATION OF EXPRESSION OF SLITS AND ROBOS	160	0.0000000	0.2161534	0.4744210	0.0000024	0.0000000	0.5213421	0.1826228	0.0000000
1125	TRANSLATION	286	0.0000000	0.2603207	0.4144965	0.0000000	0.0000000	0.4894632	0.1090188	0.0000000
85	AUF1 HNRNP D0 BINDS AND DESTABILIZES MRNA	53	0.0000013	0.3252286	0.3648733	0.0000422	0.0000043	0.4887803	0.0280330	0.0000434
1009	STABILIZATION OF P53	54	0.0000015	0.3124658	0.3662771	0.0000715	0.0000032	0.4814496	0.0380503	0.0000458
569	MITOCHONDRIAL TRANSLATION	93	0.0000000	0.3126021	0.3617636	0.0000002	0.0000000	0.4781139	0.0347624	0.0000000
1069	THE ROLE OF GTSE1 IN G2 M PROGRESSION AFTER G2 CHECKPOINT	66	0.0000001	0.3888889	0.2636005	0.0000000	0.0002135	0.4698082	0.0885923	0.0000040
412	HEDGEHOG LIGAND BIOGENESIS	61	0.0000009	0.3091486	0.3447552	0.0000298	0.0000032	0.4630648	0.0251777	0.0000309
817	REGULATION OF RUNX3 EXPRESSION AND ACTIVITY	53	0.0000061	0.3216879	0.3309907	0.0000511	0.0000307	0.4615603	0.0065781	0.0001425
215	DECTIN 1 MEDIATED NONCANONICAL NF KB SIGNALING	59	0.0000018	0.2991956	0.3466256	0.0000706	0.0000041	0.4578944	0.0335380	0.0000533
221	DEGRADATION OF AXIN	53	0.0000080	0.3016468	0.3407936	0.0001458	0.0000178	0.4551165	0.0276810	0.0001718
609	NEGATIVE REGULATION OF NOTCH4 SIGNALING	54	0.0000067	0.2904916	0.3477498	0.0002226	0.0000099	0.4531173	0.0404876	0.0001481
80	ASYMMETRIC LOCALIZATION OF PCP PROTEINS	62	0.0000020	0.3135452	0.3171655	0.0000196	0.0000157	0.4459871	0.0025599	0.0000580
180	COOPERATION OF PREFOLDIN AND TRIC CCT IN ACTIN AND TUBULIN FOLDING	26	0.0025700	0.3846063	0.2224863	0.0006876	0.0495991	0.4443221	0.1146362	0.0194758
217	DEFECTIVE CFTR CAUSES CYSTIC FIBROSIS	59	0.0000032	0.2687760	0.3531336	0.0003570	0.0000027	0.4437836	0.0596498	0.0000851
550	METABOLISM OF POLYAMINES	57	0.0000065	0.2571449	0.3530833	0.0007872	0.0000040	0.4367966	0.0678387	0.0001470
224	DEGRADATION OF DVL	55	0.0000126	0.2792637	0.3357703	0.0003412	0.0000166	0.4367264	0.0399562	0.0002515
225	DEGRADATION OF GLI1 BY THE PROTEASOME	57	0.0000340	0.2685667	0.3106392	0.0004542	0.0000499	0.4106395	0.0297497	0.0005788
139	CELLULAR RESPONSE TO HYPOXIA	72	0.0000009	0.2095508	0.3530529	0.0021163	0.0000002	0.4105580	0.1014713	0.0000309
907	SCF SKP2 MEDIATED DEGRADATION OF P27 P21	59	0.0000220	0.2476811	0.3249268	0.0010028	0.0000159	0.4085626	0.0546209	0.0004034
798	REGULATION OF HMOX1 EXPRESSION AND ACTIVITY	63	0.0000159	0.2548562	0.3127519	0.0004696	0.0000177	0.4034420	0.0409384	0.0003012
235	DISEASES ASSOCIATED WITH GLYCOSAMINOGLYCAN METABOLISM	40	0.0013469	0.2596240	0.2950993	0.0044999	0.0012418	0.3930499	0.0250848	0.0116093
62	APC C CDH1 MEDIATED DEGRADATION OF CDC20 AND OTHER APC C CDH1 TARGETED PROTEINS IN LATE MITOSIS EARLY G1	70	0.0000101	0.2627316	0.2916482	0.0001447	0.0000246	0.3925386	0.0204471	0.0002055
894	RRNA PROCESSING	194	0.0000000	0.2045188	0.3201775	0.0000009	0.0000000	0.3799231	0.0817830	0.0000000
568	MITOCHONDRIAL PROTEIN IMPORT	63	0.0000657	0.2583629	0.2772774	0.0003916	0.0001416	0.3789910	0.0133746	0.0010158
358	G1 S DNA DAMAGE CHECKPOINTS	65	0.0000450	0.2391888	0.2928578	0.0008561	0.0000446	0.3781230	0.0379497	0.0007443
681	PCP CE PATHWAY	89	0.0000021	0.2723788	0.2532696	0.0000090	0.0000365	0.3719351	0.0135122	0.0000580
63	APC C MEDIATED DEGRADATION OF CELL CYCLE PROTEINS	81	0.0000062	0.2382244	0.2850290	0.0002113	0.0000093	0.3714733	0.0330958	0.0001425
812	REGULATION OF PTEN STABILITY AND ACTIVITY	66	0.0001012	0.2339218	0.2745142	0.0010182	0.0001154	0.3606626	0.0287032	0.0014879
814	REGULATION OF RAS BY GAPS	66	0.0001026	0.2104222	0.2875002	0.0031237	0.0000538	0.3562778	0.0545024	0.0014879
3	ABC TRANSPORTER DISORDERS	70	0.0001017	0.2058273	0.2786687	0.0029134	0.0000556	0.3464407	0.0515067	0.0014879
1082	TNFR2 NON CANONICAL NF KB PATHWAY	79	0.0001123	0.2240192	0.2409983	0.0005795	0.0002140	0.3290362	0.0120061	0.0015519

subset(as,s.LPS<-0.2 & s.OVA<0.2)  %>%
  kbl(caption = "Pathways downregulated in LPS and OVA") %>%
  kable_paper("hover", full_width = F)

Pathways downregulated in LPS and OVA

	set	setSize	pMANOVA	s.LPS	s.OVA	p.LPS	p.OVA	s.dist	SD	p.adjustMANOVA
789	REGULATION OF COMMISSURAL AXON PATHFINDING BY SLIT AND ROBO	10	0.0029071	-0.2596719	-0.6241001	0.1550844	0.0006316	0.6759663	0.2576896	0.0208948
783	REDUCTION OF CYTOSOLIC CA LEVELS	11	0.0047378	-0.5570697	-0.3357011	0.0013775	0.0538808	0.6504013	0.1565312	0.0307566
724	POST CHAPERONIN TUBULIN FOLDING PATHWAY	17	0.0000053	0.6289245	-0.0037748	0.0000071	0.9785061	0.6289359	0.4473860	0.0001302
337	FORMATION OF TUBULIN FOLDING INTERMEDIATES BY CCT TRIC	19	0.0000642	0.5807870	0.1993860	0.0000117	0.1324715	0.6140589	0.2696913	0.0010059
143	CGMP EFFECTS	15	0.0008013	-0.5629853	-0.2197627	0.0001597	0.1406164	0.6043576	0.2426950	0.0079791
1135	TRANSPORT OF CONNEXONS TO THE PLASMA MEMBRANE	13	0.0007834	0.5915627	0.1217340	0.0002213	0.4473220	0.6039582	0.3322191	0.0078672
157	CLASS C 3 METABOTROPIC GLUTAMATE PHEROMONE RECEPTORS	12	0.0086329	-0.2343799	-0.5132200	0.1598075	0.0020809	0.5642063	0.1971697	0.0494811
627	NITRIC OXIDE STIMULATES GUANYLATE CYCLASE	20	0.0003350	-0.5164974	-0.1981105	0.0000636	0.1251300	0.5531883	0.2251335	0.0038218
1167	VOLTAGE GATED POTASSIUM CHANNELS	39	0.0000057	-0.3994268	-0.3608675	0.0000159	0.0000964	0.5383002	0.0272655	0.0001377
921	SEROTONIN NEUROTRANSMITTER RELEASE CYCLE	18	0.0009568	-0.1551803	-0.5046378	0.2544237	0.0002099	0.5279586	0.2471038	0.0090228
908	SEALING OF THE NUCLEAR ENVELOPE NE BY ESCRT III	23	0.0000754	0.5121746	0.1040597	0.0000212	0.3877283	0.5226387	0.2885808	0.0011363
832	RESOLUTION OF D LOOP STRUCTURES THROUGH SYNTHESIS DEPENDENT STRAND ANNEALING SDSA	24	0.0011054	-0.2831325	-0.4170456	0.0163599	0.0004053	0.5040745	0.0946909	0.0099913
690	PHASE 0 RAPID DEPOLARISATION	30	0.0003539	-0.3085746	-0.3865926	0.0034450	0.0002476	0.4946434	0.0551671	0.0039987
1170	WNT5A DEPENDENT INTERNALIZATION OF FZD2 FZD5 AND ROR2	13	0.0083226	0.4713315	0.0516256	0.0032558	0.7472597	0.4741504	0.2967769	0.0486518
368	GAP JUNCTION ASSEMBLY	21	0.0018563	0.4361804	0.0880481	0.0005396	0.4849389	0.4449784	0.2461666	0.0149397
306	EXPORT OF VIRAL RIBONUCLEOPROTEINS FROM NUCLEUS	31	0.0004774	-0.1683804	-0.4058303	0.1047476	0.0000920	0.4393748	0.1679024	0.0051465
498	IRE1ALPHA ACTIVATES CHAPERONES	50	0.0000021	0.4158926	0.1340140	0.0000004	0.1012442	0.4369512	0.1993183	0.0000585
796	REGULATION OF GLUCOKINASE BY GLUCOKINASE REGULATORY PROTEIN	29	0.0007187	-0.1400680	-0.4072355	0.1917954	0.0001472	0.4306505	0.1889160	0.0074079
1022	SUMOYLATION OF RNA BINDING PROTEINS	45	0.0000622	-0.2136584	-0.3731470	0.0131666	0.0000149	0.4299868	0.1127754	0.0009878
650	NUCLEAR PORE COMPLEX NPC DISASSEMBLY	32	0.0008181	-0.1919353	-0.3831319	0.0602776	0.0001762	0.4285198	0.1351965	0.0080777
797	REGULATION OF GLYCOLYSIS BY FRUCTOSE 2 6 BISPHOSPHATE METABOLISM	11	0.0086111	0.2012983	-0.3727085	0.2477085	0.0323273	0.4235949	0.4058841	0.0494811
1023	SUMOYLATION OF SUMOYLATION PROTEINS	33	0.0008405	-0.1951244	-0.3757357	0.0524359	0.0001874	0.4233803	0.1277115	0.0080946
645	NS1 MEDIATED EFFECTS ON HOST PATHWAYS	37	0.0005468	-0.2250325	-0.3578497	0.0178702	0.0001655	0.4227245	0.0939160	0.0057882
261	DOWNREGULATION OF SMAD2 3 SMAD4 TRANSCRIPTIONAL ACTIVITY	23	0.0079424	-0.3708709	-0.1992482	0.0020784	0.0981475	0.4210048	0.1213556	0.0468962
112	CARBOXYTERMINAL POST TRANSLATIONAL MODIFICATIONS OF TUBULIN	36	0.0000011	0.3966476	-0.1229249	0.0000381	0.2019370	0.4152588	0.3673932	0.0000372
487	INTRAFLAGELLAR TRANSPORT	51	0.0000097	0.3864607	0.1171332	0.0000018	0.1480047	0.4038218	0.1904433	0.0001997
649	NUCLEAR IMPORT OF REV PROTEIN	32	0.0013536	-0.1562906	-0.3712184	0.1260604	0.0002789	0.4027777	0.1519769	0.0116093
461	INTERACTIONS OF VPR WITH HOST CELLULAR PROTEINS	34	0.0010912	-0.1498189	-0.3660220	0.1306564	0.0002213	0.3954969	0.1528787	0.0099395
43	AGGREPHAGY	35	0.0000703	0.3944836	0.0110243	0.0000537	0.9101567	0.3946376	0.2711466	0.0010735
460	INTERACTIONS OF REV WITH HOST CELLULAR PROTEINS	35	0.0017261	-0.1436170	-0.3484276	0.1415469	0.0003610	0.3768655	0.1448230	0.0140845
204	CYTOSOLIC TRNA AMINOACYLATION	24	0.0004582	0.3516074	-0.1311225	0.0028680	0.2662404	0.3752611	0.3413416	0.0050314
506	KERATAN SULFATE KERATIN METABOLISM	30	0.0081298	0.3202332	0.1921068	0.0024014	0.0686331	0.3734358	0.0905990	0.0477628
1137	TRANSPORT OF MATURE MRNAS DERIVED FROM INTRONLESS TRANSCRIPTS	41	0.0003045	-0.0901480	-0.3580845	0.3180266	0.0000727	0.3692576	0.1894597	0.0035422
854	RHO GTPASES ACTIVATE WASPS AND WAVES	36	0.0001425	-0.3676123	0.0062812	0.0001352	0.9480132	0.3676660	0.2643826	0.0018815
728	POTASSIUM CHANNELS	90	0.0000037	-0.2507952	-0.2607336	0.0000394	0.0000192	0.3617737	0.0070276	0.0000970
1026	SUMOYLATION OF UBIQUITINYLATION PROTEINS	37	0.0029377	-0.1579433	-0.3231502	0.0964797	0.0006709	0.3596834	0.1168189	0.0209197
1016	SUMOYLATION OF CHROMATIN ORGANIZATION PROTEINS	58	0.0002329	-0.1873064	-0.3025190	0.0136421	0.0000677	0.3558109	0.0814676	0.0028504
1140	TRANSPORT OF THE SLBP DEPENDANT MATURE MRNA	34	0.0050458	-0.1398632	-0.3222015	0.1582242	0.0011497	0.3512485	0.1289327	0.0323979
205	DAG AND IP3 SIGNALING	40	0.0069085	-0.2447328	-0.2387887	0.0074100	0.0089808	0.3419272	0.0042031	0.0416282
1017	SUMOYLATION OF DNA DAMAGE RESPONSE AND REPAIR PROTEINS	73	0.0001894	-0.2199091	-0.2483466	0.0011638	0.0002447	0.3317168	0.0201083	0.0023930
416	HEME SIGNALING	45	0.0032838	-0.2904500	-0.1401152	0.0007502	0.1039978	0.3224802	0.1063027	0.0232440
113	CARDIAC CONDUCTION	109	0.0000193	-0.2203060	-0.2130807	0.0000714	0.0001224	0.3064933	0.0051091	0.0003605
413	HEDGEHOG OFF STATE	106	0.0000077	0.2711707	0.1390594	0.0000014	0.0134336	0.3047476	0.0934168	0.0001665
432	HSP90 CHAPERONE CYCLE FOR STEROID HORMONE RECEPTORS SHR	48	0.0001255	0.3000552	-0.0488083	0.0003232	0.5586625	0.3039990	0.2466837	0.0016952
1147	TRNA AMINOACYLATION	42	0.0011312	0.3033525	0.0082997	0.0006708	0.9258706	0.3034661	0.2086339	0.0101460
356	G PROTEIN MEDIATED EVENTS	52	0.0048812	-0.2501729	-0.1717140	0.0018080	0.0322415	0.3034340	0.0554788	0.0315130
615	NETRIN 1 SIGNALING	49	0.0053213	-0.1340165	-0.2657769	0.1047055	0.0012910	0.2976538	0.0931687	0.0337703
390	GLYCOLYSIS	66	0.0000014	0.1209143	-0.2700827	0.0894988	0.0001485	0.2959137	0.2764766	0.0000434
1113	TRANSCRIPTIONAL REGULATION BY MECP2	59	0.0016695	-0.2682223	-0.0876527	0.0003671	0.2444037	0.2821812	0.1276820	0.0138148
679	PARASITE INFECTION	55	0.0004304	-0.2777985	0.0061761	0.0003666	0.9368747	0.2778672	0.2008004	0.0048167
840	RET SIGNALING	37	0.0077383	-0.0172795	-0.2771792	0.8557095	0.0035316	0.2777173	0.1837768	0.0459218
596	NCAM1 INTERACTIONS	41	0.0029164	0.0274500	-0.2694174	0.7610935	0.0028400	0.2708122	0.2099170	0.0208948
380	GLUCOSE METABOLISM	80	0.0000008	0.1365386	-0.2322159	0.0348382	0.0003314	0.2693826	0.2607488	0.0000296
60	ANTIVIRAL MECHANISM BY IFN STIMULATED GENES	73	0.0036468	-0.2027305	-0.1756091	0.0027548	0.0095076	0.2682130	0.0191777	0.0255057
463	INTERFERON ALPHA BETA SIGNALING	55	0.0001042	-0.1834910	0.1800378	0.0186137	0.0209500	0.2570653	0.2570537	0.0014892
1155	UNFOLDED PROTEIN RESPONSE UPR	86	0.0002531	0.2495692	0.0585545	0.0000636	0.3482038	0.2563463	0.1350678	0.0030344
166	COLLAGEN CHAIN TRIMERIZATION	40	0.0022161	0.1117167	-0.2279262	0.2215988	0.0126346	0.2538326	0.2401638	0.0169438
109	CA2 PATHWAY	57	0.0002600	-0.2337032	0.0925301	0.0022799	0.2270896	0.2513544	0.2306818	0.0030648
617	NEURONAL SYSTEM	368	0.0000000	-0.1545673	-0.1970436	0.0000004	0.0000000	0.2504341	0.0300353	0.0000000
702	PI METABOLISM	79	0.0062249	-0.1601198	-0.1857408	0.0139208	0.0043321	0.2452305	0.0181168	0.0382944
959	SIGNALING BY HEDGEHOG	141	0.0000457	0.2167682	0.1112385	0.0000090	0.0227080	0.2436441	0.0746208	0.0007466
86	AURKA ACTIVATION BY TPX2	71	0.0008368	-0.0110883	-0.2403592	0.8717178	0.0004636	0.2406148	0.1621190	0.0080946
182	COPI INDEPENDENT GOLGI TO ER RETROGRADE TRAFFIC	44	0.0051310	0.2334195	-0.0510960	0.0073977	0.5577099	0.2389466	0.2011829	0.0327656
464	INTERFERON GAMMA SIGNALING	77	0.0000357	-0.2175923	0.0978846	0.0009672	0.1377361	0.2385955	0.2230759	0.0005991
165	COLLAGEN BIOSYNTHESIS AND MODIFYING ENZYMES	62	0.0006506	0.2261253	-0.0608634	0.0020805	0.4073908	0.2341730	0.2029317	0.0067655
748	PROTEIN FOLDING	89	0.0060296	0.1738139	0.1538347	0.0046141	0.0121719	0.2321129	0.0141274	0.0376850
243	DISEASES OF METABOLISM	197	0.0000086	0.1906160	0.1322132	0.0000041	0.0013950	0.2319801	0.0412970	0.0001808
909	SELECTIVE AUTOPHAGY	72	0.0046856	0.2174330	0.0420665	0.0014274	0.5373059	0.2214649	0.1240029	0.0307566
183	COPI MEDIATED ANTEROGRADE TRANSPORT	91	0.0001194	0.2117481	-0.0482376	0.0004837	0.4267162	0.2171730	0.1838377	0.0016320
780	RECRUITMENT OF MITOTIC CENTROSOME PROTEINS AND COMPLEXES	78	0.0011959	0.0072359	-0.2164736	0.9120719	0.0009529	0.2165945	0.1581865	0.0106457
595	NCAM SIGNALING FOR NEURITE OUT GROWTH	61	0.0065049	0.0001213	-0.2145838	0.9986933	0.0037584	0.2145838	0.1518194	0.0396024
466	INTERLEUKIN 1 FAMILY SIGNALING	121	0.0021357	0.1127801	0.1795202	0.0322695	0.0006534	0.2120068	0.0471924	0.0165096
107	C TYPE LECTIN RECEPTORS CLRS	111	0.0012511	0.0616618	0.1998331	0.2620870	0.0002779	0.2091303	0.0977019	0.0110525
781	RECRUITMENT OF NUMA TO MITOTIC CENTROSOMES	85	0.0002316	0.0784995	-0.1913893	0.2111802	0.0022978	0.2068624	0.1908402	0.0028504
255	DNA REPLICATION	121	0.0043012	0.1192267	0.1641391	0.0236074	0.0018313	0.2028710	0.0317578	0.0285531
465	INTERFERON SIGNALING	164	0.0000152	-0.2019378	-0.0193675	0.0000083	0.6690301	0.2028644	0.1290967	0.0002930
1133	TRANSMISSION ACROSS CHEMICAL SYNAPSES	240	0.0000223	-0.1182036	-0.1643485	0.0016359	0.0000119	0.2024414	0.0326294	0.0004034
338	FOXO MEDIATED TRANSCRIPTION	58	0.0025987	-0.1543623	0.1305113	0.0420862	0.0856940	0.2021408	0.2014360	0.0194758
308	EXTRA NUCLEAR ESTROGEN SIGNALING	67	0.0020367	-0.1873524	0.0732094	0.0080253	0.3002912	0.2011480	0.1842450	0.0161699
619	NEUROTRANSMITTER RECEPTORS AND POSTSYNAPTIC SIGNAL TRANSMISSION	179	0.0004401	-0.1137654	-0.1623568	0.0087304	0.0001819	0.1982481	0.0343593	0.0048788
181	COPI DEPENDENT GOLGI TO ER RETROGRADE TRAFFIC	86	0.0040545	0.1951889	0.0161274	0.0017633	0.7961226	0.1958541	0.1266156	0.0272230
1115	TRANSCRIPTIONAL REGULATION BY RUNX2	108	0.0038546	0.0631045	0.1853204	0.2575519	0.0008828	0.1957698	0.0864197	0.0261112
168	COLLAGEN FORMATION	79	0.0017144	0.1901614	-0.0445149	0.0034911	0.4941933	0.1953022	0.1659412	0.0140845
992	SIGNALING BY VEGF	103	0.0071549	-0.1793320	-0.0751760	0.0016722	0.1877076	0.1944515	0.0736494	0.0428930
391	GLYCOSAMINOGLYCAN METABOLISM	114	0.0084254	0.1615781	0.1067496	0.0028994	0.0491295	0.1936568	0.0387696	0.0490092
588	MUSCLE CONTRACTION	162	0.0016629	-0.1492920	-0.1206405	0.0010539	0.0081230	0.1919433	0.0202597	0.0138148
810	REGULATION OF PLK1 ACTIVITY AT G2 M TRANSITION	85	0.0026189	0.0171676	-0.1900923	0.7845170	0.0024610	0.1908659	0.1465549	0.0194758
621	NEUTROPHIL DEGRANULATION	385	0.0000000	0.0636419	0.1754163	0.0325021	0.0000000	0.1866043	0.0790364	0.0000013
87	AUTOPHAGY	139	0.0017778	0.1721712	0.0416053	0.0004622	0.3975186	0.1771268	0.0923240	0.0144062
977	SIGNALING BY NTRKS	130	0.0037245	-0.0482193	-0.1685222	0.3428543	0.0009146	0.1752850	0.0850669	0.0258953
141	CELLULAR RESPONSES TO EXTERNAL STIMULI	603	0.0000000	0.0875495	0.1500045	0.0002549	0.0000000	0.1736843	0.0441624	0.0000001
572	MITOTIC G1 PHASE AND G1 S TRANSITION	140	0.0047121	0.0648838	0.1603779	0.1854507	0.0010624	0.1730057	0.0675245	0.0307566
53	ANCHORING OF THE BASAL BODY TO THE PLASMA MEMBRANE	96	0.0014390	0.0634230	-0.1606230	0.2831757	0.0065632	0.1726912	0.1584245	0.0122527
152	CILIUM ASSEMBLY	191	0.0000140	0.1608409	-0.0409220	0.0001287	0.3300682	0.1659651	0.1426679	0.0002736
770	RAC3 GTPASE CYCLE	89	0.0053458	-0.1600566	0.0420134	0.0090929	0.4935615	0.1654788	0.1428851	0.0337703
552	METABOLISM OF RNA	644	0.0000000	0.1093603	0.1198438	0.0000024	0.0000002	0.1622412	0.0074129	0.0000013
902	S PHASE	153	0.0061011	0.0515901	0.1493870	0.2712351	0.0014411	0.1580444	0.0691528	0.0379035
396	GOLGI TO ER RETROGRADE TRANSPORT	119	0.0045286	0.1547517	-0.0106224	0.0035704	0.8414935	0.1551159	0.1169371	0.0298935
768	RAC1 GTPASE CYCLE	177	0.0038275	-0.1452114	-0.0502656	0.0008717	0.2492517	0.1536652	0.0671368	0.0261112
543	METABOLISM OF CARBOHYDRATES	258	0.0000264	0.1513722	-0.0012089	0.0000292	0.9733771	0.1513770	0.1078911	0.0004704
293	ER TO GOLGI ANTEROGRADE TRANSPORT	142	0.0013200	0.1385172	-0.0444725	0.0044120	0.3607199	0.1454813	0.1293933	0.0115745
226	DEGRADATION OF THE EXTRACELLULAR MATRIX	107	0.0068537	0.0524289	-0.1328230	0.3491111	0.0176815	0.1427961	0.1309928	0.0415109
575	MITOTIC PROMETAPHASE	177	0.0040863	-0.0188280	-0.1386846	0.6660604	0.0014764	0.1399568	0.0847514	0.0272809
441	INFECTIOUS DISEASE	732	0.0000001	0.0472228	0.1224633	0.0304164	0.0000000	0.1312526	0.0532031	0.0000059
1142	TRANSPORT TO THE GOLGI AND SUBSEQUENT MODIFICATION	171	0.0014987	0.1204825	-0.0471331	0.0066242	0.2881978	0.1293737	0.1185221	0.0126685
450	INNATE IMMUNE SYSTEM	778	0.0000001	0.0112042	0.1128776	0.5970186	0.0000001	0.1134323	0.0718939	0.0000059
72	ASPARAGINE N LINKED GLYCOSYLATION	285	0.0026449	0.1084099	-0.0002835	0.0016699	0.9934421	0.1084103	0.0768579	0.0195458
199	CYTOKINE SIGNALING IN IMMUNE SYSTEM	539	0.0001108	-0.0682286	0.0485029	0.0069394	0.0549704	0.0837119	0.0825417	0.0015502
878	RNA POLYMERASE II TRANSCRIPTION	1087	0.0000543	-0.0795033	-0.0226566	0.0000112	0.2107206	0.0826686	0.0401967	0.0008732
547	METABOLISM OF LIPIDS	613	0.0028834	0.0090432	0.0774132	0.7034241	0.0011162	0.0779396	0.0483449	0.0208948
1162	VESICLE MEDIATED TRANSPORT	611	0.0064496	0.0293378	-0.0516475	0.2175177	0.0299313	0.0593984	0.0572653	0.0394702

# heatmap
mr1 <- head(subset(mres1$enrichment_result,p.adjustMANOVA<0.05),30)
mr1 <- as.matrix(mr1[,4:5])
rownames(mr1) <- head(mres1$enrichment_result,30)$set

colfunc <- colorRampPalette(c("blue", "white", "red"))

par(mar=c(2,2,2,2))
heatmap.2(mr1,trace="none",col=colfunc(25),scale="none", margins = c(10,28), cexRow=0.7,
  main="Top ranked pathways in hypothalamus" , cexCol=0.9 )

pdf("plot22_mitch1_heatmap01.pdf")
heatmap.2(mr1,trace="none",col=colfunc(25),scale="none", margins = c(10,28), cexRow=0.7,
  main="Top ranked pathways in hypothalamus" , cexCol=0.9 )
dev.off()

## X11cairo 
##        2

Two dimensional mitch analysis in hyp with focus on discordant pathways

mres1 <- mitch_calc(m1, genesets, priority="SD")

## Note: Prioritisation by SD after selecting sets with 
##             p.adjustMANOVA<=0.05.

head(mres1$enrichment_result,20) %>%
  kbl(caption = "Top gene pathway discordant differences in hypothalamus for LPS and OVA") %>%
  kable_paper("hover", full_width = F)

Top gene pathway discordant differences in hypothalamus for LPS and OVA

	set	setSize	pMANOVA	s.LPS	s.OVA	p.LPS	p.OVA	s.dist	SD	p.adjustMANOVA
283	ENDOSOMAL VACUOLAR PATHWAY	11	0.0001919	-0.2757209	0.4955551	0.1133476	0.0044279	0.5670952	0.5453745	0.0023985
724	POST CHAPERONIN TUBULIN FOLDING PATHWAY	17	0.0000053	0.6289245	-0.0037748	0.0000071	0.9785061	0.6289359	0.4473860	0.0001302
657	NUCLEOTIDE LIKE PURINERGIC RECEPTORS	13	0.0027340	-0.3141983	0.2847330	0.0498345	0.0754980	0.4240206	0.4235084	0.0199533
797	REGULATION OF GLYCOLYSIS BY FRUCTOSE 2 6 BISPHOSPHATE METABOLISM	11	0.0086111	0.2012983	-0.3727085	0.2477085	0.0323273	0.4235949	0.4058841	0.0494811
112	CARBOXYTERMINAL POST TRANSLATIONAL MODIFICATIONS OF TUBULIN	36	0.0000011	0.3966476	-0.1229249	0.0000381	0.2019370	0.4152588	0.3673932	0.0000372
204	CYTOSOLIC TRNA AMINOACYLATION	24	0.0004582	0.3516074	-0.1311225	0.0028680	0.2662404	0.3752611	0.3413416	0.0050314
1172	YAP1 AND WWTR1 TAZ STIMULATED GENE EXPRESSION	11	0.0015116	0.1440006	0.6164919	0.4082939	0.0003990	0.6330864	0.3341017	0.0126863
1135	TRANSPORT OF CONNEXONS TO THE PLASMA MEMBRANE	13	0.0007834	0.5915627	0.1217340	0.0002213	0.4473220	0.6039582	0.3322191	0.0078672
304	EUKARYOTIC TRANSLATION ELONGATION	87	0.0000000	0.3005016	0.7571632	0.0000013	0.0000000	0.8146148	0.3229085	0.0000000
352	G BETA GAMMA SIGNALLING THROUGH CDC42	18	0.0073887	-0.1377442	0.3126369	0.3117235	0.0216649	0.3416362	0.3184676	0.0440697
837	RESPONSE OF EIF2AK4 GCN2 TO AMINO ACID DEFICIENCY	95	0.0000000	0.2223137	0.6530942	0.0001818	0.0000000	0.6898952	0.3046078	0.0000000
305	EUKARYOTIC TRANSLATION INITIATION	114	0.0000000	0.2044179	0.6309008	0.0001645	0.0000000	0.6631912	0.3015689	0.0000000
28	ACTIVATION OF THE MRNA UPON BINDING OF THE CAP BINDING COMPLEX AND EIFS AND SUBSEQUENT BINDING TO 43S	59	0.0000000	0.1620035	0.5870293	0.0314327	0.0000000	0.6089733	0.3005386	0.0000000
1008	SRP DEPENDENT COTRANSLATIONAL PROTEIN TARGETING TO MEMBRANE	106	0.0000000	0.2541257	0.6771379	0.0000062	0.0000000	0.7232535	0.2991148	0.0000000
1170	WNT5A DEPENDENT INTERNALIZATION OF FZD2 FZD5 AND ROR2	13	0.0083226	0.4713315	0.0516256	0.0032558	0.7472597	0.4741504	0.2967769	0.0486518
328	FORMATION OF ATP BY CHEMIOSMOTIC COUPLING	18	0.0002608	0.1250312	0.5429475	0.3584914	0.0000665	0.5571578	0.2955115	0.0030648
40	ADP SIGNALLING THROUGH P2Y PURINOCEPTOR 12	19	0.0086185	-0.0792814	0.3338471	0.5497163	0.0117646	0.3431318	0.2921260	0.0494811
437	IMMUNOREGULATORY INTERACTIONS BETWEEN A LYMPHOID AND A NON LYMPHOID CELL	48	0.0000044	-0.0622684	0.3487797	0.4555992	0.0000291	0.3542945	0.2906548	0.0001126
908	SEALING OF THE NUCLEAR ENVELOPE NE BY ESCRT III	23	0.0000754	0.5121746	0.1040597	0.0000212	0.3877283	0.5226387	0.2885808	0.0011363
390	GLYCOLYSIS	66	0.0000014	0.1209143	-0.2700827	0.0894988	0.0001485	0.2959137	0.2764766	0.0000434

m1_top <- subset(mres1$enrichment_result,p.adjustMANOVA<0.05)
mitch_report(mres1,outfile="mitch1_discord.html",overwrite=TRUE)

## Note: overwriting existing report

## Dataset saved as " /tmp/RtmpXoIrRA/mitch1_discord.rds ".

## 
## 
## processing file: mitch.Rmd

## 1/34                             
## 2/34 [checklibraries]            
## 3/34                             
## 4/34 [peek]                      
## 5/34                             
## 6/34 [metrics]                   
## 7/34                             
## 8/34 [scatterplot]

## 9/34                             
## 10/34 [contourplot]

## 11/34                             
## 12/34 [input_geneset_metrics1]    
## 13/34                             
## 14/34 [input_geneset_metrics2]

## 15/34                             
## 16/34 [input_geneset_metrics3]

## 17/34                             
## 18/34 [echart1d]                  
## 19/34 [echart2d]                  
## 20/34                             
## 21/34 [heatmap]

## 22/34                             
## 23/34 [effectsize]                
## 24/34                             
## 25/34 [results_table]             
## 26/34                             
## 27/34 [results_table_complete]    
## 28/34                             
## 29/34 [detailed_geneset_reports1d]
## 30/34                             
## 31/34 [detailed_geneset_reports2d]

## 32/34                             
## 33/34 [session_info]              
## 34/34

## output file: /home/mark.ziemann@domain.internal.burnet.edu.au/projects/lung_brain/mitch.knit.md

## /usr/bin/pandoc +RTS -K512m -RTS /home/mark.ziemann@domain.internal.burnet.edu.au/projects/lung_brain/mitch.knit.md --to html4 --from markdown+autolink_bare_uris+tex_math_single_backslash --output /tmp/RtmpXoIrRA/mitch_report.html --lua-filter /usr/local/lib/R/site-library/rmarkdown/rmarkdown/lua/pagebreak.lua --lua-filter /usr/local/lib/R/site-library/rmarkdown/rmarkdown/lua/latex-div.lua --self-contained --variable bs3=TRUE --section-divs --template /usr/local/lib/R/site-library/rmarkdown/rmd/h/default.html --no-highlight --variable highlightjs=1 --variable theme=bootstrap --mathjax --variable 'mathjax-url=https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML' --include-in-header /tmp/RtmpXoIrRA/rmarkdown-str2d12056fcefb35.html

## 
## Output created: /tmp/RtmpXoIrRA/mitch_report.html

## [1] TRUE

write.table(mres1$enrichment_result,file="mitch1_discord.tsv",quote=FALSE,sep="\t",row.names=FALSE)

m1_top_up <- head(subset(m1_top,s.dist>0),10)[,"s.dist"]
names(m1_top_up) <- head(subset(m1_top,s.dist>0),10)[,"set"]
m1_top_dn <- head(subset(m1_top,s.dist<0),10)[,"s.dist"]
names(m1_top_dn) <- head(subset(m1_top,s.dist<0),10)[,"set"]
m1_top_updn <- c(m1_top_up,m1_top_dn)
m1_top_updn <- m1_top_updn[order(m1_top_updn)]

Paper 1 (the hypothalamus, referred as mitch 1 in our analysis):

Thanks for sending the MDS plots and the discordant Mitch1. Criag and I are interested in the following gene sets: Neuronal System gene sets: directly related to mental health, very interesting) (Appeared in the old original JOINT mitch 1 heatmap), which are:

• Highly calcium permeable postsynaptic nicotinic acetylcholine receptors

• Acetylcholine binding and downstream events

• Serotonin Neurotransmitter Release Cycle

• CREB1 phosphorylation through the activation of Adenylate Cyclase

• Voltage gated Potassium channels

mres1 <- mitch_calc(m1, genesets, priority="effect")

## Note: Enrichments with large effect sizes may not be 
##             statistically significant.

## Neuronal sets
mysets2 <- c("HIGHLY CALCIUM PERMEABLE POSTSYNAPTIC NICOTINIC ACETYLCHOLINE RECEPTORS",
"ACETYLCHOLINE BINDING AND DOWNSTREAM EVENTS",
"SEROTONIN NEUROTRANSMITTER RELEASE CYCLE",
"CREB1 PHOSPHORYLATION THROUGH THE ACTIVATION OF ADENYLATE CYCLASE",
"VOLTAGE GATED POTASSIUM CHANNELS")

mres1_bespoke <- mres1$enrichment_result[which(mres1$enrichment_result$set %in% mysets2),]
rownames(mres1_bespoke) <- mres1_bespoke$set

mres1_bespoke %>%
  kbl(caption = "Selected neuronal pathways in hypothalamus for LPS and OVA") %>%
  kable_paper("hover", full_width = F)

Selected neuronal pathways in hypothalamus for LPS and OVA

	set	setSize	pMANOVA	s.LPS	s.OVA	p.LPS	p.OVA	s.dist	SD	p.adjustMANOVA
VOLTAGE GATED POTASSIUM CHANNELS	VOLTAGE GATED POTASSIUM CHANNELS	39	0.0000057	-0.3994268	-0.3608675	0.0000159	0.0000964	0.5383002	0.0272655	0.0001377
SEROTONIN NEUROTRANSMITTER RELEASE CYCLE	SEROTONIN NEUROTRANSMITTER RELEASE CYCLE	18	0.0009568	-0.1551803	-0.5046378	0.2544237	0.0002099	0.5279586	0.2471038	0.0090228
ACETYLCHOLINE BINDING AND DOWNSTREAM EVENTS	ACETYLCHOLINE BINDING AND DOWNSTREAM EVENTS	10	0.0185517	-0.0817184	-0.4983477	0.6545672	0.0063554	0.5050033	0.2946014	0.0871932
HIGHLY CALCIUM PERMEABLE POSTSYNAPTIC NICOTINIC ACETYLCHOLINE RECEPTORS	HIGHLY CALCIUM PERMEABLE POSTSYNAPTIC NICOTINIC ACETYLCHOLINE RECEPTORS	10	0.0185517	-0.0817184	-0.4983477	0.6545672	0.0063554	0.5050033	0.2946014	0.0871932
CREB1 PHOSPHORYLATION THROUGH THE ACTIVATION OF ADENYLATE CYCLASE	CREB1 PHOSPHORYLATION THROUGH THE ACTIVATION OF ADENYLATE CYCLASE	11	0.0588469	-0.3803911	-0.3052389	0.0289281	0.0796286	0.4877173	0.0531407	0.1951552

mres1mx <- as.matrix(mres1_bespoke[,c("s.LPS","s.OVA")])

heatmap.2(mres1mx,trace="none",col=colfunc(25),scale="none", margins = c(15,28), cexRow=0.7,
  main="Neuronal pathways in hypothalamus" , cexCol=0.9 )

lapply(1:length(mysets2), function(i) {
  myset=mysets2[i]
  mygenes <- genesets[[which(names(genesets) == myset)]]
  allnames <- rownames(rpm1)
  allnames <- sapply(strsplit(allnames," "),"[[",2)
  mymx <- rpm1[which(allnames %in% mygenes),]
  mymx <- mymx[,colnames(mymx) %in% rownames(ss1)] # filter for those present in ss1
  mycols <- gsub("OVA","purple",gsub("LPS","orange",gsub("Control","green",ss1$Treatment)))

  heatmap.2(mymx,trace="none",col=colfunc(25),scale="row", margins = c(10,15), cexRow=0.7,
    ColSideColors=mycols,main=myset)
  mtext("Ctrl=green,LPS=orange,OVA=purple")
})

## [[1]]
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## [[2]]
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## [[3]]
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## [[4]]
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## [[5]]
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pdf("mitch1_bespoke_heatmap01.pdf")
heatmap.2(mres1mx,trace="none",col=colfunc(25),scale="none", margins = c(10,28), cexRow=0.7,
  main="Neuronal pathways in hypothalamus" , cexCol=0.9 )

lapply(1:length(mysets2), function(i) {
  myset=mysets2[i]
  mygenes <- genesets[[which(names(genesets) == myset)]]
  allnames <- rownames(rpm1)
  allnames <- sapply(strsplit(allnames," "),"[[",2)
  mymx <- rpm1[which(allnames %in% mygenes),]
  mymx <- mymx[,colnames(mymx) %in% rownames(ss1)] # filter for those present in ss1
  mycols <- gsub("OVA","purple",gsub("LPS","orange",gsub("Control","green",ss1$Treatment)))

  heatmap.2(mymx,trace="none",col=colfunc(25),scale="row", margins = c(10,15), cexRow=0.7,
    ColSideColors=mycols,main=myset)
  mtext("Ctrl=green,LPS=orange,OVA=purple")
})

## [[1]]
## NULL
## 
## [[2]]
## NULL
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## [[3]]
## NULL
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## [[4]]
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## [[5]]
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dev.off()

## X11cairo 
##        2

mysets2 <- genesets[names(genesets) %in% mysets2]
mres1b1 <- mitch_calc(m1, mysets2, priority="effect")

## Note: Enrichments with large effect sizes may not be 
##             statistically significant.

mitch_plots(res=mres1b1,outfile="mitch1_bespoke_charts.pdf")

## X11cairo 
##        2

## Immuno sets
mysets3 <- c("IMMUNOREGULATORY INTERACTIONS BETWEEN A LYMPHOID AND A NON LYMPHOID CELL",
"INTERFERON GAMMA SIGNALING",
"ENDOSOMAL VACUOLAR PATHWAY",
"INTERFERON ALPHA BETA SIGNALING")

mres1_bespoke <- mres1$enrichment_result[which(mres1$enrichment_result$set %in% mysets3),]
rownames(mres1_bespoke) <- mres1_bespoke$set

mres1_bespoke %>%
  kbl(caption = "Selected immune pathways in hypothalamus for LPS and OVA") %>%
  kable_paper("hover", full_width = F)

Selected immune pathways in hypothalamus for LPS and OVA

	set	setSize	pMANOVA	s.LPS	s.OVA	p.LPS	p.OVA	s.dist	SD	p.adjustMANOVA
ENDOSOMAL VACUOLAR PATHWAY	ENDOSOMAL VACUOLAR PATHWAY	11	0.0001919	-0.2757209	0.4955551	0.1133476	0.0044279	0.5670952	0.5453745	0.0023985
IMMUNOREGULATORY INTERACTIONS BETWEEN A LYMPHOID AND A NON LYMPHOID CELL	IMMUNOREGULATORY INTERACTIONS BETWEEN A LYMPHOID AND A NON LYMPHOID CELL	48	0.0000044	-0.0622684	0.3487797	0.4555992	0.0000291	0.3542945	0.2906548	0.0001126
INTERFERON ALPHA BETA SIGNALING	INTERFERON ALPHA BETA SIGNALING	55	0.0001042	-0.1834910	0.1800378	0.0186137	0.0209500	0.2570653	0.2570537	0.0014892
INTERFERON GAMMA SIGNALING	INTERFERON GAMMA SIGNALING	77	0.0000357	-0.2175923	0.0978846	0.0009672	0.1377361	0.2385955	0.2230759	0.0005991

mres1mx <- as.matrix(mres1_bespoke[,c("s.LPS","s.OVA")])

heatmap.2(mres1mx,trace="none",col=colfunc(25),scale="none", margins = c(15,28), cexRow=0.7,
  main="Immune pathways in hypothalamus" , cexCol=0.9 )

lapply(1:length(mysets3), function(i) {
  myset=mysets3[i]
  mygenes <- genesets[[which(names(genesets) == myset)]]
  allnames <- rownames(rpm1)
  allnames <- sapply(strsplit(allnames," "),"[[",2)
  mymx <- rpm1[which(allnames %in% mygenes),]
  mymx <- mymx[,colnames(mymx) %in% rownames(ss1)] # filter for those present in ss1
  mycols <- gsub("OVA","purple",gsub("LPS","orange",gsub("Control","green",ss1$Treatment)))

  heatmap.2(mymx,trace="none",col=colfunc(25),scale="row", margins = c(10,15), cexRow=0.7,
    ColSideColors=mycols,main=myset)
  mtext("Ctrl=green,LPS=orange,OVA=purple")
})

## [[1]]
## NULL
## 
## [[2]]
## NULL
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## [[3]]
## NULL
## 
## [[4]]
## NULL

pdf("mitch1_bespoke_heatmap02.pdf")
heatmap.2(mres1mx,trace="none",col=colfunc(25),scale="none", margins = c(10,28), cexRow=0.7,
  main="Immune pathways in hypothalamus" , cexCol=0.9 )

lapply(1:length(mysets3), function(i) {
  myset=mysets3[i]
  mygenes <- genesets[[which(names(genesets) == myset)]]
  allnames <- rownames(rpm1)
  allnames <- sapply(strsplit(allnames," "),"[[",2)
  mymx <- rpm1[which(allnames %in% mygenes),]
  mymx <- mymx[,colnames(mymx) %in% rownames(ss1)] # filter for those present in ss1
  mycols <- gsub("OVA","purple",gsub("LPS","orange",gsub("Control","green",ss1$Treatment)))

  heatmap.2(mymx,trace="none",col=colfunc(25),scale="row", margins = c(10,15), cexRow=0.7,
    ColSideColors=mycols,main=myset)
  mtext("Ctrl=green,LPS=orange,OVA=purple")
})

## [[1]]
## NULL
## 
## [[2]]
## NULL
## 
## [[3]]
## NULL
## 
## [[4]]
## NULL

dev.off()

## X11cairo 
##        2

mysets3 <- genesets[names(genesets) %in% mysets3]

mres1b2 <- mitch_calc(m1, mysets3, priority="effect")

## Note: Enrichments with large effect sizes may not be 
##             statistically significant.

mitch_plots(res=mres1b2,outfile="mitch1_bespoke_charts2.pdf")

## X11cairo 
##        2

Mitch pathway analysis in amygdala

Mitch for LPS in amy

m2_l <- mitch_import(dge2_l, DEtype="deseq2",geneTable=gt)

## The input is a single dataframe; one contrast only. Converting
##         it to a list for you.

## Note: Mean no. genes in input = 16818

## Note: no. genes in output = 16805

## Note: estimated proportion of input genes in output = 0.999

mres2_l <- mitch_calc(m2_l, genesets, priority="effect")

## Note: Enrichments with large effect sizes may not be
##             statistically significant.

head(mres2_l$enrichment_result,20) %>%
  kbl(caption = "Top gene pathway differences in amy for LPS") %>%
  kable_paper("hover", full_width = F)

Top gene pathway differences in amy for LPS

	set	setSize	pANOVA	s.dist	p.adjustANOVA
534	MET ACTIVATES RAP1 AND RAC1	11	0.0010360	-0.5712105	0.0294883
1039	SYNTHESIS OF PIPS AT THE LATE ENDOSOME MEMBRANE	11	0.0021395	-0.5345849	0.0509548
24	ACTIVATION OF RAC1	13	0.0017581	-0.5010903	0.0446019
443	INITIAL TRIGGERING OF COMPLEMENT	10	0.0100199	0.4702828	0.1538585
301	EUKARYOTIC TRANSLATION ELONGATION	87	0.0000000	0.4370016	0.0000000
185	CREB1 PHOSPHORYLATION THROUGH THE ACTIVATION OF ADENYLATE CYCLASE	11	0.0121708	-0.4365737	0.1711250
621	NF KB IS ACTIVATED AND SIGNALS SURVIVAL	13	0.0067949	0.4335764	0.1239016
20	ACTIVATION OF IRF3 IRF7 MEDIATED BY TBK1 IKK EPSILON	16	0.0027697	0.4320686	0.0633763
909	SEMA4D INDUCED CELL MIGRATION AND GROWTH CONE COLLAPSE	20	0.0009077	0.4285255	0.0264831
199	CYTOSOLIC IRON SULFUR CLUSTER ASSEMBLY	13	0.0092131	0.4171309	0.1472838
901	SCAVENGING BY CLASS A RECEPTORS	14	0.0075363	0.4124829	0.1305423
917	SEROTONIN RECEPTORS	10	0.0291373	-0.3984281	0.2951866
164	COLLAGEN CHAIN TRIMERIZATION	40	0.0000146	0.3961378	0.0008093
1037	SYNTHESIS OF PIPS AT THE EARLY ENDOSOME MEMBRANE	16	0.0061311	-0.3957576	0.1135714
798	REGULATION OF INNATE IMMUNE RESPONSES TO CYTOSOLIC DNA	13	0.0188315	0.3762598	0.2265600
833	RESPONSE OF EIF2AK4 GCN2 TO AMINO ACID DEFICIENCY	94	0.0000000	0.3739962	0.0000001
45	ALPHA LINOLENIC OMEGA3 AND LINOLEIC OMEGA6 ACID METABOLISM	12	0.0277052	0.3670379	0.2912789
163	COLLAGEN BIOSYNTHESIS AND MODIFYING ENZYMES	63	0.0000010	0.3569447	0.0000701
629	NONSENSE MEDIATED DECAY NMD	109	0.0000000	0.3467072	0.0000001
42	AGGREPHAGY	35	0.0005188	0.3390374	0.0163640

m2_l_top <- subset(mres2_l$enrichment_result,p.adjustANOVA<0.05)
m2_l_up <- subset(m2_l_top,s.dist>0)$set
m2_l_dn <- subset(m2_l_top,s.dist<0)$set
mitch_report(mres2_l,outfile="mitch2_l.html",overwrite=TRUE)

## Note: overwriting existing report

## Dataset saved as " /tmp/RtmpXoIrRA/mitch2_l.rds ".

## 
## 
## processing file: mitch.Rmd

## 1/34                             
## 2/34 [checklibraries]            
## 3/34                             
## 4/34 [peek]                      
## 5/34                             
## 6/34 [metrics]                   
## 7/34                             
## 8/34 [scatterplot]

## 9/34                             
## 10/34 [contourplot]               
## 11/34                             
## 12/34 [input_geneset_metrics1]    
## 13/34                             
## 14/34 [input_geneset_metrics2]

## 15/34                             
## 16/34 [input_geneset_metrics3]

## 17/34                             
## 18/34 [echart1d]                  
## 19/34 [echart2d]                  
## 20/34                             
## 21/34 [heatmap]                   
## 22/34                             
## 23/34 [effectsize]                
## 24/34                             
## 25/34 [results_table]             
## 26/34                             
## 27/34 [results_table_complete]    
## 28/34                             
## 29/34 [detailed_geneset_reports1d]

## 30/34                             
## 31/34 [detailed_geneset_reports2d]
## 32/34                             
## 33/34 [session_info]              
## 34/34

## output file: /home/mark.ziemann@domain.internal.burnet.edu.au/projects/lung_brain/mitch.knit.md

## /usr/bin/pandoc +RTS -K512m -RTS /home/mark.ziemann@domain.internal.burnet.edu.au/projects/lung_brain/mitch.knit.md --to html4 --from markdown+autolink_bare_uris+tex_math_single_backslash --output /tmp/RtmpXoIrRA/mitch_report.html --lua-filter /usr/local/lib/R/site-library/rmarkdown/rmarkdown/lua/pagebreak.lua --lua-filter /usr/local/lib/R/site-library/rmarkdown/rmarkdown/lua/latex-div.lua --self-contained --variable bs3=TRUE --section-divs --template /usr/local/lib/R/site-library/rmarkdown/rmd/h/default.html --no-highlight --variable highlightjs=1 --variable theme=bootstrap --mathjax --variable 'mathjax-url=https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML' --include-in-header /tmp/RtmpXoIrRA/rmarkdown-str2d12052fd0e6ef.html

## 
## Output created: /tmp/RtmpXoIrRA/mitch_report.html

## [1] TRUE

write.table(mres2_l$enrichment_result,file="mitch2_l.tsv",quote=FALSE,sep="\t",row.names=FALSE)

m2_l_top_up <- head(subset(m2_l_top,s.dist>0),10)[,"s.dist"]
names(m2_l_top_up) <- head(subset(m2_l_top,s.dist>0),10)[,"set"]
m2_l_top_dn <- head(subset(m2_l_top,s.dist<0),10)[,"s.dist"]
names(m2_l_top_dn) <- head(subset(m2_l_top,s.dist<0),10)[,"set"]
m2_l_top_updn <- c(m2_l_top_up,m2_l_top_dn)
m2_l_top_updn <- m2_l_top_updn[order(m2_l_top_updn)]

par(mar=c(5,25,3,1))
barplot(m2_l_top_updn,horiz=TRUE,las=1,col="darkgray",
  xlab="Enrichment score",cex.names=0.8,xlim=c(-1,1),
  main="Effect of LPS in amy")
grid()

pdf("plot23_mitch2lps01.pdf")
par(mar=c(5,25,3,1))
barplot(m2_l_top_updn,horiz=TRUE,las=1,col="darkgray",
  xlab="Enrichment score",cex.names=0.8,xlim=c(-1,1),
  main="Effect of LPS in amy")
grid()
dev.off()

## X11cairo 
##        2

Mitch for OVA in amy

m2_o <- mitch_import(dge2_o, DEtype="deseq2",geneTable=gt)

## The input is a single dataframe; one contrast only. Converting
##         it to a list for you.

## Note: Mean no. genes in input = 16624

## Note: no. genes in output = 16611

## Note: estimated proportion of input genes in output = 0.999

mres2_o <- mitch_calc(m2_o, genesets, priority="effect")

## Note: Enrichments with large effect sizes may not be
##             statistically significant.

head(mres2_o$enrichment_result,20) %>%
  kbl(caption = "Top gene pathway differences in amy for OVA") %>%
  kable_paper("hover", full_width = F)

Top gene pathway differences in amy for OVA

	set	setSize	pANOVA	s.dist	p.adjustANOVA
1096	TRAFFICKING AND PROCESSING OF ENDOSOMAL TLR	11	0.0001756	0.6532311	0.0016524
656	NUCLEOTIDE LIKE PURINERGIC RECEPTORS	13	0.0005322	0.5548398	0.0043435
39	ADP SIGNALLING THROUGH P2Y PURINOCEPTOR 12	19	0.0000405	0.5439146	0.0005132
72	ASPARTATE AND ASPARAGINE METABOLISM	10	0.0030160	0.5416662	0.0171690
170	COMPLEX I BIOGENESIS	56	0.0000000	0.5353777	0.0000000
1045	SYNTHESIS OF VERY LONG CHAIN FATTY ACYL COAS	20	0.0000349	0.5345187	0.0004577
1137	TRIGLYCERIDE CATABOLISM	14	0.0006660	0.5252929	0.0052667
704	PINK1 PRKN MEDIATED MITOPHAGY	22	0.0000243	0.5198954	0.0003329
344	FRS MEDIATED FGFR4 SIGNALING	12	0.0019086	0.5175011	0.0121050
921	SHC MEDIATED CASCADE FGFR4	10	0.0047228	0.5159689	0.0242800
302	EUKARYOTIC TRANSLATION ELONGATION	87	0.0000000	0.5146676	0.0000000
188	CRMPS IN SEMA3A SIGNALING	16	0.0003855	-0.5125565	0.0033082
831	RESPIRATORY ELECTRON TRANSPORT	102	0.0000000	0.5112886	0.0000000
700	PI 3K CASCADE FGFR4	10	0.0053282	0.5088730	0.0264558
832	RESPIRATORY ELECTRON TRANSPORT ATP SYNTHESIS BY CHEMIOSMOTIC COUPLING AND HEAT PRODUCTION BY UNCOUPLING PROTEINS	125	0.0000000	0.5040543	0.0000000
718	PLATELET SENSITIZATION BY LDL	15	0.0007634	0.5019121	0.0057444
343	FRS MEDIATED FGFR3 SIGNALING	14	0.0012001	0.4999354	0.0081902
950	SIGNALING BY FGFR3 FUSIONS IN CANCER	10	0.0062690	0.4991747	0.0302309
920	SHC MEDIATED CASCADE FGFR3	12	0.0029455	0.4957126	0.0168498
153	CITRIC ACID CYCLE TCA CYCLE	22	0.0000573	0.4955255	0.0006620

m2_o_top <- subset(mres2_o$enrichment_result,p.adjustANOVA<0.05)
m2_o_up <- subset(m2_o_top,s.dist>0)$set
m2_o_dn <- subset(m2_o_top,s.dist<0)$set
mitch_report(mres2_o,outfile="mitch2_o.html",overwrite=TRUE)

## Note: overwriting existing report

## Dataset saved as " /tmp/RtmpXoIrRA/mitch2_o.rds ".

## 
## 
## processing file: mitch.Rmd

## 1/34                             
## 2/34 [checklibraries]            
## 3/34                             
## 4/34 [peek]                      
## 5/34                             
## 6/34 [metrics]                   
## 7/34                             
## 8/34 [scatterplot]

## 9/34                             
## 10/34 [contourplot]               
## 11/34                             
## 12/34 [input_geneset_metrics1]    
## 13/34                             
## 14/34 [input_geneset_metrics2]

## 15/34                             
## 16/34 [input_geneset_metrics3]

## 17/34                             
## 18/34 [echart1d]                  
## 19/34 [echart2d]                  
## 20/34                             
## 21/34 [heatmap]                   
## 22/34                             
## 23/34 [effectsize]                
## 24/34                             
## 25/34 [results_table]             
## 26/34                             
## 27/34 [results_table_complete]    
## 28/34                             
## 29/34 [detailed_geneset_reports1d]

## 30/34                             
## 31/34 [detailed_geneset_reports2d]
## 32/34                             
## 33/34 [session_info]              
## 34/34

## output file: /home/mark.ziemann@domain.internal.burnet.edu.au/projects/lung_brain/mitch.knit.md

## /usr/bin/pandoc +RTS -K512m -RTS /home/mark.ziemann@domain.internal.burnet.edu.au/projects/lung_brain/mitch.knit.md --to html4 --from markdown+autolink_bare_uris+tex_math_single_backslash --output /tmp/RtmpXoIrRA/mitch_report.html --lua-filter /usr/local/lib/R/site-library/rmarkdown/rmarkdown/lua/pagebreak.lua --lua-filter /usr/local/lib/R/site-library/rmarkdown/rmarkdown/lua/latex-div.lua --self-contained --variable bs3=TRUE --section-divs --template /usr/local/lib/R/site-library/rmarkdown/rmd/h/default.html --no-highlight --variable highlightjs=1 --variable theme=bootstrap --mathjax --variable 'mathjax-url=https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML' --include-in-header /tmp/RtmpXoIrRA/rmarkdown-str2d1205466f887f.html

## 
## Output created: /tmp/RtmpXoIrRA/mitch_report.html

## [1] TRUE

write.table(mres2_o$enrichment_result,file="mitch2_o.tsv",quote=FALSE,sep="\t",row.names=FALSE)

m2_o_top_up <- head(subset(m2_o_top,s.dist>0),10)[,"s.dist"]
names(m2_o_top_up) <- head(subset(m2_o_top,s.dist>0),10)[,"set"]
m2_o_top_dn <- head(subset(m2_o_top,s.dist<0),10)[,"s.dist"]
names(m2_o_top_dn) <- head(subset(m2_o_top,s.dist<0),10)[,"set"]
m2_o_top_updn <- c(m2_o_top_up,m2_o_top_dn)
m2_o_top_updn <- m2_o_top_updn[order(m2_o_top_updn)]

par(mar=c(5,25,3,1))
barplot(m2_o_top_updn,horiz=TRUE,las=1,col="darkgray",
  xlab="Enrichment score",cex.names=0.8,xlim=c(-1,1),
  main="Effect of LPS in amy")
grid()

pdf("plot24_mitch2ova01.pdf")
par(mar=c(5,25,3,1))
barplot(m2_o_top_updn,horiz=TRUE,las=1,col="darkgray",
  xlab="Enrichment score",cex.names=0.8,xlim=c(-1,1),
  main="Effect of LPS in amy")
grid()
dev.off()

## X11cairo 
##        2

Mitch 2 euler.

m2_l_up <- subset(m2_l_top,s.dist>0)$set
m2_l_dn <- subset(m2_l_top,s.dist<0)$set
m2_o_up <- subset(m2_o_top,s.dist>0)$set
m2_o_dn <- subset(m2_o_top,s.dist<0)$set

par(mar=c(2,2,2,2))

v0 <- list("LPS up"=m2_l_up,
  "LPS dn"=m2_l_dn,
  "OVA up"=m2_o_up,
  "OVA dn"=m2_o_dn)
plot(euler(v0),quantities = TRUE, edges = "gray", main="effect of treatments on amy")

pdf("plot25_mitch2lpsovaeuler01.pdf")
plot(euler(v0),quantities = TRUE, edges = "gray", main="effect of treatments on amy")
dev.off()

## X11cairo 
##        2

Two dimensional mitch analysis in amy.

ml <- list("LPS"=dge2_l,"OVA"=dge2_o)
m2 <- mitch_import(ml, DEtype="deseq2",geneTable=gt)

## Note: Mean no. genes in input = 16721

## Note: no. genes in output = 16550

## Note: estimated proportion of input genes in output = 0.99

head(m2)

##                      LPS          OVA
## 0610009B22Rik  0.2684894  1.831236577
## 0610009E02Rik -0.7521235 -0.103702849
## 0610009L18Rik -1.1838923 -0.075637695
## 0610010K14Rik -0.0433030  0.417670579
## 0610012G03Rik -0.1930476  0.008709563
## 0610030E20Rik  1.0496464 -1.609523779

mres2 <- mitch_calc(m2, genesets, priority="effect")

## Note: Enrichments with large effect sizes may not be 
##             statistically significant.

head(mres2$enrichment_result,20) %>%
  kbl(caption = "Top gene pathway differences in amy for LPS and OVA") %>%
  kable_paper("hover", full_width = F)

Top gene pathway differences in amy for LPS and OVA

	set	setSize	pMANOVA	s.LPS	s.OVA	p.LPS	p.OVA	s.dist	SD	p.adjustMANOVA
301	EUKARYOTIC TRANSLATION ELONGATION	87	0.0000000	0.4430297	0.5156244	0.0000000	0.0000000	0.6798116	0.0513322	0.0000000
1095	TRAFFICKING AND PROCESSING OF ENDOSOMAL TLR	11	0.0008503	0.0789264	0.6538650	0.6503946	0.0001730	0.6586112	0.4065430	0.0063961
534	MET ACTIVATES RAP1 AND RAC1	11	0.0012900	-0.5676115	0.2448043	0.0011146	0.1597948	0.6181521	0.5744647	0.0087961
169	COMPLEX I BIOGENESIS	56	0.0000000	0.2952502	0.5362275	0.0001329	0.0000000	0.6121377	0.1703966	0.0000000
833	RESPONSE OF EIF2AK4 GCN2 TO AMINO ACID DEFICIENCY	94	0.0000000	0.3800903	0.4681898	0.0000000	0.0000000	0.6030509	0.0622958	0.0000000
45	ALPHA LINOLENIC OMEGA3 AND LINOLEIC OMEGA6 ACID METABOLISM	12	0.0022675	0.3721127	0.4720139	0.0256238	0.0046376	0.6010533	0.0706408	0.0142145
1004	SRP DEPENDENT COTRANSLATIONAL PROTEIN TARGETING TO MEMBRANE	106	0.0000000	0.3243576	0.4911983	0.0000000	0.0000000	0.5886285	0.1179742	0.0000000
39	ADP SIGNALLING THROUGH P2Y PURINOCEPTOR 12	19	0.0000306	-0.2223605	0.5448806	0.0933928	0.0000392	0.5885058	0.5425214	0.0004249
443	INITIAL TRIGGERING OF COMPLEMENT	10	0.0085049	0.4743047	0.3372189	0.0093999	0.0648311	0.5819635	0.0969343	0.0395086
123	CD28 DEPENDENT VAV1 PATHWAY	10	0.0045592	-0.4094075	0.4085490	0.0249781	0.0252838	0.5783829	0.5783826	0.0243853
302	EUKARYOTIC TRANSLATION INITIATION	114	0.0000000	0.3057271	0.4748925	0.0000000	0.0000000	0.5647937	0.1196180	0.0000000
655	NUCLEOTIDE LIKE PURINERGIC RECEPTORS	13	0.0024330	0.0355520	0.5557189	0.8243775	0.0005215	0.5568549	0.3678135	0.0148530
703	PINK1 PRKN MEDIATED MITOPHAGY	22	0.0000610	0.1878355	0.5208297	0.1272824	0.0000234	0.5536657	0.2354624	0.0007331
830	RESPIRATORY ELECTRON TRANSPORT	102	0.0000000	0.2102216	0.5121571	0.0002459	0.0000000	0.5536227	0.2135006	0.0000000
1029	SYNTHESIS OF BILE ACIDS AND BILE SALTS VIA 7ALPHA HYDROXYCHOLESTEROL	12	0.0057157	0.3196174	0.4512335	0.0552416	0.0067994	0.5529620	0.0930666	0.0282393
27	ACTIVATION OF THE MRNA UPON BINDING OF THE CAP BINDING COMPLEX AND EIFS AND SUBSEQUENT BINDING TO 43S	59	0.0000000	0.2703241	0.4804130	0.0003301	0.0000000	0.5512457	0.1485553	0.0000000
185	CREB1 PHOSPHORYLATION THROUGH THE ACTIVATION OF ADENYLATE CYCLASE	11	0.0046348	-0.4323170	0.3418202	0.0130394	0.0496554	0.5511252	0.5473976	0.0244530
831	RESPIRATORY ELECTRON TRANSPORT ATP SYNTHESIS BY CHEMIOSMOTIC COUPLING AND HEAT PRODUCTION BY UNCOUPLING PROTEINS	125	0.0000000	0.2156727	0.5049507	0.0000316	0.0000000	0.5490810	0.2045505	0.0000000
164	COLLAGEN CHAIN TRIMERIZATION	39	0.0000000	0.4232656	-0.3400608	0.0000048	0.0002384	0.5429504	0.5397533	0.0000003
71	ASPARTATE AND ASPARAGINE METABOLISM	10	0.0120499	0.0136880	0.5423579	0.9402592	0.0029791	0.5425306	0.3738261	0.0522310

m2_top <- subset(mres2$enrichment_result,p.adjustMANOVA<0.05)
m2_up <- subset(m2_top,s.dist>0)$set
m2_dn <- subset(m2_top,s.dist<0)$set
mitch_report(mres2,outfile="mitch2.html",overwrite=TRUE)

## Note: overwriting existing report

## Dataset saved as " /tmp/RtmpXoIrRA/mitch2.rds ".

## 
## 
## processing file: mitch.Rmd

## 1/34                             
## 2/34 [checklibraries]            
## 3/34                             
## 4/34 [peek]                      
## 5/34                             
## 6/34 [metrics]                   
## 7/34                             
## 8/34 [scatterplot]

## 9/34                             
## 10/34 [contourplot]

## 11/34                             
## 12/34 [input_geneset_metrics1]    
## 13/34                             
## 14/34 [input_geneset_metrics2]

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## 16/34 [input_geneset_metrics3]

## 17/34                             
## 18/34 [echart1d]                  
## 19/34 [echart2d]                  
## 20/34                             
## 21/34 [heatmap]

## 22/34                             
## 23/34 [effectsize]                
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## 25/34 [results_table]             
## 26/34                             
## 27/34 [results_table_complete]    
## 28/34                             
## 29/34 [detailed_geneset_reports1d]
## 30/34                             
## 31/34 [detailed_geneset_reports2d]

## 32/34                             
## 33/34 [session_info]              
## 34/34

## output file: /home/mark.ziemann@domain.internal.burnet.edu.au/projects/lung_brain/mitch.knit.md

## /usr/bin/pandoc +RTS -K512m -RTS /home/mark.ziemann@domain.internal.burnet.edu.au/projects/lung_brain/mitch.knit.md --to html4 --from markdown+autolink_bare_uris+tex_math_single_backslash --output /tmp/RtmpXoIrRA/mitch_report.html --lua-filter /usr/local/lib/R/site-library/rmarkdown/rmarkdown/lua/pagebreak.lua --lua-filter /usr/local/lib/R/site-library/rmarkdown/rmarkdown/lua/latex-div.lua --self-contained --variable bs3=TRUE --section-divs --template /usr/local/lib/R/site-library/rmarkdown/rmd/h/default.html --no-highlight --variable highlightjs=1 --variable theme=bootstrap --mathjax --variable 'mathjax-url=https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML' --include-in-header /tmp/RtmpXoIrRA/rmarkdown-str2d12057ceebb87.html

## 
## Output created: /tmp/RtmpXoIrRA/mitch_report.html

## [1] TRUE

write.table(mres2$enrichment_result,file="mitch2.tsv",quote=FALSE,sep="\t",row.names=FALSE)

m2_top_up <- head(subset(m2_top,s.dist>0),10)[,"s.dist"]
names(m2_top_up) <- head(subset(m2_top,s.dist>0),10)[,"set"]
m2_top_dn <- head(subset(m2_top,s.dist<0),10)[,"s.dist"]
names(m2_top_dn) <- head(subset(m2_top,s.dist<0),10)[,"set"]
m2_top_updn <- c(m2_top_up,m2_top_dn)
m2_top_updn <- m2_top_updn[order(m2_top_updn)]

# heatmap
mr2 <- head(subset(mres2$enrichment_result,p.adjustMANOVA<0.05),30)
mr2 <- as.matrix(mr2[,4:5])
rownames(mr2) <- head(mres2$enrichment_result,30)$set

colfunc <- colorRampPalette(c("blue", "white", "red"))

heatmap.2(mr2,trace="none",col=colfunc(25),scale="none", margins = c(10,28), cexRow=0.7,
  main="Top ranked pathways in amy" , cexCol=0.9 )

pdf("plot26_mitch2_heatmap01.pdf")
heatmap.2(mr2,trace="none",col=colfunc(25),scale="none", margins = c(10,28), cexRow=0.7,
  main="Top ranked pathways in amy" , cexCol=0.9 )
dev.off()

## X11cairo 
##        2

Need to make a scatterplot to highlight the following:

• Serotonin Neurotransmitter Release Cycle (DN)
• Class C3 metabotropic glutamate pheromone receptors (DN)
• Regulation of commissural axon pathfinding by SLIT AND robo (DN)
• Reduction of Cytosolic Ca (DN)
• YAP1 and WWTR1 TAZ STIMULATED GENE EXPRESSION (UP)
• Activation of the mRNA upon binding (UP)
• Regulation of expression of slits and robs (UP)

par(mar=c(4,4,2,1))

a <- mres2$enrichment_result
plot(a$s.LPS , a$s.OVA,pch=19,col="gray")
abline(h=0,lty=2)
abline(v=0,lty=2)
as <- subset(a,p.adjustMANOVA<0.05)
points(as$s.LPS , as$s.OVA,pch=19,col="red")

abline(h=0.2,lty=2,col="red")
abline(v=0.2,lty=2,col="red")
abline(h=-0.2,lty=2,col="red")
abline(v=-0.2,lty=2,col="red")

mysets <- c("SEROTONIN NEUROTRANSMITTER RELEASE CYCLE",
  "CLASS C 3 METABOTROPIC GLUTAMATE PHEROMONE RECEPTORS",
  "REGULATION OF COMMISSURAL AXON PATHFINDING BY SLIT AND ROBO",
  "REDUCTION OF CYTOSOLIC CA LEVELS",
  "YAP1 AND WWTR1 TAZ STIMULATED GENE EXPRESSION",
  "ACTIVATION OF THE MRNA UPON BINDING OF THE CAP BINDING COMPLEX AND EIFS AND SUBSEQUENT BINDING TO 43S",
  "REGULATION OF EXPRESSION OF SLITS AND ROBOS")

a2 <- a[which(a$set %in% mysets),]

points(a2$s.LPS , a2$s.OVA+0.025,pch=25,col="red")

pdf("plot26b_mitch2_scatter01.pdf")

a <- mres2$enrichment_result
plot(a$s.LPS , a$s.OVA,pch=19,col="gray")
abline(h=0,lty=2)
abline(v=0,lty=2)
as <- subset(a,p.adjustMANOVA<0.05)
points(as$s.LPS , as$s.OVA,pch=19,col="red")

abline(h=0.2,lty=2,col="red")
abline(v=0.2,lty=2,col="red")
abline(h=-0.2,lty=2,col="red")
abline(v=-0.2,lty=2,col="red")

mysets <- c("SEROTONIN NEUROTRANSMITTER RELEASE CYCLE",
  "CLASS C 3 METABOTROPIC GLUTAMATE PHEROMONE RECEPTORS",
  "REGULATION OF COMMISSURAL AXON PATHFINDING BY SLIT AND ROBO",
  "REDUCTION OF CYTOSOLIC CA LEVELS",
  "YAP1 AND WWTR1 TAZ STIMULATED GENE EXPRESSION",
  "ACTIVATION OF THE MRNA UPON BINDING OF THE CAP BINDING COMPLEX AND EIFS AND SUBSEQUENT BINDING TO 43S",
  "REGULATION OF EXPRESSION OF SLITS AND ROBOS")

a2 <- a[which(a$set %in% mysets),]

points(a2$s.LPS , a2$s.OVA+0.025,pch=25,col="red")

dev.off()

## X11cairo 
##        2

Mitch pathway analysis in hippocampus

Mitch for LPS in hip

m3_l <- mitch_import(dge3_l, DEtype="deseq2",geneTable=gt)

## The input is a single dataframe; one contrast only. Converting
##         it to a list for you.

## Note: Mean no. genes in input = 16927

## Note: no. genes in output = 16911

## Note: estimated proportion of input genes in output = 0.999

mres3_l <- mitch_calc(m3_l, genesets, priority="effect")

## Note: Enrichments with large effect sizes may not be
##             statistically significant.

head(mres3_l$enrichment_result,20) %>%
  kbl(caption = "Top gene pathway differences in hip for LPS") %>%
  kable_paper("hover", full_width = F)

Top gene pathway differences in hip for LPS

	set	setSize	pANOVA	s.dist	p.adjustANOVA
164	COHESIN LOADING ONTO CHROMATIN	10	0.0035266	-0.5328205	0.0606784
1048	SYNTHESIS SECRETION AND INACTIVATION OF GLUCAGON LIKE PEPTIDE 1 GLP 1	12	0.0013981	-0.5326548	0.0382180
438	INCRETIN SYNTHESIS SECRETION AND INACTIVATION	13	0.0011772	-0.5196701	0.0369519
725	POST CHAPERONIN TUBULIN FOLDING PATHWAY	17	0.0002637	0.5110969	0.0172683
191	CROSSLINKING OF COLLAGEN FIBRILS	15	0.0009332	0.4935922	0.0369412
1057	TERMINATION OF O GLYCAN BIOSYNTHESIS	11	0.0051884	0.4867025	0.0775088
737	PROCESSING AND ACTIVATION OF SUMO	10	0.0082053	-0.4827643	0.1012285
66	APOPTOSIS INDUCED DNA FRAGMENTATION	10	0.0082194	-0.4826578	0.1012285
35	ADENYLATE CYCLASE ACTIVATING PATHWAY	10	0.0105880	0.4667889	0.1103673
1131	TRANSPORT OF CONNEXONS TO THE PLASMA MEMBRANE	13	0.0043999	0.4561942	0.0686391
1047	SYNTHESIS SECRETION AND DEACYLATION OF GHRELIN	12	0.0062757	-0.4556483	0.0863827
201	CYTOSOLIC IRON SULFUR CLUSTER ASSEMBLY	13	0.0053223	0.4464069	0.0778381
125	CD28 DEPENDENT VAV1 PATHWAY	11	0.0113946	-0.4406132	0.1128154
26	ACTIVATION OF SMO	16	0.0027889	0.4317624	0.0553052
579	MITOTIC TELOPHASE CYTOKINESIS	13	0.0111915	-0.4063385	0.1119152
1029	SYNTHESIS OF ACTIVE UBIQUITIN ROLES OF E1 AND E2 ENZYMES	29	0.0001730	-0.4029184	0.0144601
537	MET ACTIVATES RAP1 AND RAC1	11	0.0226168	-0.3969984	0.1653851
826	REPRESSION OF WNT TARGET GENES	14	0.0116359	0.3894605	0.1134499
187	CREB1 PHOSPHORYLATION THROUGH THE ACTIVATION OF ADENYLATE CYCLASE	11	0.0266524	-0.3859817	0.1812980
146	CHK1 CHK2 CDS1 MEDIATED INACTIVATION OF CYCLIN B CDK1 COMPLEX	10	0.0352390	-0.3845453	0.2193068

m3_l_top <- subset(mres3_l$enrichment_result,p.adjustANOVA<0.05)
m3_l_up <- subset(m3_l_top,s.dist>0)$set
m3_l_dn <- subset(m3_l_top,s.dist<0)$set
mitch_report(mres3_l,outfile="mitch3_l.html",overwrite=TRUE)

## Note: overwriting existing report

## Dataset saved as " /tmp/RtmpXoIrRA/mitch3_l.rds ".

## 
## 
## processing file: mitch.Rmd

## 1/34                             
## 2/34 [checklibraries]            
## 3/34                             
## 4/34 [peek]                      
## 5/34                             
## 6/34 [metrics]                   
## 7/34                             
## 8/34 [scatterplot]

## 9/34                             
## 10/34 [contourplot]               
## 11/34                             
## 12/34 [input_geneset_metrics1]    
## 13/34                             
## 14/34 [input_geneset_metrics2]

## 15/34                             
## 16/34 [input_geneset_metrics3]

## 17/34                             
## 18/34 [echart1d]                  
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## 22/34                             
## 23/34 [effectsize]                
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## 29/34 [detailed_geneset_reports1d]

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## 34/34

## output file: /home/mark.ziemann@domain.internal.burnet.edu.au/projects/lung_brain/mitch.knit.md

## /usr/bin/pandoc +RTS -K512m -RTS /home/mark.ziemann@domain.internal.burnet.edu.au/projects/lung_brain/mitch.knit.md --to html4 --from markdown+autolink_bare_uris+tex_math_single_backslash --output /tmp/RtmpXoIrRA/mitch_report.html --lua-filter /usr/local/lib/R/site-library/rmarkdown/rmarkdown/lua/pagebreak.lua --lua-filter /usr/local/lib/R/site-library/rmarkdown/rmarkdown/lua/latex-div.lua --self-contained --variable bs3=TRUE --section-divs --template /usr/local/lib/R/site-library/rmarkdown/rmd/h/default.html --no-highlight --variable highlightjs=1 --variable theme=bootstrap --mathjax --variable 'mathjax-url=https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML' --include-in-header /tmp/RtmpXoIrRA/rmarkdown-str2d12056e6fca29.html

## 
## Output created: /tmp/RtmpXoIrRA/mitch_report.html

## [1] TRUE

write.table(mres3_l$enrichment_result,file="mitch3_l.tsv",quote=FALSE,sep="\t",row.names=FALSE)

m3_l_top_up <- head(subset(m3_l_top,s.dist>0),10)[,"s.dist"]
names(m3_l_top_up) <- head(subset(m3_l_top,s.dist>0),10)[,"set"]
m3_l_top_dn <- head(subset(m3_l_top,s.dist<0),10)[,"s.dist"]
names(m3_l_top_dn) <- head(subset(m3_l_top,s.dist<0),10)[,"set"]
m3_l_top_updn <- c(m3_l_top_up,m3_l_top_dn)
m3_l_top_updn <- m3_l_top_updn[order(m3_l_top_updn)]

par(mar=c(5,25,3,1))
barplot(m3_l_top_updn,horiz=TRUE,las=1,col="darkgray",
  xlab="Enrichment score",cex.names=0.8,xlim=c(-1,1),
  main="Effect of LPS in hip")
grid()

pdf("plot27_mitch3lps01.pdf")
par(mar=c(5,25,3,1))
barplot(m3_l_top_updn,horiz=TRUE,las=1,col="darkgray",
  xlab="Enrichment score",cex.names=0.8,xlim=c(-1,1),
  main="Effect of LPS in hip")
grid()
dev.off()

## X11cairo 
##        2

Mitch for OVA in hip

m3_o <- mitch_import(dge3_o, DEtype="deseq2",geneTable=gt)

## The input is a single dataframe; one contrast only. Converting
##         it to a list for you.

## Note: Mean no. genes in input = 16953

## Note: no. genes in output = 16937

## Note: estimated proportion of input genes in output = 0.999

mres3_o <- mitch_calc(m3_o, genesets, priority="effect")

## Note: Enrichments with large effect sizes may not be
##             statistically significant.

head(mres3_o$enrichment_result,20) %>%
  kbl(caption = "Top gene pathway differences in hip for OVA") %>%
  kable_paper("hover", full_width = F)

Top gene pathway differences in hip for OVA

	set	setSize	pANOVA	s.dist	p.adjustANOVA
1049	SYNTHESIS SECRETION AND INACTIVATION OF GLUCAGON LIKE PEPTIDE 1 GLP 1	12	0.0000870	-0.6541605	0.0009584
438	INCRETIN SYNTHESIS SECRETION AND INACTIVATION	13	0.0000524	-0.6477738	0.0006203
303	EUKARYOTIC TRANSLATION ELONGATION	87	0.0000000	-0.5747427	0.0000000
371	GENE AND PROTEIN EXPRESSION BY JAK STAT SIGNALING AFTER INTERLEUKIN 12 STIMULATION	31	0.0000001	-0.5550043	0.0000025
28	ACTIVATION OF THE MRNA UPON BINDING OF THE CAP BINDING COMPLEX AND EIFS AND SUBSEQUENT BINDING TO 43S	59	0.0000000	-0.5534213	0.0000000
304	EUKARYOTIC TRANSLATION INITIATION	114	0.0000000	-0.5495588	0.0000000
1007	SRP DEPENDENT COTRANSLATIONAL PROTEIN TARGETING TO MEMBRANE	106	0.0000000	-0.5424032	0.0000000
837	RESPONSE OF EIF2AK4 GCN2 TO AMINO ACID DEFICIENCY	94	0.0000000	-0.5368238	0.0000000
1048	SYNTHESIS SECRETION AND DEACYLATION OF GHRELIN	12	0.0015460	-0.5277991	0.0119103
35	ADENYLATE CYCLASE ACTIVATING PATHWAY	10	0.0048893	0.5139363	0.0301338
1030	SYNTHESIS OF ACTIVE UBIQUITIN ROLES OF E1 AND E2 ENZYMES	29	0.0000034	-0.4984011	0.0000583
171	COMPLEX I BIOGENESIS	56	0.0000000	-0.4948844	0.0000000
125	CD28 DEPENDENT VAV1 PATHWAY	11	0.0049343	-0.4895212	0.0302516
189	CRMPS IN SEMA3A SIGNALING	16	0.0007160	0.4885350	0.0063037
909	SELENOAMINO ACID METABOLISM	109	0.0000000	-0.4832302	0.0000000
66	APOPTOSIS INDUCED DNA FRAGMENTATION	10	0.0090302	-0.4768122	0.0487300
751	PROTEIN METHYLATION	17	0.0007841	-0.4704561	0.0068012
1099	TRAFFICKING AND PROCESSING OF ENDOSOMAL TLR	11	0.0083861	-0.4590248	0.0462996
834	RESPIRATORY ELECTRON TRANSPORT	102	0.0000000	-0.4579034	0.0000000
1058	TERMINATION OF O GLYCAN BIOSYNTHESIS	10	0.0125175	0.4560525	0.0637303

m3_o_top <- subset(mres3_o$enrichment_result,p.adjustANOVA<0.05)
m3_o_up <- subset(m3_o_top,s.dist>0)$set
m3_o_dn <- subset(m3_o_top,s.dist<0)$set
mitch_report(mres3_o,outfile="mitch3_o.html",overwrite=TRUE)

## Note: overwriting existing report

## Dataset saved as " /tmp/RtmpXoIrRA/mitch3_o.rds ".

## 
## 
## processing file: mitch.Rmd

## 1/34                             
## 2/34 [checklibraries]            
## 3/34                             
## 4/34 [peek]                      
## 5/34                             
## 6/34 [metrics]                   
## 7/34                             
## 8/34 [scatterplot]

## 9/34                             
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## 11/34                             
## 12/34 [input_geneset_metrics1]    
## 13/34                             
## 14/34 [input_geneset_metrics2]

## 15/34                             
## 16/34 [input_geneset_metrics3]

## 17/34                             
## 18/34 [echart1d]                  
## 19/34 [echart2d]                  
## 20/34                             
## 21/34 [heatmap]                   
## 22/34                             
## 23/34 [effectsize]                
## 24/34                             
## 25/34 [results_table]             
## 26/34                             
## 27/34 [results_table_complete]    
## 28/34                             
## 29/34 [detailed_geneset_reports1d]

## 30/34                             
## 31/34 [detailed_geneset_reports2d]
## 32/34                             
## 33/34 [session_info]              
## 34/34

## output file: /home/mark.ziemann@domain.internal.burnet.edu.au/projects/lung_brain/mitch.knit.md

## /usr/bin/pandoc +RTS -K512m -RTS /home/mark.ziemann@domain.internal.burnet.edu.au/projects/lung_brain/mitch.knit.md --to html4 --from markdown+autolink_bare_uris+tex_math_single_backslash --output /tmp/RtmpXoIrRA/mitch_report.html --lua-filter /usr/local/lib/R/site-library/rmarkdown/rmarkdown/lua/pagebreak.lua --lua-filter /usr/local/lib/R/site-library/rmarkdown/rmarkdown/lua/latex-div.lua --self-contained --variable bs3=TRUE --section-divs --template /usr/local/lib/R/site-library/rmarkdown/rmd/h/default.html --no-highlight --variable highlightjs=1 --variable theme=bootstrap --mathjax --variable 'mathjax-url=https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML' --include-in-header /tmp/RtmpXoIrRA/rmarkdown-str2d120563cf734.html

## 
## Output created: /tmp/RtmpXoIrRA/mitch_report.html

## [1] TRUE

write.table(mres3_o$enrichment_result,file="mitch3_o.tsv",quote=FALSE,sep="\t",row.names=FALSE)

m3_o_top_up <- head(subset(m3_o_top,s.dist>0),10)[,"s.dist"]
names(m3_o_top_up) <- head(subset(m3_o_top,s.dist>0),10)[,"set"]
m3_o_top_dn <- head(subset(m3_o_top,s.dist<0),10)[,"s.dist"]
names(m3_o_top_dn) <- head(subset(m3_o_top,s.dist<0),10)[,"set"]
m3_o_top_updn <- c(m3_o_top_up,m3_o_top_dn)
m3_o_top_updn <- m3_o_top_updn[order(m3_o_top_updn)]

par(mar=c(5,25,3,1))
barplot(m3_o_top_updn,horiz=TRUE,las=1,col="darkgray",
  xlab="Enrichment score",cex.names=0.8,xlim=c(-1,1),
  main="Effect of LPS in hip")
grid()

pdf("plot28_mitch3ova01.pdf")
par(mar=c(5,25,3,1))
barplot(m3_o_top_updn,horiz=TRUE,las=1,col="darkgray",
  xlab="Enrichment score",cex.names=0.8,xlim=c(-1,1),
  main="Effect of LPS in hip")
grid()
dev.off()

## X11cairo 
##        2

Mitch 3 euler.

m3_l_up <- subset(m3_l_top,s.dist>0)$set
m3_l_dn <- subset(m3_l_top,s.dist<0)$set
m3_o_up <- subset(m3_o_top,s.dist>0)$set
m3_o_dn <- subset(m3_o_top,s.dist<0)$set

par(mar=c(2,2,2,2))

v0 <- list("LPS up"=m3_l_up,
  "LPS dn"=m3_l_dn,
  "OVA up"=m3_o_up,
  "OVA dn"=m3_o_dn)
plot(euler(v0),quantities = TRUE, edges = "gray", main="effect of treatments on hip")

pdf("plot29_mitch3lpsovaeuler01.pdf")
plot(euler(v0),quantities = TRUE, edges = "gray", main="effect of treatments on hip")
dev.off()

## X11cairo 
##        2

Two dimensional mitch analysis in hip.

ml <- list("LPS"=dge3_l,"OVA"=dge3_o)
m3 <- mitch_import(ml, DEtype="deseq2",geneTable=gt)

## Note: Mean no. genes in input = 16940

## Note: no. genes in output = 16806

## Note: estimated proportion of input genes in output = 0.992

head(m3)

##                      LPS        OVA
## 0610009B22Rik -3.3814425 -2.2697336
## 0610009E02Rik -0.3557659 -0.3519839
## 0610009L18Rik -0.8507498 -0.9139963
## 0610010K14Rik  1.2510787 -0.1751685
## 0610012G03Rik  3.9729326  0.5678859
## 0610030E20Rik  0.1581737  1.4359134

mres3 <- mitch_calc(m3, genesets, priority="effect")

## Note: Enrichments with large effect sizes may not be 
##             statistically significant.

head(mres3$enrichment_result,20) %>%
  kbl(caption = "Top gene pathway differences in hip for LPS and OVA") %>%
  kable_paper("hover", full_width = F)

Top gene pathway differences in hip for LPS and OVA

	set	setSize	pMANOVA	s.LPS	s.OVA	p.LPS	p.OVA	s.dist	SD	p.adjustMANOVA
1048	SYNTHESIS SECRETION AND INACTIVATION OF GLUCAGON LIKE PEPTIDE 1 GLP 1	12	0.0004711	-0.5305962	-0.6525049	0.0014592	0.0000907	0.8410083	0.0862024	0.0034450
438	INCRETIN SYNTHESIS SECRETION AND INACTIVATION	13	0.0002917	-0.5175096	-0.6460613	0.0012342	0.0000549	0.8277750	0.0908998	0.0022450
1047	SYNTHESIS SECRETION AND DEACYLATION OF GHRELIN	12	0.0065634	-0.4546763	-0.5267457	0.0063878	0.0015800	0.6958387	0.0509608	0.0309644
35	ADENYLATE CYCLASE ACTIVATING PATHWAY	10	0.0171862	0.4671231	0.5138724	0.0105326	0.0048948	0.6944558	0.0330567	0.0652851
66	APOPTOSIS INDUCED DNA FRAGMENTATION	10	0.0224696	-0.4819124	-0.4765182	0.0083192	0.0090730	0.6777235	0.0038142	0.0806425
1057	TERMINATION OF O GLYCAN BIOSYNTHESIS	10	0.0215017	0.4988092	0.4559300	0.0063070	0.0125414	0.6757831	0.0303202	0.0781274
125	CD28 DEPENDENT VAV1 PATHWAY	11	0.0181225	-0.4389564	-0.4879645	0.0117076	0.0050733	0.6563475	0.0346540	0.0681779
191	CROSSLINKING OF COLLAGEN FIBRILS	15	0.0041689	0.4936494	0.4063486	0.0009319	0.0064350	0.6393817	0.0617309	0.0213323
1029	SYNTHESIS OF ACTIVE UBIQUITIN ROLES OF E1 AND E2 ENZYMES	29	0.0000216	-0.4019070	-0.4972633	0.0001797	0.0000036	0.6393747	0.0674271	0.0002274
371	GENE AND PROTEIN EXPRESSION BY JAK STAT SIGNALING AFTER INTERLEUKIN 12 STIMULATION	31	0.0000000	-0.2950993	-0.5535638	0.0044644	0.0000001	0.6273089	0.1827620	0.0000005
737	PROCESSING AND ACTIVATION OF SUMO	10	0.0295199	-0.4807692	-0.3557395	0.0084741	0.0514328	0.5980716	0.0884094	0.1001110
26	ACTIVATION OF SMO	16	0.0090898	0.4320057	0.4081596	0.0027736	0.0047046	0.5943258	0.0168617	0.0398317
28	ACTIVATION OF THE MRNA UPON BINDING OF THE CAP BINDING COMPLEX AND EIFS AND SUBSEQUENT BINDING TO 43S	59	0.0000000	-0.2071881	-0.5519440	0.0059283	0.0000000	0.5895499	0.2437792	0.0000000
303	EUKARYOTIC TRANSLATION ELONGATION	87	0.0000000	-0.1277733	-0.5730908	0.0395070	0.0000000	0.5871619	0.3148871	0.0000000
189	CRMPS IN SEMA3A SIGNALING	16	0.0021195	0.3210616	0.4888699	0.0261943	0.0007100	0.5848712	0.1186583	0.0125242
201	CYTOSOLIC IRON SULFUR CLUSTER ASSEMBLY	13	0.0204852	0.4462070	0.3755457	0.0053429	0.0190581	0.5832111	0.0499651	0.0756080
910	SEMA3A PLEXIN REPULSION SIGNALING BY INHIBITING INTEGRIN ADHESION	14	0.0150385	0.3653696	0.4472283	0.0179378	0.0037638	0.5775016	0.0578828	0.0592426
304	EUKARYOTIC TRANSLATION INITIATION	114	0.0000000	-0.1675348	-0.5480796	0.0020155	0.0000000	0.5731135	0.2690858	0.0000000
1006	SRP DEPENDENT COTRANSLATIONAL PROTEIN TARGETING TO MEMBRANE	106	0.0000000	-0.1731850	-0.5408361	0.0020785	0.0000000	0.5678879	0.2599686	0.0000000
146	CHK1 CHK2 CDS1 MEDIATED INACTIVATION OF CYCLIN B CDK1 COMPLEX	10	0.0707565	-0.3833055	-0.4123005	0.0358339	0.0239710	0.5629519	0.0205026	0.1911896

m3_top <- subset(mres3$enrichment_result,p.adjustMANOVA<0.05)
m3_up <- subset(m3_top,s.dist>0)$set
m3_dn <- subset(m3_top,s.dist<0)$set
mitch_report(mres3,outfile="mitch3.html",overwrite=TRUE)

## Note: overwriting existing report

## Dataset saved as " /tmp/RtmpXoIrRA/mitch3.rds ".

## 
## 
## processing file: mitch.Rmd

## 1/34                             
## 2/34 [checklibraries]            
## 3/34                             
## 4/34 [peek]                      
## 5/34                             
## 6/34 [metrics]                   
## 7/34                             
## 8/34 [scatterplot]

## 9/34                             
## 10/34 [contourplot]

## 11/34                             
## 12/34 [input_geneset_metrics1]    
## 13/34                             
## 14/34 [input_geneset_metrics2]

## 15/34                             
## 16/34 [input_geneset_metrics3]

## 17/34                             
## 18/34 [echart1d]                  
## 19/34 [echart2d]                  
## 20/34                             
## 21/34 [heatmap]

## 22/34                             
## 23/34 [effectsize]                
## 24/34                             
## 25/34 [results_table]             
## 26/34                             
## 27/34 [results_table_complete]    
## 28/34                             
## 29/34 [detailed_geneset_reports1d]
## 30/34                             
## 31/34 [detailed_geneset_reports2d]

## 32/34                             
## 33/34 [session_info]              
## 34/34

## output file: /home/mark.ziemann@domain.internal.burnet.edu.au/projects/lung_brain/mitch.knit.md

## /usr/bin/pandoc +RTS -K512m -RTS /home/mark.ziemann@domain.internal.burnet.edu.au/projects/lung_brain/mitch.knit.md --to html4 --from markdown+autolink_bare_uris+tex_math_single_backslash --output /tmp/RtmpXoIrRA/mitch_report.html --lua-filter /usr/local/lib/R/site-library/rmarkdown/rmarkdown/lua/pagebreak.lua --lua-filter /usr/local/lib/R/site-library/rmarkdown/rmarkdown/lua/latex-div.lua --self-contained --variable bs3=TRUE --section-divs --template /usr/local/lib/R/site-library/rmarkdown/rmd/h/default.html --no-highlight --variable highlightjs=1 --variable theme=bootstrap --mathjax --variable 'mathjax-url=https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML' --include-in-header /tmp/RtmpXoIrRA/rmarkdown-str2d1205d1ea015.html

## 
## Output created: /tmp/RtmpXoIrRA/mitch_report.html

## [1] TRUE

write.table(mres3$enrichment_result,file="mitch3.tsv",quote=FALSE,sep="\t",row.names=FALSE)

m3_top_up <- head(subset(m3_top,s.dist>0),10)[,"s.dist"]
names(m3_top_up) <- head(subset(m3_top,s.dist>0),10)[,"set"]
m3_top_dn <- head(subset(m3_top,s.dist<0),10)[,"s.dist"]
names(m3_top_dn) <- head(subset(m3_top,s.dist<0),10)[,"set"]
m3_top_updn <- c(m3_top_up,m3_top_dn)
m3_top_updn <- m3_top_updn[order(m3_top_updn)]

# heatmap
mr3 <- head(subset(mres3$enrichment_result,p.adjustMANOVA<0.05),30)
mr3 <- as.matrix(mr3[,4:5])
rownames(mr3) <- head(mres3$enrichment_result,30)$set

colfunc <- colorRampPalette(c("blue", "white", "red"))

heatmap.2(mr3,trace="none",col=colfunc(25),scale="none", margins = c(10,28), cexRow=0.7,
  main="Top ranked pathways in hip" , cexCol=0.9 )

pdf("plot30_mitch3_heatmap01.pdf")
heatmap.2(mr3,trace="none",col=colfunc(25),scale="none", margins = c(10,28), cexRow=0.7,
  main="Top ranked pathways in hip" , cexCol=0.9 )
dev.off()

## X11cairo 
##        2

Need to make a scatterplot to highlight the following:

• Serotonin Neurotransmitter Release Cycle (DN)
• Class C3 metabotropic glutamate pheromone receptors (DN)
• Regulation of commissural axon pathfinding by SLIT AND robo (DN)
• Reduction of Cytosolic Ca (DN)
• YAP1 and WWTR1 TAZ STIMULATED GENE EXPRESSION (UP)
• Activation of the mRNA upon binding (UP)
• Regulation of expression of slits and robs (UP)

par(mar=c(4,4,2,1))

a <- mres3$enrichment_result
plot(a$s.LPS , a$s.OVA,pch=19,col="gray")
abline(h=0,lty=2)
abline(v=0,lty=2)
as <- subset(a,p.adjustMANOVA<0.05)
points(as$s.LPS , as$s.OVA,pch=19,col="red")

abline(h=0.2,lty=2,col="red")
abline(v=0.2,lty=2,col="red")
abline(h=-0.2,lty=2,col="red")
abline(v=-0.2,lty=2,col="red")

mysets <- c("SEROTONIN NEUROTRANSMITTER RELEASE CYCLE",
  "CLASS C 3 METABOTROPIC GLUTAMATE PHEROMONE RECEPTORS",
  "REGULATION OF COMMISSURAL AXON PATHFINDING BY SLIT AND ROBO",
  "REDUCTION OF CYTOSOLIC CA LEVELS",
  "YAP1 AND WWTR1 TAZ STIMULATED GENE EXPRESSION",
  "ACTIVATION OF THE MRNA UPON BINDING OF THE CAP BINDING COMPLEX AND EIFS AND SUBSEQUENT BINDING TO 43S",
  "REGULATION OF EXPRESSION OF SLITS AND ROBOS")

a2 <- a[which(a$set %in% mysets),]

points(a2$s.LPS , a2$s.OVA+0.025,pch=25,col="red")

pdf("plot30b_mitch3_scatter01.pdf")

a <- mres3$enrichment_result
plot(a$s.LPS , a$s.OVA,pch=19,col="gray")
abline(h=0,lty=2)
abline(v=0,lty=2)
as <- subset(a,p.adjustMANOVA<0.05)
points(as$s.LPS , as$s.OVA,pch=19,col="red")

abline(h=0.2,lty=2,col="red")
abline(v=0.2,lty=2,col="red")
abline(h=-0.2,lty=2,col="red")
abline(v=-0.2,lty=2,col="red")

mysets <- c("SEROTONIN NEUROTRANSMITTER RELEASE CYCLE",
  "CLASS C 3 METABOTROPIC GLUTAMATE PHEROMONE RECEPTORS",
  "REGULATION OF COMMISSURAL AXON PATHFINDING BY SLIT AND ROBO",
  "REDUCTION OF CYTOSOLIC CA LEVELS",
  "YAP1 AND WWTR1 TAZ STIMULATED GENE EXPRESSION",
  "ACTIVATION OF THE MRNA UPON BINDING OF THE CAP BINDING COMPLEX AND EIFS AND SUBSEQUENT BINDING TO 43S",
  "REGULATION OF EXPRESSION OF SLITS AND ROBOS")

a2 <- a[which(a$set %in% mysets),]

points(a2$s.LPS , a2$s.OVA+0.025,pch=25,col="red")

dev.off()

## X11cairo 
##        2

Bespoke analysis

As discussed before, could you please generate a Combined Heatmap with total 4 columns (Amygdala and Hippocampus): (common gene sets between these 2 brain regions; these will be (Figure 2A in the paper showing similar directionality); (Figure 2B in the paper showing opposite directionality) (in PDF format).

ml <- list( "amy LPS"=dge2_l,"amy OVA"=dge2_o ,"hip LPS"=dge3_l,"hip OVA"=dge3_o)
m23 <- mitch_import(ml, DEtype="deseq2",geneTable=gt)

## Note: Mean no. genes in input = 16830.5

## Note: no. genes in output = 16173

## Note: estimated proportion of input genes in output = 0.961

head(m23)

##                  amy LPS      amy OVA    hip LPS    hip OVA
## 0610009B22Rik  0.2684894  1.831236577 -3.3814425 -2.2697336
## 0610009E02Rik -0.7521235 -0.103702849 -0.3557659 -0.3519839
## 0610009L18Rik -1.1838923 -0.075637695 -0.8507498 -0.9139963
## 0610010K14Rik -0.0433030  0.417670579  1.2510787 -0.1751685
## 0610012G03Rik -0.1930476  0.008709563  3.9729326  0.5678859
## 0610030E20Rik  1.0496464 -1.609523779  0.1581737  1.4359134

mres23 <- mitch_calc(m23, genesets, priority="effect")

## Note: Enrichments with large effect sizes may not be 
##             statistically significant.

head(mres23$enrichment_result,20) %>%
  kbl(caption = "Top gene pathway differences in amy and hip for LPS and OVA") %>%
  kable_paper("hover", full_width = F)

Top gene pathway differences in amy and hip for LPS and OVA

	set	setSize	pMANOVA	s.amy.LPS	s.amy.OVA	s.hip.LPS	s.hip.OVA	p.amy.LPS	p.amy.OVA	p.hip.LPS	p.hip.OVA	s.dist	SD	p.adjustMANOVA
301	EUKARYOTIC TRANSLATION ELONGATION	87	0.0000000	0.4431211	0.5128712	-0.1299424	-0.5709998	0.0000000	0.0000000	0.0362948	0.0000000	0.8957226	0.5118777	0.0000000
123	CD28 DEPENDENT VAV1 PATHWAY	10	0.0183799	-0.4087113	0.4065829	-0.4448184	-0.5007115	0.0252268	0.0259970	0.0148650	0.0061109	0.8837024	0.4306646	0.0538435
435	INCRETIN SYNTHESIS SECRETION AND INACTIVATION	13	0.0025031	-0.1326447	0.2766946	-0.5146230	-0.6433264	0.4076831	0.0841302	0.0013145	0.0000590	0.8791246	0.4146734	0.0110268
1043	SYNTHESIS SECRETION AND INACTIVATION OF GLUCAGON LIKE PEPTIDE 1 GLP 1	12	0.0032397	-0.1456902	0.2194274	-0.5278036	-0.6498257	0.3822490	0.1881708	0.0015461	0.0000969	0.8776241	0.3939457	0.0135532
189	CROSSLINKING OF COLLAGEN FIBRILS	13	0.0061351	0.4078065	-0.2607483	0.5699067	0.4334158	0.0109032	0.1035973	0.0003736	0.0068163	0.8642562	0.3724222	0.0237839
1042	SYNTHESIS SECRETION AND DEACYLATION OF GHRELIN	12	0.0124633	-0.0397871	0.4719489	-0.4530660	-0.5257719	0.8114033	0.0046435	0.0065780	0.0016121	0.8402522	0.4587338	0.0394954
1024	SYNTHESIS OF ACTIVE UBIQUITIN ROLES OF E1 AND E2 ENZYMES	29	0.0000035	-0.2155770	0.4927890	-0.4020582	-0.4970738	0.0445440	0.0000044	0.0001787	0.0000036	0.8354924	0.4477170	0.0000344
1092	TRAFFICKING AND PROCESSING OF ENDOSOMAL TLR	11	0.0018248	0.0791981	0.6517983	-0.1981303	-0.4591128	0.6492746	0.0001814	0.2552376	0.0083745	0.8253203	0.4760230	0.0083883
831	RESPONSE OF EIF2AK4 GCN2 TO AMINO ACID DEFICIENCY	94	0.0000000	0.3802462	0.4655696	-0.1529020	-0.5333533	0.0000000	0.0000000	0.0104597	0.0000000	0.8180385	0.4700433	0.0000000
1001	SRP DEPENDENT COTRANSLATIONAL PROTEIN TARGETING TO MEMBRANE	106	0.0000000	0.3245231	0.4885403	-0.1749103	-0.5390799	0.0000000	0.0000000	0.0018767	0.0000000	0.8155903	0.4700120	0.0000000
27	ACTIVATION OF THE MRNA UPON BINDING OF THE CAP BINDING COMPLEX AND EIFS AND SUBSEQUENT BINDING TO 43S	59	0.0000000	0.2704523	0.4778769	-0.2096777	-0.5503983	0.0003281	0.0000000	0.0053593	0.0000000	0.8052415	0.4648940	0.0000000
39	ADP SIGNALLING THROUGH P2Y PURINOCEPTOR 12	19	0.0001492	-0.2219688	0.5421111	-0.3139258	-0.4527736	0.0939762	0.0000429	0.0178499	0.0006338	0.8041816	0.4460399	0.0009968
302	EUKARYOTIC TRANSLATION INITIATION	114	0.0000000	0.3058939	0.4722334	-0.1698681	-0.5466301	0.0000000	0.0000000	0.0017448	0.0000000	0.8026426	0.4630643	0.0000000
169	COMPLEX I BIOGENESIS	56	0.0000000	0.2955575	0.5340767	-0.1501387	-0.4925123	0.0001309	0.0000000	0.0520631	0.0000000	0.7985626	0.4578797	0.0000000
187	CRMPS IN SEMA3A SIGNALING	16	0.0015607	-0.0763291	-0.5108622	0.3177957	0.4846584	0.5971360	0.0004031	0.0277602	0.0007894	0.7763338	0.4438880	0.0073013
533	MET ACTIVATES RAP1 AND RAC1	11	0.0065347	-0.5671890	0.2437480	-0.3921657	-0.2327120	0.0011243	0.1616082	0.0243187	0.1814488	0.7675059	0.3484433	0.0248363
903	SELENOAMINO ACID METABOLISM	107	0.0000000	0.3320346	0.4161793	-0.1438808	-0.4953213	0.0000000	0.0000000	0.0101887	0.0000000	0.7412807	0.4268201	0.0000000
1133	TRIGLYCERIDE CATABOLISM	14	0.0061623	-0.0518950	0.5240512	-0.2593247	-0.4388178	0.7367599	0.0006860	0.0929899	0.0044724	0.7328937	0.4180772	0.0238098
34	ADENYLATE CYCLASE ACTIVATING PATHWAY	10	0.0797587	0.2150096	-0.1373136	0.4596671	0.5097074	0.2391053	0.4521632	0.0118368	0.0052539	0.7322429	0.2955732	0.1573679
1041	SYNTHESIS OF VERY LONG CHAIN FATTY ACYL COAS	20	0.0007316	-0.0064941	0.5334922	-0.3433913	-0.3620813	0.9599084	0.0000362	0.0078527	0.0050620	0.7305317	0.4186141	0.0038325

#mitch_report(mres23,outfile="mitch23.html",overwrite=TRUE)
write.table(mres23$enrichment_result,file="mitch23.tsv",quote=FALSE,sep="\t",row.names=FALSE)

# heatmap
mr23 <- head(subset(mres23$enrichment_result,p.adjustMANOVA<0.05),30)
mr23 <- as.matrix(mr23[,4:7])
rownames(mr23) <- head(mres23$enrichment_result,30)$set

colfunc <- colorRampPalette(c("blue", "white", "red"))

heatmap.2(mr23,trace="none",col=colfunc(25),scale="none", margins = c(10,25), cexRow=0.65,
  main="Top ranked pathways in amy/hip" , cexCol=0.9 )

pdf("plot40_mitch23_heatmap01.pdf")
heatmap.2(mr23,trace="none",col=colfunc(25),scale="none", margins = c(10,25), cexRow=0.65,
  main="Top ranked pathways in amy/hip" , cexCol=0.9 )
dev.off()

## X11cairo 
##        2

# now select 30 sets from mres2 and mres3
n=17
mysets <- union( head(mres2$enrichment_result,n)$set , head(mres3$enrichment_result,n)$set )
mr23 <- mres23$enrichment_result[which(mres23$enrichment_result$set %in% mysets),]
rownames(mr23) <- mr23$set
mr23 <- as.matrix(mr23[,4:7])

heatmap.2(mr23,trace="none",col=colfunc(25),scale="none", margins = c(10,25), cexRow=0.65,
  main="Top ranked pathways in amy/hip" , cexCol=0.9 )

pdf("plot41_mitch23_heatmap01.pdf")
heatmap.2(mr23,trace="none",col=colfunc(25),scale="none", margins = c(10,25), cexRow=0.65,
  main="Top ranked pathways in amy/hip" , cexCol=0.9 )
dev.off()

## X11cairo 
##        2

Discordant analysis.

mres23d <- mitch_calc(m23, genesets, priority="SD")

## Note: Prioritisation by SD after selecting sets with 
##             p.adjustMANOVA<=0.05.

head(mres23d$enrichment_result,20) %>%
  kbl(caption = "Top gene pathway differences in amy and hip for LPS and OVA (discordant)") %>%
  kable_paper("hover", full_width = F)

Top gene pathway differences in amy and hip for LPS and OVA (discordant)

	set	setSize	pMANOVA	s.amy.LPS	s.amy.OVA	s.hip.LPS	s.hip.OVA	p.amy.LPS	p.amy.OVA	p.hip.LPS	p.hip.OVA	s.dist	SD	p.adjustMANOVA
301	EUKARYOTIC TRANSLATION ELONGATION	87	0.0000000	0.4431211	0.5128712	-0.1299424	-0.5709998	0.0000000	0.0000000	0.0362948	0.0000000	0.8957226	0.5118777	0.0000000
1092	TRAFFICKING AND PROCESSING OF ENDOSOMAL TLR	11	0.0018248	0.0791981	0.6517983	-0.1981303	-0.4591128	0.6492746	0.0001814	0.2552376	0.0083745	0.8253203	0.4760230	0.0083883
831	RESPONSE OF EIF2AK4 GCN2 TO AMINO ACID DEFICIENCY	94	0.0000000	0.3802462	0.4655696	-0.1529020	-0.5333533	0.0000000	0.0000000	0.0104597	0.0000000	0.8180385	0.4700433	0.0000000
1001	SRP DEPENDENT COTRANSLATIONAL PROTEIN TARGETING TO MEMBRANE	106	0.0000000	0.3245231	0.4885403	-0.1749103	-0.5390799	0.0000000	0.0000000	0.0018767	0.0000000	0.8155903	0.4700120	0.0000000
27	ACTIVATION OF THE MRNA UPON BINDING OF THE CAP BINDING COMPLEX AND EIFS AND SUBSEQUENT BINDING TO 43S	59	0.0000000	0.2704523	0.4778769	-0.2096777	-0.5503983	0.0003281	0.0000000	0.0053593	0.0000000	0.8052415	0.4648940	0.0000000
302	EUKARYOTIC TRANSLATION INITIATION	114	0.0000000	0.3058939	0.4722334	-0.1698681	-0.5466301	0.0000000	0.0000000	0.0017448	0.0000000	0.8026426	0.4630643	0.0000000
1042	SYNTHESIS SECRETION AND DEACYLATION OF GHRELIN	12	0.0124633	-0.0397871	0.4719489	-0.4530660	-0.5257719	0.8114033	0.0046435	0.0065780	0.0016121	0.8402522	0.4587338	0.0394954
169	COMPLEX I BIOGENESIS	56	0.0000000	0.2955575	0.5340767	-0.1501387	-0.4925123	0.0001309	0.0000000	0.0520631	0.0000000	0.7985626	0.4578797	0.0000000
1024	SYNTHESIS OF ACTIVE UBIQUITIN ROLES OF E1 AND E2 ENZYMES	29	0.0000035	-0.2155770	0.4927890	-0.4020582	-0.4970738	0.0445440	0.0000044	0.0001787	0.0000036	0.8354924	0.4477170	0.0000344
39	ADP SIGNALLING THROUGH P2Y PURINOCEPTOR 12	19	0.0001492	-0.2219688	0.5421111	-0.3139258	-0.4527736	0.0939762	0.0000429	0.0178499	0.0006338	0.8041816	0.4460399	0.0009968
187	CRMPS IN SEMA3A SIGNALING	16	0.0015607	-0.0763291	-0.5108622	0.3177957	0.4846584	0.5971360	0.0004031	0.0277602	0.0007894	0.7763338	0.4438880	0.0073013
903	SELENOAMINO ACID METABOLISM	107	0.0000000	0.3320346	0.4161793	-0.1438808	-0.4953213	0.0000000	0.0000000	0.0101887	0.0000000	0.7412807	0.4268201	0.0000000
1041	SYNTHESIS OF VERY LONG CHAIN FATTY ACYL COAS	20	0.0007316	-0.0064941	0.5334922	-0.3433913	-0.3620813	0.9599084	0.0000362	0.0078527	0.0050620	0.7305317	0.4186141	0.0038325
1133	TRIGLYCERIDE CATABOLISM	14	0.0061623	-0.0518950	0.5240512	-0.2593247	-0.4388178	0.7367599	0.0006860	0.0929899	0.0044724	0.7328937	0.4180772	0.0238098
828	RESPIRATORY ELECTRON TRANSPORT	102	0.0000000	0.2105839	0.5099833	-0.1124922	-0.4557265	0.0002401	0.0000000	0.0498137	0.0000000	0.7244099	0.4159195	0.0000000
435	INCRETIN SYNTHESIS SECRETION AND INACTIVATION	13	0.0025031	-0.1326447	0.2766946	-0.5146230	-0.6433264	0.4076831	0.0841302	0.0013145	0.0000590	0.8791246	0.4146734	0.0110268
829	RESPIRATORY ELECTRON TRANSPORT ATP SYNTHESIS BY CHEMIOSMOTIC COUPLING AND HEAT PRODUCTION BY UNCOUPLING PROTEINS	125	0.0000000	0.2160100	0.5027657	-0.1003250	-0.4358933	0.0000308	0.0000000	0.0529626	0.0000000	0.7067544	0.4046274	0.0000000
746	PROTEIN METHYLATION	17	0.0027758	0.0034516	0.4506648	-0.2672983	-0.4688406	0.9803463	0.0012955	0.0564109	0.0008176	0.7031149	0.3976960	0.0119567
628	NONSENSE MEDIATED DECAY NMD	109	0.0000000	0.3529506	0.3701638	-0.1350470	-0.4432968	0.0000000	0.0000000	0.0149386	0.0000000	0.6901776	0.3962766	0.0000000
1043	SYNTHESIS SECRETION AND INACTIVATION OF GLUCAGON LIKE PEPTIDE 1 GLP 1	12	0.0032397	-0.1456902	0.2194274	-0.5278036	-0.6498257	0.3822490	0.1881708	0.0015461	0.0000969	0.8776241	0.3939457	0.0135532

# heatmap
mr23d <- head(subset(mres23d$enrichment_result,p.adjustMANOVA<0.05),30)
rownames(mr23d) <- mr23d$set
mr23d <- as.matrix(mr23d[,4:7])

heatmap.2(mr23d,trace="none",col=colfunc(25),scale="none", margins = c(10,25), cexRow=0.65,
  main="Top ranked pathways in amy/hip (discordant)" , cexCol=0.9 )

pdf("plot42_mitch23d_heatmap01.pdf")
heatmap.2(mr23d,trace="none",col=colfunc(25),scale="none", margins = c(10,25), cexRow=0.65,
  main="Top ranked pathways in amy/hip (discordant)" , cexCol=0.9 )
dev.off()

## X11cairo 
##        2

ml2d <- list("LPS"=dge2_l,"OVA"=dge2_o)
m2d <- mitch_import(ml2d, DEtype="deseq2",geneTable=gt)

## Note: Mean no. genes in input = 16721

## Note: no. genes in output = 16550

## Note: estimated proportion of input genes in output = 0.99

mres2d <- mitch_calc(m2d, genesets, priority="SD")

## Note: Prioritisation by SD after selecting sets with 
##             p.adjustMANOVA<=0.05.

head(mres2d$enrichment_result)

##                                                                    set setSize
## 123                                        CD28 DEPENDENT VAV1 PATHWAY      10
## 534                                        MET ACTIVATES RAP1 AND RAC1      11
## 185  CREB1 PHOSPHORYLATION THROUGH THE ACTIVATION OF ADENYLATE CYCLASE      11
## 39                          ADP SIGNALLING THROUGH P2Y PURINOCEPTOR 12      19
## 164                                       COLLAGEN CHAIN TRIMERIZATION      39
## 1027          SYNTHESIS OF ACTIVE UBIQUITIN ROLES OF E1 AND E2 ENZYMES      29
##           pMANOVA      s.LPS      s.OVA        p.LPS        p.OVA    s.dist
## 123  4.559178e-03 -0.4094075  0.4085490 2.497815e-02 2.528380e-02 0.5783829
## 534  1.289999e-03 -0.5676115  0.2448043 1.114620e-03 1.597948e-01 0.6181521
## 185  4.634753e-03 -0.4323170  0.3418202 1.303944e-02 4.965539e-02 0.5511252
## 39   3.061078e-05 -0.2223605  0.5448806 9.339280e-02 3.920153e-05 0.5885058
## 164  9.420970e-09  0.4232656 -0.3400608 4.784541e-06 2.383575e-04 0.5429504
## 1027 1.507292e-06 -0.2160197  0.4954426 4.410379e-02 3.869540e-06 0.5404886
##             SD p.adjustMANOVA
## 123  0.5783826   2.438533e-02
## 534  0.5744647   8.796134e-03
## 185  0.5473976   2.445304e-02
## 39   0.5425214   4.249067e-04
## 164  0.5397533   3.328743e-07
## 1027 0.5030798   3.586739e-05

ml3d <- list("LPS"=dge3_l,"OVA"=dge3_o)
m3d <- mitch_import(ml3d, DEtype="deseq2",geneTable=gt)

## Note: Mean no. genes in input = 16940

## Note: no. genes in output = 16806

## Note: estimated proportion of input genes in output = 0.992

mres3d <- mitch_calc(m3d, genesets, priority="SD")

## Note: Prioritisation by SD after selecting sets with 
##             p.adjustMANOVA<=0.05.

head(mres3d$enrichment_result)

##                                                                     set setSize
## 1131                      TRANSPORT OF CONNEXONS TO THE PLASMA MEMBRANE      13
## 725                             POST CHAPERONIN TUBULIN FOLDING PATHWAY      17
## 367                                               GAP JUNCTION ASSEMBLY      22
## 336              FORMATION OF TUBULIN FOLDING INTERMEDIATES BY CCT TRIC      19
## 303                                   EUKARYOTIC TRANSLATION ELONGATION      87
## 180  COOPERATION OF PREFOLDIN AND TRIC CCT IN ACTIN AND TUBULIN FOLDING      26
##           pMANOVA      s.LPS       s.OVA        p.LPS        p.OVA    s.dist
## 1131 7.127689e-08  0.4559180 -0.09106816 0.0044239280 5.697220e-01 0.4649243
## 725  2.780070e-10  0.5110664 -0.03343576 0.0002639311 8.113842e-01 0.5121590
## 367  1.220899e-10  0.2929142 -0.21280549 0.0174015517 8.404988e-02 0.3620565
## 336  8.405634e-09  0.1601584 -0.32868479 0.2268849761 1.313252e-02 0.3656288
## 303  1.028286e-38 -0.1277733 -0.57309084 0.0395069625 2.338602e-20 0.5871619
## 180  2.520725e-10  0.0277299 -0.41375722 0.8066915685 2.603783e-04 0.4146854
##             SD p.adjustMANOVA
## 1131 0.3867776   1.323714e-06
## 725  0.3850212   9.035226e-09
## 367  0.3575979   4.607908e-09
## 336  0.3456643   2.092466e-07
## 303  0.3148871   4.010316e-36
## 180  0.3121785   8.426423e-09

mysets <- union(head(mres2d$enrichment_result,15)$set , head(mres3d$enrichment_result,15)$set)
mr23d <- mres23d$enrichment_result[mres23d$enrichment_result$set %in% mysets,]
rownames(mr23d) <- mr23d$set
mr23d <- as.matrix(mr23d[,4:7])

heatmap.2(mr23d,trace="none",col=colfunc(25),scale="none", margins = c(10,25), cexRow=0.65,
  main="Top ranked pathways in amy/hip (discordant)" , cexCol=0.9 )

pdf("plot43_mitch23d_heatmap01.pdf")
heatmap.2(mr23d,trace="none",col=colfunc(25),scale="none", margins = c(10,25), cexRow=0.65,
  main="Top ranked pathways in amy/hip (discordant)" , cexCol=0.9 )
dev.off()

## X11cairo 
##        2

Mitch pathway analysis in PAG

Mitch for LPS in pag

m4_l <- mitch_import(dge4_l, DEtype="deseq2",geneTable=gt)

## The input is a single dataframe; one contrast only. Converting
##         it to a list for you.

## Note: Mean no. genes in input = 17612

## Note: no. genes in output = 17595

## Note: estimated proportion of input genes in output = 0.999

mres4_l <- mitch_calc(m4_l, genesets, priority="effect")

## Note: Enrichments with large effect sizes may not be
##             statistically significant.

head(mres4_l$enrichment_result,20) %>%
  kbl(caption = "Top gene pathway differences in pag for LPS") %>%
  kable_paper("hover", full_width = F)

Top gene pathway differences in pag for LPS

	set	setSize	pANOVA	s.dist	p.adjustANOVA
340	FORMATION OF TUBULIN FOLDING INTERMEDIATES BY CCT TRIC	19	0.0000001	0.7074360	0.0000055
47	ALPHA LINOLENIC OMEGA3 AND LINOLEIC OMEGA6 ACID METABOLISM	12	0.0002635	0.6082675	0.0039925
398	GOLGI CISTERNAE PERICENTRIOLAR STACK REORGANIZATION	12	0.0005667	0.5746554	0.0070265
757	PURINE RIBONUCLEOSIDE MONOPHOSPHATE BIOSYNTHESIS	12	0.0007868	0.5597073	0.0090868
860	RHOBTB3 ATPASE CYCLE	10	0.0024395	0.5534603	0.0211304
1138	TRANSPORT OF CONNEXONS TO THE PLASMA MEMBRANE	13	0.0008345	0.5351540	0.0093622
92	BBSOME MEDIATED CARGO TARGETING TO CILIUM	23	0.0000093	0.5340116	0.0002796
154	CITRIC ACID CYCLE TCA CYCLE	22	0.0000336	0.5107474	0.0008172
655	NUCLEOBASE BIOSYNTHESIS	15	0.0006664	0.5074630	0.0080107
505	KERATAN SULFATE BIOSYNTHESIS	24	0.0000253	0.4966469	0.0006480
182	COPI INDEPENDENT GOLGI TO ER RETROGRADE TRAFFIC	44	0.0000000	0.4935409	0.0000012
180	COOPERATION OF PREFOLDIN AND TRIC CCT IN ACTIN AND TUBULIN FOLDING	26	0.0000252	0.4773268	0.0006480
435	HSP90 CHAPERONE CYCLE FOR STEROID HORMONE RECEPTORS SHR	48	0.0000000	0.4652030	0.0000017
507	KERATAN SULFATE KERATIN METABOLISM	30	0.0000145	0.4573489	0.0004071
110	CALNEXIN CALRETICULIN CYCLE	26	0.0000588	0.4551548	0.0012132
391	GLYCOGEN STORAGE DISEASES	12	0.0064228	0.4543688	0.0422685
371	GAP JUNCTION ASSEMBLY	21	0.0003129	0.4543292	0.0046074
284	ENDOSOMAL VACUOLAR PATHWAY	10	0.0130917	-0.4531362	0.0679385
842	RETROGRADE NEUROTROPHIN SIGNALLING	13	0.0047622	0.4521407	0.0338362
392	GLYCOGEN SYNTHESIS	14	0.0036211	0.4490887	0.0273438

m4_l_top <- subset(mres4_l$enrichment_result,p.adjustANOVA<0.05)
m4_l_up <- subset(m4_l_top,s.dist>0)$set
m4_l_dn <- subset(m4_l_top,s.dist<0)$set
mitch_report(mres4_l,outfile="mitch4_l.html",overwrite=TRUE)

## Note: overwriting existing report

## Dataset saved as " /tmp/RtmpXoIrRA/mitch4_l.rds ".

## 
## 
## processing file: mitch.Rmd

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## 3/34                             
## 4/34 [peek]                      
## 5/34                             
## 6/34 [metrics]                   
## 7/34                             
## 8/34 [scatterplot]

## 9/34                             
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## 11/34                             
## 12/34 [input_geneset_metrics1]    
## 13/34                             
## 14/34 [input_geneset_metrics2]

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## 17/34                             
## 18/34 [echart1d]                  
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## 34/34

## output file: /home/mark.ziemann@domain.internal.burnet.edu.au/projects/lung_brain/mitch.knit.md

## /usr/bin/pandoc +RTS -K512m -RTS /home/mark.ziemann@domain.internal.burnet.edu.au/projects/lung_brain/mitch.knit.md --to html4 --from markdown+autolink_bare_uris+tex_math_single_backslash --output /tmp/RtmpXoIrRA/mitch_report.html --lua-filter /usr/local/lib/R/site-library/rmarkdown/rmarkdown/lua/pagebreak.lua --lua-filter /usr/local/lib/R/site-library/rmarkdown/rmarkdown/lua/latex-div.lua --self-contained --variable bs3=TRUE --section-divs --template /usr/local/lib/R/site-library/rmarkdown/rmd/h/default.html --no-highlight --variable highlightjs=1 --variable theme=bootstrap --mathjax --variable 'mathjax-url=https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML' --include-in-header /tmp/RtmpXoIrRA/rmarkdown-str2d12057395eba5.html

## 
## Output created: /tmp/RtmpXoIrRA/mitch_report.html

## [1] TRUE

write.table(mres4_l$enrichment_result,file="mitch4_l.tsv",quote=FALSE,sep="\t",row.names=FALSE)

m4_l_top_up <- head(subset(m4_l_top,s.dist>0),10)[,"s.dist"]
names(m4_l_top_up) <- head(subset(m4_l_top,s.dist>0),10)[,"set"]
m4_l_top_dn <- head(subset(m4_l_top,s.dist<0),10)[,"s.dist"]
names(m4_l_top_dn) <- head(subset(m4_l_top,s.dist<0),10)[,"set"]
m4_l_top_updn <- c(m4_l_top_up,m4_l_top_dn)
m4_l_top_updn <- m4_l_top_updn[order(m4_l_top_updn)]

par(mar=c(5,25,3,1))
barplot(m4_l_top_updn,horiz=TRUE,las=1,col="darkgray",
  xlab="Enrichment score",cex.names=0.8,xlim=c(-1,1),
  main="Effect of LPS in pag")
grid()

pdf("plot31_mitch4lps01.pdf")
par(mar=c(5,25,3,1))
barplot(m4_l_top_updn,horiz=TRUE,las=1,col="darkgray",
  xlab="Enrichment score",cex.names=0.8,xlim=c(-1,1),
  main="Effect of LPS in pag")
grid()
dev.off()

## X11cairo 
##        2

Mitch for OVA in pag

m4_o <- mitch_import(dge4_o, DEtype="deseq2",geneTable=gt)

## The input is a single dataframe; one contrast only. Converting
##         it to a list for you.

## Note: Mean no. genes in input = 17662

## Note: no. genes in output = 17643

## Note: estimated proportion of input genes in output = 0.999

mres4_o <- mitch_calc(m4_o, genesets, priority="effect")

## Note: Enrichments with large effect sizes may not be
##             statistically significant.

head(mres4_o$enrichment_result,20) %>%
  kbl(caption = "Top gene pathway differences in pag for OVA") %>%
  kable_paper("hover", full_width = F)

Top gene pathway differences in pag for OVA

	set	setSize	pANOVA	s.dist	p.adjustANOVA
154	CITRIC ACID CYCLE TCA CYCLE	22	0.0000462	0.5016845	0.0012106
663	OLFACTORY SIGNALING PATHWAY	37	0.0000001	-0.4989945	0.0000110
1046	SYNTHESIS OF PIPS AT THE LATE ENDOSOME MEMBRANE	11	0.0041951	0.4985357	0.0329455
47	ALPHA LINOLENIC OMEGA3 AND LINOLEIC OMEGA6 ACID METABOLISM	12	0.0030441	0.4940162	0.0257983
757	PURINE RIBONUCLEOSIDE MONOPHOSPHATE BIOSYNTHESIS	12	0.0036144	0.4851398	0.0295722
110	CALNEXIN CALRETICULIN CYCLE	26	0.0000234	0.4791744	0.0007879
293	ER QUALITY CONTROL COMPARTMENT ERQC	21	0.0001581	0.4762121	0.0029771
860	RHOBTB3 ATPASE CYCLE	10	0.0093462	0.4746555	0.0583746
104	BRANCHED CHAIN AMINO ACID CATABOLISM	21	0.0001917	0.4701266	0.0033698
1044	SYNTHESIS OF PIPS AT THE EARLY ENDOSOME MEMBRANE	16	0.0011386	0.4698261	0.0126839
448	INHIBITION OF REPLICATION INITIATION OF DAMAGED DNA BY RB1 E2F1	13	0.0039779	0.4613028	0.0316620
74	ASPARTATE AND ASPARAGINE METABOLISM	10	0.0124484	0.4564056	0.0711801
539	MET ACTIVATES RAP1 AND RAC1	10	0.0132401	0.4524017	0.0732248
92	BBSOME MEDIATED CARGO TARGETING TO CILIUM	23	0.0001740	0.4521640	0.0032034
842	RETROGRADE NEUROTROPHIN SIGNALLING	14	0.0048064	0.4352487	0.0362942
111	CAMK IV MEDIATED PHOSPHORYLATION OF CREB	10	0.0186908	0.4294675	0.0898684
907	SCAVENGING OF HEME FROM PLASMA	11	0.0143530	-0.4263220	0.0758201
1051	SYNTHESIS OF VERY LONG CHAIN FATTY ACYL COAS	20	0.0009820	0.4256710	0.0114298
594	N GLYCAN TRIMMING IN THE ER AND CALNEXIN CALRETICULIN CYCLE	35	0.0000156	0.4219640	0.0005974
1073	THE ROLE OF NEF IN HIV 1 REPLICATION AND DISEASE PATHOGENESIS	27	0.0001875	0.4152959	0.0033468

m4_o_top <- subset(mres4_o$enrichment_result,p.adjustANOVA<0.05)
m4_o_up <- subset(m4_o_top,s.dist>0)$set
m4_o_dn <- subset(m4_o_top,s.dist<0)$set
mitch_report(mres4_o,outfile="mitch4_o.html",overwrite=TRUE)

## Note: overwriting existing report

## Dataset saved as " /tmp/RtmpXoIrRA/mitch4_o.rds ".

## 
## 
## processing file: mitch.Rmd

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## 5/34                             
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## 7/34                             
## 8/34 [scatterplot]

## 9/34                             
## 10/34 [contourplot]               
## 11/34                             
## 12/34 [input_geneset_metrics1]    
## 13/34                             
## 14/34 [input_geneset_metrics2]

## 15/34                             
## 16/34 [input_geneset_metrics3]

## 17/34                             
## 18/34 [echart1d]                  
## 19/34 [echart2d]                  
## 20/34                             
## 21/34 [heatmap]                   
## 22/34                             
## 23/34 [effectsize]                
## 24/34                             
## 25/34 [results_table]             
## 26/34                             
## 27/34 [results_table_complete]    
## 28/34                             
## 29/34 [detailed_geneset_reports1d]

## 30/34                             
## 31/34 [detailed_geneset_reports2d]
## 32/34                             
## 33/34 [session_info]              
## 34/34

## output file: /home/mark.ziemann@domain.internal.burnet.edu.au/projects/lung_brain/mitch.knit.md

## /usr/bin/pandoc +RTS -K512m -RTS /home/mark.ziemann@domain.internal.burnet.edu.au/projects/lung_brain/mitch.knit.md --to html4 --from markdown+autolink_bare_uris+tex_math_single_backslash --output /tmp/RtmpXoIrRA/mitch_report.html --lua-filter /usr/local/lib/R/site-library/rmarkdown/rmarkdown/lua/pagebreak.lua --lua-filter /usr/local/lib/R/site-library/rmarkdown/rmarkdown/lua/latex-div.lua --self-contained --variable bs3=TRUE --section-divs --template /usr/local/lib/R/site-library/rmarkdown/rmd/h/default.html --no-highlight --variable highlightjs=1 --variable theme=bootstrap --mathjax --variable 'mathjax-url=https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML' --include-in-header /tmp/RtmpXoIrRA/rmarkdown-str2d12054998aaf8.html

## 
## Output created: /tmp/RtmpXoIrRA/mitch_report.html

## [1] TRUE

write.table(mres4_o$enrichment_result,file="mitch4_o.tsv",quote=FALSE,sep="\t",row.names=FALSE)

m4_o_top_up <- head(subset(m4_o_top,s.dist>0),10)[,"s.dist"]
names(m4_o_top_up) <- head(subset(m4_o_top,s.dist>0),10)[,"set"]
m4_o_top_dn <- head(subset(m4_o_top,s.dist<0),10)[,"s.dist"]
names(m4_o_top_dn) <- head(subset(m4_o_top,s.dist<0),10)[,"set"]
m4_o_top_updn <- c(m4_o_top_up,m4_o_top_dn)
m4_o_top_updn <- m4_o_top_updn[order(m4_o_top_updn)]

par(mar=c(5,25,3,1))
barplot(m4_o_top_updn,horiz=TRUE,las=1,col="darkgray",
  xlab="Enrichment score",cex.names=0.8,xlim=c(-1,1),
  main="Effect of LPS in pag")
grid()

pdf("plot32_mitch4ova01.pdf")
par(mar=c(5,25,3,1))
barplot(m4_o_top_updn,horiz=TRUE,las=1,col="darkgray",
  xlab="Enrichment score",cex.names=0.8,xlim=c(-1,1),
  main="Effect of LPS in pag")
grid()
dev.off()

## X11cairo 
##        2

Mitch 4 euler.

m4_l_up <- subset(m4_l_top,s.dist>0)$set
m4_l_dn <- subset(m4_l_top,s.dist<0)$set
m4_o_up <- subset(m4_o_top,s.dist>0)$set
m4_o_dn <- subset(m4_o_top,s.dist<0)$set

par(mar=c(2,2,2,2))

v0 <- list("LPS up"=m4_l_up,
  "LPS dn"=m4_l_dn,
  "OVA up"=m4_o_up,
  "OVA dn"=m4_o_dn)
plot(euler(v0),quantities = TRUE, edges = "gray", main="effect of treatments on pag")

pdf("plot33_mitch4lpsovaeuler01.pdf")
plot(euler(v0),quantities = TRUE, edges = "gray", main="effect of treatments on pag")
dev.off()

## X11cairo 
##        2

Two dimensional mitch analysis in pag.

ml <- list("LPS"=dge4_l,"OVA"=dge4_o)
m4 <- mitch_import(ml, DEtype="deseq2",geneTable=gt)

## Note: Mean no. genes in input = 17637

## Note: no. genes in output = 17476

## Note: estimated proportion of input genes in output = 0.991

head(m4)

##                       LPS         OVA
## 0610009B22Rik  0.02592933  0.45655760
## 0610009E02Rik  0.75195906 -0.40699076
## 0610009L18Rik -1.06444668 -0.18691102
## 0610010K14Rik  0.44760852 -0.37114848
## 0610012G03Rik -0.16331991 -0.34216334
## 0610030E20Rik -0.62496606  0.08425395

mres4 <- mitch_calc(m4, genesets, priority="effect")

## Note: Enrichments with large effect sizes may not be 
##             statistically significant.

head(mres4$enrichment_result,20) %>%
  kbl(caption = "Top gene pathway differences in pag for LPS and OVA") %>%
  kable_paper("hover", full_width = F)

Top gene pathway differences in pag for LPS and OVA

	set	setSize	pMANOVA	s.LPS	s.OVA	p.LPS	p.OVA	s.dist	SD	p.adjustMANOVA
47	ALPHA LINOLENIC OMEGA3 AND LINOLEIC OMEGA6 ACID METABOLISM	12	0.0009549	0.6081940	0.4942358	0.0002639	0.0030312	0.7836893	0.0805806	0.0082038
340	FORMATION OF TUBULIN FOLDING INTERMEDIATES BY CCT TRIC	19	0.0000002	0.7073410	0.3204717	0.0000001	0.0155966	0.7765523	0.2735580	0.0000098
756	PURINE RIBONUCLEOSIDE MONOPHOSPHATE BIOSYNTHESIS	12	0.0022659	0.5595320	0.4856848	0.0007898	0.0035768	0.7409223	0.0522178	0.0149831
859	RHOBTB3 ATPASE CYCLE	10	0.0072254	0.5533379	0.4748655	0.0024450	0.0093151	0.7291639	0.0554884	0.0333503
154	CITRIC ACID CYCLE TCA CYCLE	22	0.0000358	0.5105941	0.5020834	0.0000338	0.0000456	0.7160964	0.0060180	0.0006567
92	BBSOME MEDIATED CARGO TARGETING TO CILIUM	23	0.0000281	0.5339234	0.4525072	0.0000093	0.0001721	0.6998836	0.0575699	0.0005334
663	OLFACTORY SIGNALING PATHWAY	37	0.0000001	-0.4716936	-0.4985300	0.0000007	0.0000002	0.6863141	0.0189762	0.0000059
110	CALNEXIN CALRETICULIN CYCLE	26	0.0000325	0.4551333	0.4795283	0.0000589	0.0000231	0.6611307	0.0172499	0.0006072
398	GOLGI CISTERNAE PERICENTRIOLAR STACK REORGANIZATION	12	0.0021558	0.5746202	0.2948446	0.0005671	0.0769919	0.6458496	0.1978312	0.0145829
654	NUCLEOBASE BIOSYNTHESIS	15	0.0027383	0.5073287	0.3845942	0.0006686	0.0099118	0.6366280	0.0867864	0.0171302
841	RETROGRADE NEUROTROPHIN SIGNALLING	13	0.0103209	0.4521428	0.4266912	0.0047621	0.0077269	0.6216900	0.0179970	0.0441735
594	N GLYCAN TRIMMING IN THE ER AND CALNEXIN CALRETICULIN CYCLE	35	0.0000130	0.4196925	0.4223595	0.0000173	0.0000153	0.5954237	0.0018858	0.0002888
104	BRANCHED CHAIN AMINO ACID CATABOLISM	21	0.0008159	0.3589175	0.4703387	0.0044101	0.0001904	0.5916420	0.0787867	0.0073827
293	ER QUALITY CONTROL COMPARTMENT ERQC	21	0.0007274	0.3488672	0.4765315	0.0056490	0.0001565	0.5905849	0.0902723	0.0068493
1050	SYNTHESIS OF VERY LONG CHAIN FATTY ACYL COAS	20	0.0020634	0.3972560	0.4259853	0.0021010	0.0009736	0.5824739	0.0203147	0.0142024
516	LDL CLEARANCE	16	0.0078137	0.4372208	0.3651704	0.0024622	0.0114431	0.5696590	0.0509473	0.0351019
182	COPI INDEPENDENT GOLGI TO ER RETROGRADE TRAFFIC	44	0.0000001	0.4935125	0.2800466	0.0000000	0.0013104	0.5674334	0.1509431	0.0000045
395	GLYCOSPHINGOLIPID METABOLISM	38	0.0000156	0.4066286	0.3945376	0.0000144	0.0000257	0.5665746	0.0085496	0.0003227
776	RECEPTOR MEDIATED MITOPHAGY	11	0.0427402	0.4240637	0.3572808	0.0148784	0.0401937	0.5545084	0.0472227	0.1260783
1048	SYNTHESIS OF PYROPHOSPHATES IN THE CYTOSOL	10	0.0622144	0.4138555	0.3594641	0.0234431	0.0490369	0.5481704	0.0384605	0.1607035

m4_top <- subset(mres4$enrichment_result,p.adjustMANOVA<0.05)
m4_up <- subset(m4_top,s.dist>0)$set
m4_dn <- subset(m4_top,s.dist<0)$set
mitch_report(mres4,outfile="mitch4.html",overwrite=TRUE)

## Note: overwriting existing report

## Dataset saved as " /tmp/RtmpXoIrRA/mitch4.rds ".

## 
## 
## processing file: mitch.Rmd

## 1/34                             
## 2/34 [checklibraries]            
## 3/34                             
## 4/34 [peek]                      
## 5/34                             
## 6/34 [metrics]                   
## 7/34                             
## 8/34 [scatterplot]

## 9/34                             
## 10/34 [contourplot]

## 11/34                             
## 12/34 [input_geneset_metrics1]    
## 13/34                             
## 14/34 [input_geneset_metrics2]

## 15/34                             
## 16/34 [input_geneset_metrics3]

## 17/34                             
## 18/34 [echart1d]                  
## 19/34 [echart2d]                  
## 20/34                             
## 21/34 [heatmap]

## 22/34                             
## 23/34 [effectsize]                
## 24/34                             
## 25/34 [results_table]             
## 26/34                             
## 27/34 [results_table_complete]    
## 28/34                             
## 29/34 [detailed_geneset_reports1d]
## 30/34                             
## 31/34 [detailed_geneset_reports2d]

## 32/34                             
## 33/34 [session_info]              
## 34/34

## output file: /home/mark.ziemann@domain.internal.burnet.edu.au/projects/lung_brain/mitch.knit.md

## /usr/bin/pandoc +RTS -K512m -RTS /home/mark.ziemann@domain.internal.burnet.edu.au/projects/lung_brain/mitch.knit.md --to html4 --from markdown+autolink_bare_uris+tex_math_single_backslash --output /tmp/RtmpXoIrRA/mitch_report.html --lua-filter /usr/local/lib/R/site-library/rmarkdown/rmarkdown/lua/pagebreak.lua --lua-filter /usr/local/lib/R/site-library/rmarkdown/rmarkdown/lua/latex-div.lua --self-contained --variable bs3=TRUE --section-divs --template /usr/local/lib/R/site-library/rmarkdown/rmd/h/default.html --no-highlight --variable highlightjs=1 --variable theme=bootstrap --mathjax --variable 'mathjax-url=https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML' --include-in-header /tmp/RtmpXoIrRA/rmarkdown-str2d1205e09b664.html

## 
## Output created: /tmp/RtmpXoIrRA/mitch_report.html

## [1] TRUE

write.table(mres4$enrichment_result,file="mitch4.tsv",quote=FALSE,sep="\t",row.names=FALSE)

m4_top_up <- head(subset(m4_top,s.dist>0),10)[,"s.dist"]
names(m4_top_up) <- head(subset(m4_top,s.dist>0),10)[,"set"]
m4_top_dn <- head(subset(m4_top,s.dist<0),10)[,"s.dist"]
names(m4_top_dn) <- head(subset(m4_top,s.dist<0),10)[,"set"]
m4_top_updn <- c(m4_top_up,m4_top_dn)
m4_top_updn <- m4_top_updn[order(m4_top_updn)]

# heatmap
mr4 <- head(subset(mres4$enrichment_result,p.adjustMANOVA<0.05),30)
mr4 <- as.matrix(mr4[,4:5])
rownames(mr4) <- head(mres4$enrichment_result,30)$set

colfunc <- colorRampPalette(c("blue", "white", "red"))

heatmap.2(mr4,trace="none",col=colfunc(25),scale="none", margins = c(10,28), cexRow=0.7,
  main="Top ranked pathways in pag" , cexCol=0.9 )

pdf("plot34_mitch4_heatmap01.pdf")
heatmap.2(mr4,trace="none",col=colfunc(25),scale="none", margins = c(10,28), cexRow=0.7,
  main="Top ranked pathways in pag" , cexCol=0.9 )
dev.off()

## X11cairo 
##        2

Need to make a scatterplot to highlight the following:

• Serotonin Neurotransmitter Release Cycle (DN)
• Class C3 metabotropic glutamate pheromone receptors (DN)
• Regulation of commissural axon pathfinding by SLIT AND robo (DN)
• Reduction of Cytosolic Ca (DN)
• YAP1 and WWTR1 TAZ STIMULATED GENE EXPRESSION (UP)
• Activation of the mRNA upon binding (UP)
• Regulation of expression of slits and robs (UP)

par(mar=c(4,4,2,1))

a <- mres4$enrichment_result
plot(a$s.LPS , a$s.OVA,pch=19,col="gray")
abline(h=0,lty=2)
abline(v=0,lty=2)
as <- subset(a,p.adjustMANOVA<0.05)
points(as$s.LPS , as$s.OVA,pch=19,col="red")

abline(h=0.2,lty=2,col="red")
abline(v=0.2,lty=2,col="red")
abline(h=-0.2,lty=2,col="red")
abline(v=-0.2,lty=2,col="red")

mysets <- c("SEROTONIN NEUROTRANSMITTER RELEASE CYCLE",
  "CLASS C 3 METABOTROPIC GLUTAMATE PHEROMONE RECEPTORS",
  "REGULATION OF COMMISSURAL AXON PATHFINDING BY SLIT AND ROBO",
  "REDUCTION OF CYTOSOLIC CA LEVELS",
  "YAP1 AND WWTR1 TAZ STIMULATED GENE EXPRESSION",
  "ACTIVATION OF THE MRNA UPON BINDING OF THE CAP BINDING COMPLEX AND EIFS AND SUBSEQUENT BINDING TO 43S",
  "REGULATION OF EXPRESSION OF SLITS AND ROBOS")

a2 <- a[which(a$set %in% mysets),]

points(a2$s.LPS , a2$s.OVA+0.025,pch=25,col="red")

pdf("plot34b_mitch4_scatter01.pdf")

a <- mres4$enrichment_result
plot(a$s.LPS , a$s.OVA,pch=19,col="gray")
abline(h=0,lty=2)
abline(v=0,lty=2)
as <- subset(a,p.adjustMANOVA<0.05)
points(as$s.LPS , as$s.OVA,pch=19,col="red")

abline(h=0.2,lty=2,col="red")
abline(v=0.2,lty=2,col="red")
abline(h=-0.2,lty=2,col="red")
abline(v=-0.2,lty=2,col="red")

mysets <- c("SEROTONIN NEUROTRANSMITTER RELEASE CYCLE",
  "CLASS C 3 METABOTROPIC GLUTAMATE PHEROMONE RECEPTORS",
  "REGULATION OF COMMISSURAL AXON PATHFINDING BY SLIT AND ROBO",
  "REDUCTION OF CYTOSOLIC CA LEVELS",
  "YAP1 AND WWTR1 TAZ STIMULATED GENE EXPRESSION",
  "ACTIVATION OF THE MRNA UPON BINDING OF THE CAP BINDING COMPLEX AND EIFS AND SUBSEQUENT BINDING TO 43S",
  "REGULATION OF EXPRESSION OF SLITS AND ROBOS")

a2 <- a[which(a$set %in% mysets),]

points(a2$s.LPS , a2$s.OVA+0.025,pch=25,col="red")

dev.off()

## X11cairo 
##        2

Mitch pathway analysis in NI

Mitch for LPS in NI

m5_l <- mitch_import(dge5_l, DEtype="deseq2",geneTable=gt)

## The input is a single dataframe; one contrast only. Converting
##         it to a list for you.

## Note: Mean no. genes in input = 17490

## Note: no. genes in output = 17472

## Note: estimated proportion of input genes in output = 0.999

mres5_l <- mitch_calc(m5_l, genesets, priority="effect")

## Note: Enrichments with large effect sizes may not be
##             statistically significant.

head(mres5_l$enrichment_result,20) %>%
  kbl(caption = "Top gene pathway differences in NI for LPS") %>%
  kable_paper("hover", full_width = F)

Top gene pathway differences in NI for LPS

	set	setSize	pANOVA	s.dist	p.adjustANOVA
299	ERYTHROCYTES TAKE UP CARBON DIOXIDE AND RELEASE OXYGEN	10	0.0001404	-0.6952468	0.0015328
35	ADENYLATE CYCLASE ACTIVATING PATHWAY	10	0.0001934	0.6806437	0.0020182
817	REGULATION OF RUNX1 EXPRESSION AND ACTIVITY	17	0.0000628	0.5605911	0.0008046
283	ENDOSOMAL VACUOLAR PATHWAY	10	0.0038749	-0.5274310	0.0255224
1106	TRAFFICKING AND PROCESSING OF ENDOSOMAL TLR	11	0.0028201	-0.5200525	0.0196736
461	INTERACTION BETWEEN L1 AND ANKYRINS	27	0.0000043	0.5109158	0.0000847
843	RETINOID CYCLE DISEASE EVENTS	10	0.0073713	-0.4893483	0.0420834
1035	SYNTHESIS OF ACTIVE UBIQUITIN ROLES OF E1 AND E2 ENZYMES	29	0.0000064	-0.4842354	0.0001120
518	LGI ADAM INTERACTIONS	14	0.0027591	0.4620477	0.0194786
1176	YAP1 AND WWTR1 TAZ STIMULATED GENE EXPRESSION	11	0.0099376	0.4489017	0.0534997
158	CLASS I PEROXISOMAL MEMBRANE PROTEIN IMPORT	20	0.0005149	-0.4485560	0.0048015
679	P75NTR REGULATES AXONOGENESIS	10	0.0152093	-0.4432940	0.0734907
736	PRESYNAPTIC DEPOLARIZATION AND CALCIUM CHANNEL OPENING	11	0.0115089	0.4399988	0.0603067
1069	THE CANONICAL RETINOID CYCLE IN RODS TWILIGHT VISION	15	0.0031735	-0.4399878	0.0213800
216	DEFECTIVE B4GALT7 CAUSES EDS PROGEROID TYPE	20	0.0011205	0.4208629	0.0091739
124	CD28 DEPENDENT VAV1 PATHWAY	11	0.0174696	-0.4138522	0.0814095
893	RORA ACTIVATES GENE EXPRESSION	18	0.0025722	0.4104376	0.0184919
571	MITOCHONDRIAL TRANSLATION	93	0.0000000	-0.4060710	0.0000000
778	RECEPTOR MEDIATED MITOPHAGY	11	0.0200050	-0.4050742	0.0886686
187	CRISTAE FORMATION	31	0.0001034	-0.4028827	0.0012196

m5_l_top <- subset(mres5_l$enrichment_result,p.adjustANOVA<0.05)
m5_l_up <- subset(m5_l_top,s.dist>0)$set
m5_l_dn <- subset(m5_l_top,s.dist<0)$set
mitch_report(mres5_l,outfile="mitch5_l.html",overwrite=TRUE)

## Note: overwriting existing report

## Dataset saved as " /tmp/RtmpXoIrRA/mitch5_l.rds ".

## 
## 
## processing file: mitch.Rmd

## 1/34                             
## 2/34 [checklibraries]            
## 3/34                             
## 4/34 [peek]                      
## 5/34                             
## 6/34 [metrics]                   
## 7/34                             
## 8/34 [scatterplot]

## 9/34                             
## 10/34 [contourplot]               
## 11/34                             
## 12/34 [input_geneset_metrics1]    
## 13/34                             
## 14/34 [input_geneset_metrics2]

## 15/34                             
## 16/34 [input_geneset_metrics3]

## 17/34                             
## 18/34 [echart1d]                  
## 19/34 [echart2d]                  
## 20/34                             
## 21/34 [heatmap]                   
## 22/34                             
## 23/34 [effectsize]                
## 24/34                             
## 25/34 [results_table]             
## 26/34                             
## 27/34 [results_table_complete]    
## 28/34                             
## 29/34 [detailed_geneset_reports1d]

## 30/34                             
## 31/34 [detailed_geneset_reports2d]
## 32/34                             
## 33/34 [session_info]              
## 34/34

## output file: /home/mark.ziemann@domain.internal.burnet.edu.au/projects/lung_brain/mitch.knit.md

## /usr/bin/pandoc +RTS -K512m -RTS /home/mark.ziemann@domain.internal.burnet.edu.au/projects/lung_brain/mitch.knit.md --to html4 --from markdown+autolink_bare_uris+tex_math_single_backslash --output /tmp/RtmpXoIrRA/mitch_report.html --lua-filter /usr/local/lib/R/site-library/rmarkdown/rmarkdown/lua/pagebreak.lua --lua-filter /usr/local/lib/R/site-library/rmarkdown/rmarkdown/lua/latex-div.lua --self-contained --variable bs3=TRUE --section-divs --template /usr/local/lib/R/site-library/rmarkdown/rmd/h/default.html --no-highlight --variable highlightjs=1 --variable theme=bootstrap --mathjax --variable 'mathjax-url=https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML' --include-in-header /tmp/RtmpXoIrRA/rmarkdown-str2d12055b0e3141.html

## 
## Output created: /tmp/RtmpXoIrRA/mitch_report.html

## [1] TRUE

write.table(mres5_l$enrichment_result,file="mitch5_l.tsv",quote=FALSE,sep="\t",row.names=FALSE)

m5_l_top_up <- head(subset(m5_l_top,s.dist>0),10)[,"s.dist"]
names(m5_l_top_up) <- head(subset(m5_l_top,s.dist>0),10)[,"set"]
m5_l_top_dn <- head(subset(m5_l_top,s.dist<0),10)[,"s.dist"]
names(m5_l_top_dn) <- head(subset(m5_l_top,s.dist<0),10)[,"set"]
m5_l_top_updn <- c(m5_l_top_up,m5_l_top_dn)
m5_l_top_updn <- m5_l_top_updn[order(m5_l_top_updn)]

par(mar=c(5,25,3,1))
barplot(m5_l_top_updn,horiz=TRUE,las=1,col="darkgray",
  xlab="Enrichment score",cex.names=0.8,xlim=c(-1,1),
  main="Effect of LPS in NI")
grid()

pdf("plot35_mitch5lps01.pdf")
par(mar=c(5,25,3,1))
barplot(m5_l_top_updn,horiz=TRUE,las=1,col="darkgray",
  xlab="Enrichment score",cex.names=0.8,xlim=c(-1,1),
  main="Effect of LPS in NI")
grid()
dev.off()

## X11cairo 
##        2

Mitch for OVA in NI

m5_o <- mitch_import(dge5_o, DEtype="deseq2",geneTable=gt)

## The input is a single dataframe; one contrast only. Converting
##         it to a list for you.

## Note: Mean no. genes in input = 17391

## Note: no. genes in output = 17372

## Note: estimated proportion of input genes in output = 0.999

mres5_o <- mitch_calc(m5_o, genesets, priority="effect")

## Note: Enrichments with large effect sizes may not be
##             statistically significant.

head(mres5_o$enrichment_result,20) %>%
  kbl(caption = "Top gene pathway differences in NI for OVA") %>%
  kable_paper("hover", full_width = F)

Top gene pathway differences in NI for OVA

	set	setSize	pANOVA	s.dist	p.adjustANOVA
818	REGULATION OF RUNX1 EXPRESSION AND ACTIVITY	17	0.0000127	0.6115105	0.0005531
299	ERYTHROCYTES TAKE UP CARBON DIOXIDE AND RELEASE OXYGEN	10	0.0019792	-0.5648888	0.0218082
638	NOTCH2 INTRACELLULAR DOMAIN REGULATES TRANSCRIPTION	11	0.0039453	0.5019139	0.0375124
1106	TRAFFICKING AND PROCESSING OF ENDOSOMAL TLR	11	0.0046893	-0.4923627	0.0428763
36	ADENYLATE CYCLASE ACTIVATING PATHWAY	10	0.0079067	0.4850478	0.0621463
642	NOTCH4 INTRACELLULAR DOMAIN REGULATES TRANSCRIPTION	20	0.0002367	0.4748213	0.0049825
10	ACETYLCHOLINE REGULATES INSULIN SECRETION	10	0.0095369	0.4733902	0.0705829
1	A TETRASACCHARIDE LINKER SEQUENCE IS REQUIRED FOR GAG SYNTHESIS	26	0.0000685	0.4510860	0.0020701
904	RUNX3 REGULATES NOTCH SIGNALING	13	0.0048671	0.4510230	0.0438037
216	DEFECTIVE B4GALT7 CAUSES EDS PROGEROID TYPE	20	0.0005585	0.4457354	0.0089821
635	NOTCH HLH TRANSCRIPTION PATHWAY	28	0.0000499	0.4428538	0.0016334
792	REGULATION OF COMMISSURAL AXON PATHFINDING BY SLIT AND ROBO	10	0.0169507	0.4360673	0.1020708
640	NOTCH3 INTRACELLULAR DOMAIN REGULATES TRANSCRIPTION	23	0.0003451	0.4310811	0.0064397
571	MITOCHONDRIAL TRANSLATION	93	0.0000000	-0.4304927	0.0000000
1036	SYNTHESIS OF ACTIVE UBIQUITIN ROLES OF E1 AND E2 ENZYMES	29	0.0000618	-0.4297411	0.0019189
679	P75NTR REGULATES AXONOGENESIS	10	0.0195647	-0.4263564	0.1125208
1126	TRANSCRIPTIONAL REGULATION OF PLURIPOTENT STEM CELLS	19	0.0013263	0.4254050	0.0162887
1176	YAP1 AND WWTR1 TAZ STIMULATED GENE EXPRESSION	12	0.0140815	0.4093318	0.0913418
188	CRISTAE FORMATION	31	0.0000817	-0.4087851	0.0023080
798	REGULATION OF GENE EXPRESSION IN LATE STAGE BRANCHING MORPHOGENESIS PANCREATIC BUD PRECURSOR CELLS	15	0.0062460	0.4078086	0.0529787

m5_o_top <- subset(mres5_o$enrichment_result,p.adjustANOVA<0.05)
m5_o_up <- subset(m5_o_top,s.dist>0)$set
m5_o_dn <- subset(m5_o_top,s.dist<0)$set
mitch_report(mres5_o,outfile="mitch5_o.html",overwrite=TRUE)

## Note: overwriting existing report

## Dataset saved as " /tmp/RtmpXoIrRA/mitch5_o.rds ".

## 
## 
## processing file: mitch.Rmd

## 1/34                             
## 2/34 [checklibraries]            
## 3/34                             
## 4/34 [peek]                      
## 5/34                             
## 6/34 [metrics]                   
## 7/34                             
## 8/34 [scatterplot]

## 9/34                             
## 10/34 [contourplot]               
## 11/34                             
## 12/34 [input_geneset_metrics1]    
## 13/34                             
## 14/34 [input_geneset_metrics2]

## 15/34                             
## 16/34 [input_geneset_metrics3]

## 17/34                             
## 18/34 [echart1d]                  
## 19/34 [echart2d]                  
## 20/34                             
## 21/34 [heatmap]                   
## 22/34                             
## 23/34 [effectsize]                
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## 26/34                             
## 27/34 [results_table_complete]    
## 28/34                             
## 29/34 [detailed_geneset_reports1d]

## 30/34                             
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## 32/34                             
## 33/34 [session_info]              
## 34/34

## output file: /home/mark.ziemann@domain.internal.burnet.edu.au/projects/lung_brain/mitch.knit.md

## /usr/bin/pandoc +RTS -K512m -RTS /home/mark.ziemann@domain.internal.burnet.edu.au/projects/lung_brain/mitch.knit.md --to html4 --from markdown+autolink_bare_uris+tex_math_single_backslash --output /tmp/RtmpXoIrRA/mitch_report.html --lua-filter /usr/local/lib/R/site-library/rmarkdown/rmarkdown/lua/pagebreak.lua --lua-filter /usr/local/lib/R/site-library/rmarkdown/rmarkdown/lua/latex-div.lua --self-contained --variable bs3=TRUE --section-divs --template /usr/local/lib/R/site-library/rmarkdown/rmd/h/default.html --no-highlight --variable highlightjs=1 --variable theme=bootstrap --mathjax --variable 'mathjax-url=https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML' --include-in-header /tmp/RtmpXoIrRA/rmarkdown-str2d120544d4894d.html

## 
## Output created: /tmp/RtmpXoIrRA/mitch_report.html

## [1] TRUE

write.table(mres5_o$enrichment_result,file="mitch5_o.tsv",quote=FALSE,sep="\t",row.names=FALSE)

m5_o_top_up <- head(subset(m5_o_top,s.dist>0),10)[,"s.dist"]
names(m5_o_top_up) <- head(subset(m5_o_top,s.dist>0),10)[,"set"]
m5_o_top_dn <- head(subset(m5_o_top,s.dist<0),10)[,"s.dist"]
names(m5_o_top_dn) <- head(subset(m5_o_top,s.dist<0),10)[,"set"]
m5_o_top_updn <- c(m5_o_top_up,m5_o_top_dn)
m5_o_top_updn <- m5_o_top_updn[order(m5_o_top_updn)]

par(mar=c(5,25,3,1))
barplot(m5_o_top_updn,horiz=TRUE,las=1,col="darkgray",
  xlab="Enrichment score",cex.names=0.8,xlim=c(-1,1),
  main="Effect of LPS in NI")
grid()

pdf("plot36_mitch5ova01.pdf")
par(mar=c(5,25,3,1))
barplot(m5_o_top_updn,horiz=TRUE,las=1,col="darkgray",
  xlab="Enrichment score",cex.names=0.8,xlim=c(-1,1),
  main="Effect of LPS in NI")
grid()
dev.off()

## X11cairo 
##        2

Mitch 5 euler.

m5_l_up <- subset(m5_l_top,s.dist>0)$set
m5_l_dn <- subset(m5_l_top,s.dist<0)$set
m5_o_up <- subset(m5_o_top,s.dist>0)$set
m5_o_dn <- subset(m5_o_top,s.dist<0)$set

par(mar=c(2,2,2,2))

v0 <- list("LPS up"=m5_l_up,
  "LPS dn"=m5_l_dn,
  "OVA up"=m5_o_up,
  "OVA dn"=m5_o_dn)
plot(euler(v0),quantities = TRUE, edges = "gray", main="effect of treatments on NI")

pdf("plot37_mitch5lpsovaeuler01.pdf")
plot(euler(v0),quantities = TRUE, edges = "gray", main="effect of treatments on NI")
dev.off()

## X11cairo 
##        2

Two dimensional mitch analysis in NI.

ml <- list("LPS"=dge5_l,"OVA"=dge5_o)
m5 <- mitch_import(ml, DEtype="deseq2",geneTable=gt)

## Note: Mean no. genes in input = 17440.5

## Note: no. genes in output = 17243

## Note: estimated proportion of input genes in output = 0.989

head(m5)

##                      LPS        OVA
## 0610009B22Rik -2.3583781 -1.1332185
## 0610009E02Rik -0.4921631 -0.3290659
## 0610009L18Rik -1.5061599  0.1306738
## 0610010K14Rik -0.7608005 -0.3126674
## 0610012G03Rik -0.6314820 -0.2034285
## 0610030E20Rik  1.2199694 -0.5747514

mres5 <- mitch_calc(m5, genesets, priority="effect")

## Note: Enrichments with large effect sizes may not be 
##             statistically significant.

head(mres5$enrichment_result,20) %>%
  kbl(caption = "Top gene pathway differences in NI for LPS and OVA") %>%
  kable_paper("hover", full_width = F)

Top gene pathway differences in NI for LPS and OVA

	set	setSize	pMANOVA	s.LPS	s.OVA	p.LPS	p.OVA	s.dist	SD	p.adjustMANOVA
298	ERYTHROCYTES TAKE UP CARBON DIOXIDE AND RELEASE OXYGEN	10	0.0006912	-0.6919283	-0.5630941	0.0001511	0.0020458	0.8920985	0.0910995	0.0055982
35	ADENYLATE CYCLASE ACTIVATING PATHWAY	10	0.0008832	0.6843846	0.4864040	0.0001783	0.0077343	0.8396256	0.1399934	0.0066157
815	REGULATION OF RUNX1 EXPRESSION AND ACTIVITY	17	0.0000339	0.5644546	0.6126648	0.0000559	0.0000122	0.8330469	0.0340898	0.0005393
1103	TRAFFICKING AND PROCESSING OF ENDOSOMAL TLR	11	0.0080395	-0.5151937	-0.4900397	0.0030886	0.0048886	0.7110299	0.0177866	0.0365039
1173	YAP1 AND WWTR1 TAZ STIMULATED GENE EXPRESSION	11	0.0135233	0.4523508	0.4933211	0.0093825	0.0046094	0.6693183	0.0289704	0.0542777
1033	SYNTHESIS OF ACTIVE UBIQUITIN ROLES OF E1 AND E2 ENZYMES	29	0.0000262	-0.4800543	-0.4281399	0.0000076	0.0000659	0.6432386	0.0367091	0.0004586
460	INTERACTION BETWEEN L1 AND ANKYRINS	27	0.0000213	0.5153518	0.3664249	0.0000036	0.0009824	0.6323406	0.1053072	0.0003852
215	DEFECTIVE B4GALT7 CAUSES EDS PROGEROID TYPE	20	0.0013404	0.4260233	0.4472856	0.0009726	0.0005342	0.6177057	0.0150347	0.0092710
677	P75NTR REGULATES AXONOGENESIS	10	0.0396662	-0.4389137	-0.4244647	0.0162452	0.0201133	0.6105862	0.0102170	0.1247257
1	A TETRASACCHARIDE LINKER SEQUENCE IS REQUIRED FOR GAG SYNTHESIS	26	0.0002794	0.3813494	0.4526385	0.0007633	0.0000646	0.5918690	0.0504090	0.0028326
569	MITOCHONDRIAL TRANSLATION	93	0.0000000	-0.4005091	-0.4281100	0.0000000	0.0000000	0.5862471	0.0195168	0.0000000
517	LGI ADAM INTERACTIONS	14	0.0104332	0.4661410	0.3505220	0.0025286	0.0231636	0.5832264	0.0817550	0.0446163
633	NOTCH HLH TRANSCRIPTION PATHWAY	28	0.0002287	0.3610307	0.4441019	0.0009447	0.0000475	0.5723370	0.0587402	0.0024673
187	CRISTAE FORMATION	31	0.0001736	-0.3973822	-0.4065768	0.0001285	0.0000893	0.5685220	0.0065016	0.0019825
965	SIGNALING BY LEPTIN	10	0.0613965	0.4015552	0.4020078	0.0278946	0.0277188	0.5682049	0.0003201	0.1686969
339	FORMATION OF TUBULIN FOLDING INTERMEDIATES BY CCT TRIC	19	0.0059592	-0.3916261	-0.3982326	0.0031242	0.0026549	0.5585340	0.0046715	0.0292001
158	CLASS I PEROXISOMAL MEMBRANE PROTEIN IMPORT	20	0.0027132	-0.4439761	-0.3317250	0.0005874	0.0102258	0.5542168	0.0793735	0.0164469
901	RUNX3 REGULATES NOTCH SIGNALING	13	0.0183066	0.3161391	0.4524666	0.0484372	0.0047323	0.5519692	0.0963981	0.0701258
640	NOTCH4 INTRACELLULAR DOMAIN REGULATES TRANSCRIPTION	20	0.0006811	0.2625907	0.4762062	0.0420714	0.0002269	0.5438072	0.1510490	0.0055705
636	NOTCH2 INTRACELLULAR DOMAIN REGULATES TRANSCRIPTION	11	0.0058338	0.2055584	0.5033764	0.2378394	0.0038415	0.5437297	0.2105891	0.0288485

m5_top <- subset(mres5$enrichment_result,p.adjustMANOVA<0.05)
m5_up <- subset(m5_top,s.dist>0)$set
m5_dn <- subset(m5_top,s.dist<0)$set
mitch_report(mres5,outfile="mitch5.html",overwrite=TRUE)

## Note: overwriting existing report

## Dataset saved as " /tmp/RtmpXoIrRA/mitch5.rds ".

## 
## 
## processing file: mitch.Rmd

## 1/34                             
## 2/34 [checklibraries]            
## 3/34                             
## 4/34 [peek]                      
## 5/34                             
## 6/34 [metrics]                   
## 7/34                             
## 8/34 [scatterplot]

## 9/34                             
## 10/34 [contourplot]

## 11/34                             
## 12/34 [input_geneset_metrics1]    
## 13/34                             
## 14/34 [input_geneset_metrics2]

## 15/34                             
## 16/34 [input_geneset_metrics3]

## 17/34                             
## 18/34 [echart1d]                  
## 19/34 [echart2d]                  
## 20/34                             
## 21/34 [heatmap]

## 22/34                             
## 23/34 [effectsize]                
## 24/34                             
## 25/34 [results_table]             
## 26/34                             
## 27/34 [results_table_complete]    
## 28/34                             
## 29/34 [detailed_geneset_reports1d]
## 30/34                             
## 31/34 [detailed_geneset_reports2d]

## 32/34                             
## 33/34 [session_info]              
## 34/34

## output file: /home/mark.ziemann@domain.internal.burnet.edu.au/projects/lung_brain/mitch.knit.md

## /usr/bin/pandoc +RTS -K512m -RTS /home/mark.ziemann@domain.internal.burnet.edu.au/projects/lung_brain/mitch.knit.md --to html4 --from markdown+autolink_bare_uris+tex_math_single_backslash --output /tmp/RtmpXoIrRA/mitch_report.html --lua-filter /usr/local/lib/R/site-library/rmarkdown/rmarkdown/lua/pagebreak.lua --lua-filter /usr/local/lib/R/site-library/rmarkdown/rmarkdown/lua/latex-div.lua --self-contained --variable bs3=TRUE --section-divs --template /usr/local/lib/R/site-library/rmarkdown/rmd/h/default.html --no-highlight --variable highlightjs=1 --variable theme=bootstrap --mathjax --variable 'mathjax-url=https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML' --include-in-header /tmp/RtmpXoIrRA/rmarkdown-str2d12055f6998fb.html

## 
## Output created: /tmp/RtmpXoIrRA/mitch_report.html

## [1] TRUE

write.table(mres5$enrichment_result,file="mitch5.tsv",quote=FALSE,sep="\t",row.names=FALSE)

m5_top_up <- head(subset(m5_top,s.dist>0),10)[,"s.dist"]
names(m5_top_up) <- head(subset(m5_top,s.dist>0),10)[,"set"]
m5_top_dn <- head(subset(m5_top,s.dist<0),10)[,"s.dist"]
names(m5_top_dn) <- head(subset(m5_top,s.dist<0),10)[,"set"]
m5_top_updn <- c(m5_top_up,m5_top_dn)
m5_top_updn <- m5_top_updn[order(m5_top_updn)]

# heatmap
mr5 <- head(subset(mres5$enrichment_result,p.adjustMANOVA<0.05),30)
mr5 <- as.matrix(mr5[,4:5])
rownames(mr5) <- head(mres5$enrichment_result,30)$set

colfunc <- colorRampPalette(c("blue", "white", "red"))

heatmap.2(mr5,trace="none",col=colfunc(25),scale="none", margins = c(10,28), cexRow=0.7,
  main="Top ranked pathways in NI" , cexCol=0.9 )

pdf("plot37_mitch5_heatmap01.pdf")
heatmap.2(mr5,trace="none",col=colfunc(25),scale="none", margins = c(10,28), cexRow=0.7,
  main="Top ranked pathways in NI" , cexCol=0.9 )
dev.off()

## X11cairo 
##        2

Need to make a scatterplot to highlight the following:

• Serotonin Neurotransmitter Release Cycle (DN)
• Class C3 metabotropic glutamate pheromone receptors (DN)
• Regulation of commissural axon pathfinding by SLIT AND robo (DN)
• Reduction of Cytosolic Ca (DN)
• YAP1 and WWTR1 TAZ STIMULATED GENE EXPRESSION (UP)
• Activation of the mRNA upon binding (UP)
• Regulation of expression of slits and robs (UP)

par(mar=c(4,4,2,1))

a <- mres5$enrichment_result
plot(a$s.LPS , a$s.OVA,pch=19,col="gray")
abline(h=0,lty=2)
abline(v=0,lty=2)
as <- subset(a,p.adjustMANOVA<0.05)
points(as$s.LPS , as$s.OVA,pch=19,col="red")

abline(h=0.2,lty=2,col="red")
abline(v=0.2,lty=2,col="red")
abline(h=-0.2,lty=2,col="red")
abline(v=-0.2,lty=2,col="red")

mysets <- c("SEROTONIN NEUROTRANSMITTER RELEASE CYCLE",
  "CLASS C 3 METABOTROPIC GLUTAMATE PHEROMONE RECEPTORS",
  "REGULATION OF COMMISSURAL AXON PATHFINDING BY SLIT AND ROBO",
  "REDUCTION OF CYTOSOLIC CA LEVELS",
  "YAP1 AND WWTR1 TAZ STIMULATED GENE EXPRESSION",
  "ACTIVATION OF THE MRNA UPON BINDING OF THE CAP BINDING COMPLEX AND EIFS AND SUBSEQUENT BINDING TO 43S",
  "REGULATION OF EXPRESSION OF SLITS AND ROBOS")

a2 <- a[which(a$set %in% mysets),]

points(a2$s.LPS , a2$s.OVA+0.025,pch=25,col="red")

pdf("plot37b_mitch5_scatter01.pdf")

a <- mres5$enrichment_result
plot(a$s.LPS , a$s.OVA,pch=19,col="gray")
abline(h=0,lty=2)
abline(v=0,lty=2)
as <- subset(a,p.adjustMANOVA<0.05)
points(as$s.LPS , as$s.OVA,pch=19,col="red")

abline(h=0.2,lty=2,col="red")
abline(v=0.2,lty=2,col="red")
abline(h=-0.2,lty=2,col="red")
abline(v=-0.2,lty=2,col="red")

mysets <- c("SEROTONIN NEUROTRANSMITTER RELEASE CYCLE",
  "CLASS C 3 METABOTROPIC GLUTAMATE PHEROMONE RECEPTORS",
  "REGULATION OF COMMISSURAL AXON PATHFINDING BY SLIT AND ROBO",
  "REDUCTION OF CYTOSOLIC CA LEVELS",
  "YAP1 AND WWTR1 TAZ STIMULATED GENE EXPRESSION",
  "ACTIVATION OF THE MRNA UPON BINDING OF THE CAP BINDING COMPLEX AND EIFS AND SUBSEQUENT BINDING TO 43S",
  "REGULATION OF EXPRESSION OF SLITS AND ROBOS")

a2 <- a[which(a$set %in% mysets),]

points(a2$s.LPS , a2$s.OVA+0.025,pch=25,col="red")

dev.off()

## X11cairo 
##        2

Mitch pathway analysis - LPS all tissues

ml <- list("hyp"=dge1_l,
  "amy"=dge2_l,
  "hip"=dge3_l,
  "pag"=dge4_l,
  "ni"=dge5_l)

m6 <- mitch_import(ml, DEtype="deseq2",geneTable=gt)

## Note: Mean no. genes in input = 17188.4

## Note: no. genes in output = 15915

## Note: estimated proportion of input genes in output = 0.926

head(m6)

##                      hyp        amy        hip         pag         ni
## 0610009B22Rik -0.3304181  0.2684894 -3.3814425  0.02592933 -2.3583781
## 0610009E02Rik  0.7596333 -0.7521235 -0.3557659  0.75195906 -0.4921631
## 0610009L18Rik -1.2521412 -1.1838923 -0.8507498 -1.06444668 -1.5061599
## 0610010K14Rik  0.4193627 -0.0433030  1.2510787  0.44760852 -0.7608005
## 0610012G03Rik  1.4462155 -0.1930476  3.9729326 -0.16331991 -0.6314820
## 0610030E20Rik -0.2426628  1.0496464  0.1581737 -0.62496606  1.2199694

mres6 <- mitch_calc(m6, genesets, priority="effect")

## Note: Enrichments with large effect sizes may not be 
##             statistically significant.

head(mres6$enrichment_result,20) %>%
  kbl(caption = "Top gene pathway differences in all tissues caused by LPS") %>%
  kable_paper("hover", full_width = F)

Top gene pathway differences in all tissues caused by LPS

	set	setSize	pMANOVA	s.hyp	s.amy	s.hip	s.pag	s.ni	p.hyp	p.amy	p.hip	p.pag	p.ni	s.dist	SD	p.adjustMANOVA
332	FORMATION OF TUBULIN FOLDING INTERMEDIATES BY CCT TRIC	19	0.0000000	0.5822981	0.1560604	0.1544778	0.6954282	-0.3831086	0.0000111	0.2390119	0.2438091	0.0000002	0.0038422	1.0088022	0.4263806	0.0000000
1118	TRANSPORT OF CONNEXONS TO THE PLASMA MEMBRANE	13	0.0000172	0.5932587	0.3167091	0.4489131	0.5229821	-0.1372928	0.0002123	0.0480410	0.0050717	0.0010947	0.3914527	0.9726989	0.2904471	0.0001274
713	POST CHAPERONIN TUBULIN FOLDING PATHWAY	17	0.0000011	0.6304012	0.3165696	0.5031487	0.3588317	-0.0845537	0.0000068	0.0238489	0.0003285	0.0104292	0.5461975	0.9416422	0.2701762	0.0000103
33	ADENYLATE CYCLASE ACTIVATING PATHWAY	10	0.0001895	-0.0885130	0.2080981	0.4568626	0.1617730	0.6787300	0.6279478	0.2545465	0.0123633	0.3757629	0.0002017	0.8641225	0.2937507	0.0010897
187	CROSSLINKING OF COLLAGEN FIBRILS	13	0.0176921	0.3352167	0.4013815	0.5673500	0.0343160	0.3112526	0.0363892	0.0122225	0.0003970	0.8303920	0.0520234	0.8327179	0.1931567	0.0513870
529	MET ACTIVATES RAP1 AND RAC1	10	0.0643591	-0.3637598	-0.5405218	-0.3609557	0.1325244	-0.3218736	0.0464000	0.0030783	0.0481117	0.4680921	0.0780071	0.8221553	0.2513837	0.1385459
894	SEALING OF THE NUCLEAR ENVELOPE NE BY ESCRT III	23	0.0000005	0.5136082	0.2585605	0.3151818	0.4227229	-0.1673689	0.0000201	0.0318571	0.0088894	0.0004493	0.1647725	0.7979308	0.2627367	0.0000051
121	CD28 DEPENDENT VAV1 PATHWAY	11	0.0258854	-0.1266805	-0.3171529	-0.4384374	0.3943090	-0.3997393	0.4669677	0.0685724	0.0118086	0.0235523	0.0217020	0.7900206	0.3415149	0.0688096
197	CYTOSOLIC IRON SULFUR CLUSTER ASSEMBLY	13	0.0168848	0.4101661	0.4163192	0.4380001	-0.1403307	0.1677970	0.0104521	0.0093517	0.0062513	0.3810580	0.2949153	0.7623979	0.2487080	0.0497928
23	ACTIVATION OF RAC1	13	0.0212681	-0.2226909	-0.5011464	-0.3742829	0.2291923	-0.2883237	0.1645079	0.0017563	0.0194674	0.1525294	0.0718929	0.7592669	0.2777769	0.0595302
183	CREB1 PHOSPHORYLATION THROUGH THE ACTIVATION OF ADENYLATE CYCLASE	11	0.0165002	-0.3770464	-0.4368026	-0.3853576	0.2699721	-0.1381013	0.0303718	0.0121271	0.0269016	0.1210762	0.4277760	0.7572436	0.2939289	0.0489082
362	GAP JUNCTION ASSEMBLY	21	0.0000215	0.4383205	0.2261770	0.3196414	0.4386261	-0.1332758	0.0005068	0.0728149	0.0112300	0.0005022	0.2904745	0.7453904	0.2361410	0.0001543
1017	SYNTHESIS OF ACTIVE UBIQUITIN ROLES OF E1 AND E2 ENZYMES	29	0.0000107	-0.0487395	-0.2218696	-0.4021107	0.3229302	-0.4740934	0.6497113	0.0386822	0.0001784	0.0026164	0.0000099	0.7364395	0.3188207	0.0000838
176	COOPERATION OF PREFOLDIN AND TRIC CCT IN ACTIN AND TUBULIN FOLDING	26	0.0000000	0.3865374	0.1647584	0.0234463	0.4606525	-0.3842862	0.0006464	0.1459874	0.8360982	0.0000479	0.0006951	0.7327911	0.3362270	0.0000003
44	ALPHA LINOLENIC OMEGA3 AND LINOLEIC OMEGA6 ACID METABOLISM	12	0.0022441	0.1522040	0.3667128	0.0551468	0.5905490	0.0984091	0.3613373	0.0278466	0.7408478	0.0003965	0.5550640	0.7204984	0.2236477	0.0092648
139	CGMP EFFECTS	15	0.0036952	-0.5596059	-0.3016688	-0.2268931	-0.2410482	0.0180126	0.0001749	0.0431069	0.1281937	0.1060559	0.9038767	0.7169881	0.2062893	0.0141446
772	REDUCTION OF CYTOSOLIC CA LEVELS	11	0.0184747	-0.5541888	-0.3045317	-0.1996639	-0.2657879	0.0578014	0.0014589	0.0803353	0.2515805	0.1269492	0.7399623	0.7167389	0.2196449	0.0528678
1036	SYNTHESIS SECRETION AND INACTIVATION OF GLUCAGON LIKE PEPTIDE 1 GLP 1	12	0.0193904	-0.1096229	-0.1519944	-0.5266616	0.0253097	-0.4451676	0.5108965	0.3619990	0.0015830	0.8793534	0.0075831	0.7150571	0.2341943	0.0550744
680	PHASE 2 PLATEAU PHASE	10	0.1065986	0.1580384	0.1611443	0.3954228	-0.1847972	0.5066709	0.3868841	0.3776212	0.0303778	0.3116361	0.0055304	0.7058103	0.2661346	0.1980249
725	PROCESSING AND ACTIVATION OF SUMO	10	0.0314881	0.0185728	-0.2963722	-0.4768186	0.2293996	-0.3574348	0.9190048	0.1046478	0.0090305	0.2091121	0.0503357	0.7042167	0.2915930	0.0794765

m6_top <- subset(mres6$enrichment_result,p.adjustMANOVA<0.05)
m6_up <- subset(m6_top,s.dist>0)$set
m6_dn <- subset(m6_top,s.dist<0)$set
mitch_report(mres6,outfile="mitch6.html",overwrite=TRUE)

## Note: overwriting existing report

## Dataset saved as " /tmp/RtmpXoIrRA/mitch6.rds ".

## 
## 
## processing file: mitch.Rmd

## 1/34                             
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## 3/34                             
## 4/34 [peek]                      
## 5/34                             
## 6/34 [metrics]                   
## 7/34                             
## 8/34 [scatterplot]

## 9/34                             
## 10/34 [contourplot]

## 11/34                             
## 12/34 [input_geneset_metrics1]    
## 13/34                             
## 14/34 [input_geneset_metrics2]

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## 17/34                             
## 18/34 [echart1d]                  
## 19/34 [echart2d]                  
## 20/34                             
## 21/34 [heatmap]

## 22/34                             
## 23/34 [effectsize]                
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## 28/34                             
## 29/34 [detailed_geneset_reports1d]
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## 31/34 [detailed_geneset_reports2d]

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## 34/34

## output file: /home/mark.ziemann@domain.internal.burnet.edu.au/projects/lung_brain/mitch.knit.md

## /usr/bin/pandoc +RTS -K512m -RTS /home/mark.ziemann@domain.internal.burnet.edu.au/projects/lung_brain/mitch.knit.md --to html4 --from markdown+autolink_bare_uris+tex_math_single_backslash --output /tmp/RtmpXoIrRA/mitch_report.html --lua-filter /usr/local/lib/R/site-library/rmarkdown/rmarkdown/lua/pagebreak.lua --lua-filter /usr/local/lib/R/site-library/rmarkdown/rmarkdown/lua/latex-div.lua --self-contained --variable bs3=TRUE --section-divs --template /usr/local/lib/R/site-library/rmarkdown/rmd/h/default.html --no-highlight --variable highlightjs=1 --variable theme=bootstrap --mathjax --variable 'mathjax-url=https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML' --include-in-header /tmp/RtmpXoIrRA/rmarkdown-str2d12057dbe2812.html

## 
## Output created: /tmp/RtmpXoIrRA/mitch_report.html

## [1] TRUE

write.table(mres6$enrichment_result,file="mitch6.tsv",quote=FALSE,sep="\t",row.names=FALSE)

m6_top_up <- head(subset(m6_top,s.dist>0),10)[,"s.dist"]
names(m6_top_up) <- head(subset(m6_top,s.dist>0),10)[,"set"]
m6_top_dn <- head(subset(m6_top,s.dist<0),10)[,"s.dist"]
names(m6_top_dn) <- head(subset(m6_top,s.dist<0),10)[,"set"]
m6_top_updn <- c(m6_top_up,m6_top_dn)
m6_top_updn <- m6_top_updn[order(m6_top_updn)]

# heatmap
mr6 <- head(subset(mres6$enrichment_result,p.adjustMANOVA<0.05),30)
mr6 <- as.matrix(mr6[,4:8])
rownames(mr6) <- head(mres6$enrichment_result,30)$set

colfunc <- colorRampPalette(c("blue", "white", "red"))

heatmap.2(mr6,trace="none",col=colfunc(25),scale="none", margins = c(10,21), cexRow=0.7,
  main="Top ranked pathways LPS all tissues" , cexCol=0.9 )

pdf("plot38_mitch6_heatmap01.pdf")
heatmap.2(mr6,trace="none",col=colfunc(25),scale="none", margins = c(10,21), cexRow=0.7,
  main="Top ranked pathways LPS all tissues" , cexCol=0.9 )
dev.off()

## X11cairo 
##        2

Mitch pathway analysis - OVA all tissues

ml <- list("hyp"=dge1_o,
  "amy"=dge2_o,
  "hip"=dge3_o,
  "pag"=dge4_o,
  "ni"=dge5_o)

m7 <- mitch_import(ml, DEtype="deseq2",geneTable=gt)

## Note: Mean no. genes in input = 17148.8

## Note: no. genes in output = 15857

## Note: estimated proportion of input genes in output = 0.925

head(m7)

##                       hyp          amy        hip         pag         ni
## 0610009B22Rik  0.09722066  1.831236577 -2.2697336  0.45655760 -1.1332185
## 0610009E02Rik  1.26326253 -0.103702849 -0.3519839 -0.40699076 -0.3290659
## 0610009L18Rik -0.66374969 -0.075637695 -0.9139963 -0.18691102  0.1306738
## 0610010K14Rik -0.45368981  0.417670579 -0.1751685 -0.37114848 -0.3126674
## 0610012G03Rik  0.12236243  0.008709563  0.5678859 -0.34216334 -0.2034285
## 0610030E20Rik -0.04562294 -1.609523779  1.4359134  0.08425395 -0.5747514

mres7 <- mitch_calc(m7, genesets, priority="effect")

## Note: Enrichments with large effect sizes may not be 
##             statistically significant.

head(mres7$enrichment_result,20) %>%
  kbl(caption = "Top gene pathway differences in all tissues caused by OVA") %>%
  kable_paper("hover", full_width = F)

Top gene pathway differences in all tissues caused by OVA

	set	setSize	pMANOVA	s.hyp	s.amy	s.hip	s.pag	s.ni	p.hyp	p.amy	p.hip	p.pag	p.ni	s.dist	SD	p.adjustMANOVA
301	EUKARYOTIC TRANSLATION ELONGATION	87	0.0000000	0.7589618	0.5066407	-0.5680187	-0.3419354	-0.0058543	0.0000000	0.0000000	0.0000000	0.0000000	0.9248618	1.1279660	0.5585329	0.0000000
998	SRP DEPENDENT COTRANSLATIONAL PROTEIN TARGETING TO MEMBRANE	106	0.0000000	0.6788859	0.4823198	-0.5362547	-0.2520128	-0.0296836	0.0000000	0.0000000	0.0000000	0.0000075	0.5978299	1.0224867	0.5054489	0.0000000
828	RESPONSE OF EIF2AK4 GCN2 TO AMINO ACID DEFICIENCY	94	0.0000000	0.6581896	0.4594290	-0.5305233	-0.3406766	-0.0378020	0.0000000	0.0000000	0.0000000	0.0000000	0.5268168	1.0213878	0.5085589	0.0000000
169	COMPLEX I BIOGENESIS	56	0.0000000	0.5241712	0.5287437	-0.4899260	-0.3224140	-0.3640163	0.0000000	0.0000000	0.0000000	0.0000301	0.0000025	1.0152889	0.5068935	0.0000000
302	EUKARYOTIC TRANSLATION INITIATION	114	0.0000000	0.6324738	0.4659147	-0.5439800	-0.2324542	-0.0823825	0.0000000	0.0000000	0.0000000	0.0000183	0.1290224	0.9868311	0.4904989	0.0000000
325	FORMATION OF ATP BY CHEMIOSMOTIC COUPLING	18	0.0000007	0.5447384	0.4759279	-0.4188887	-0.2661504	-0.3880436	0.0000630	0.0004727	0.0020933	0.0506243	0.0043713	0.9592335	0.4794735	0.0000085
27	ACTIVATION OF THE MRNA UPON BINDING OF THE CAP BINDING COMPLEX AND EIFS AND SUBSEQUENT BINDING TO 43S	59	0.0000000	0.5884482	0.4718018	-0.5477222	-0.1011756	-0.1302289	0.0000000	0.0000000	0.0000000	0.1791165	0.0837551	0.9466066	0.4691103	0.0000000
825	RESPIRATORY ELECTRON TRANSPORT	102	0.0000000	0.4633350	0.5045569	-0.4531907	-0.2711545	-0.3776604	0.0000000	0.0000000	0.0000000	0.0000023	0.0000000	0.9438172	0.4709548	0.0000000
782	REGULATION OF COMMISSURAL AXON PATHFINDING BY SLIT AND ROBO	10	0.0011474	-0.6280684	-0.3112261	0.4452199	0.0986054	0.4318925	0.0005831	0.0883696	0.0147754	0.5892841	0.0180374	0.9411729	0.4705160	0.0056398
1089	TRAFFICKING AND PROCESSING OF ENDOSOMAL TLR	11	0.0032586	0.0865030	0.6471722	-0.4574484	0.0249905	-0.4886464	0.6193918	0.0002016	0.0086139	0.8858964	0.0050127	0.9354002	0.4658185	0.0130794
826	RESPIRATORY ELECTRON TRANSPORT ATP SYNTHESIS BY CHEMIOSMOTIC COUPLING AND HEAT PRODUCTION BY UNCOUPLING PROTEINS	125	0.0000000	0.4663422	0.4972713	-0.4334727	-0.2415601	-0.3587643	0.0000000	0.0000000	0.0000000	0.0000032	0.0000000	0.9163599	0.4579111	0.0000000
72	ASPARTATE AND ASPARAGINE METABOLISM	10	0.0010490	-0.3787720	0.5353190	-0.1915694	0.4259229	-0.3954692	0.0380846	0.0033754	0.2942364	0.0196925	0.0303586	0.8969613	0.4484795	0.0052225
900	SELENOAMINO ACID METABOLISM	107	0.0000000	0.5894140	0.4100187	-0.4926794	-0.2055885	-0.0288468	0.0000000	0.0000000	0.0000000	0.0002412	0.6065202	0.8951849	0.4434311	0.0000000
806	REGULATION OF RUNX1 EXPRESSION AND ACTIVITY	17	0.0004842	0.0136289	-0.3895499	0.4433526	-0.2127748	0.6068627	0.9225118	0.0054263	0.0015523	0.1288539	0.0000147	0.8729559	0.4241025	0.0026370
1021	SYNTHESIS OF ACTIVE UBIQUITIN ROLES OF E1 AND E2 ENZYMES	29	0.0000044	0.2195237	0.4863751	-0.4949936	0.1092215	-0.4279845	0.0407878	0.0000058	0.0000040	0.3087943	0.0000664	0.8513930	0.4250128	0.0000443
1158	YAP1 AND WWTR1 TAZ STIMULATED GENE EXPRESSION	10	0.0026988	0.6409415	0.0983909	0.2452578	0.0289519	0.4780211	0.0004483	0.5900946	0.1793235	0.8740523	0.0088582	0.8426034	0.2574046	0.0113426
186	CRISTAE FORMATION	31	0.0000001	0.3865342	0.4230645	-0.4353065	-0.1600837	-0.4057105	0.0001957	0.0000457	0.0000273	0.1230208	0.0000925	0.8414938	0.4185622	0.0000007
152	CITRIC ACID CYCLE TCA CYCLE	22	0.0000386	0.0475357	0.4875621	-0.3250050	0.4738583	-0.3608462	0.6995825	0.0000753	0.0083248	0.0001193	0.0033933	0.8368729	0.4121520	0.0003108
625	NONSENSE MEDIATED DECAY NMD	109	0.0000000	0.5292345	0.3641959	-0.4407689	-0.2887520	-0.0092442	0.0000000	0.0000000	0.0000000	0.0000002	0.8677037	0.8309442	0.4140302	0.0000000
1109	TRANSCRIPTIONAL REGULATION OF PLURIPOTENT STEM CELLS	15	0.0000156	0.3655599	0.4782561	0.0412911	0.3561587	0.4263351	0.0142409	0.0013413	0.7819057	0.0169366	0.0042534	0.8201698	0.1706605	0.0001396

m7_top <- subset(mres7$enrichment_result,p.adjustMANOVA<0.05)
m7_up <- subset(m7_top,s.dist>0)$set
m7_dn <- subset(m7_top,s.dist<0)$set
mitch_report(mres7,outfile="mitch7.html",overwrite=TRUE)

## Note: overwriting existing report

## Dataset saved as " /tmp/RtmpXoIrRA/mitch7.rds ".

## 
## 
## processing file: mitch.Rmd

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## 5/34                             
## 6/34 [metrics]                   
## 7/34                             
## 8/34 [scatterplot]

## 9/34                             
## 10/34 [contourplot]

## 11/34                             
## 12/34 [input_geneset_metrics1]    
## 13/34                             
## 14/34 [input_geneset_metrics2]

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## 17/34                             
## 18/34 [echart1d]                  
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## 20/34                             
## 21/34 [heatmap]

## 22/34                             
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## 28/34                             
## 29/34 [detailed_geneset_reports1d]
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## 31/34 [detailed_geneset_reports2d]

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## 33/34 [session_info]              
## 34/34

## output file: /home/mark.ziemann@domain.internal.burnet.edu.au/projects/lung_brain/mitch.knit.md

## /usr/bin/pandoc +RTS -K512m -RTS /home/mark.ziemann@domain.internal.burnet.edu.au/projects/lung_brain/mitch.knit.md --to html4 --from markdown+autolink_bare_uris+tex_math_single_backslash --output /tmp/RtmpXoIrRA/mitch_report.html --lua-filter /usr/local/lib/R/site-library/rmarkdown/rmarkdown/lua/pagebreak.lua --lua-filter /usr/local/lib/R/site-library/rmarkdown/rmarkdown/lua/latex-div.lua --self-contained --variable bs3=TRUE --section-divs --template /usr/local/lib/R/site-library/rmarkdown/rmd/h/default.html --no-highlight --variable highlightjs=1 --variable theme=bootstrap --mathjax --variable 'mathjax-url=https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML' --include-in-header /tmp/RtmpXoIrRA/rmarkdown-str2d120534976783.html

## 
## Output created: /tmp/RtmpXoIrRA/mitch_report.html

## [1] TRUE

write.table(mres7$enrichment_result,file="mitch7.tsv",quote=FALSE,sep="\t",row.names=FALSE)

m7_top_up <- head(subset(m7_top,s.dist>0),10)[,"s.dist"]
names(m7_top_up) <- head(subset(m7_top,s.dist>0),10)[,"set"]
m7_top_dn <- head(subset(m7_top,s.dist<0),10)[,"s.dist"]
names(m7_top_dn) <- head(subset(m7_top,s.dist<0),10)[,"set"]
m7_top_updn <- c(m7_top_up,m7_top_dn)
m7_top_updn <- m7_top_updn[order(m7_top_updn)]

# heatmap
mr7 <- head(subset(mres7$enrichment_result,p.adjustMANOVA<0.05),30)
mr7 <- as.matrix(mr7[,4:8])
rownames(mr7) <- head(mres7$enrichment_result,30)$set

colfunc <- colorRampPalette(c("blue", "white", "red"))

heatmap.2(mr7,trace="none",col=colfunc(25),scale="none", margins = c(10,21), cexRow=0.7,
  main="Top ranked pathways OVA all tissues" , cexCol=0.9 )

pdf("plot39_mitch7_heatmap01.pdf")
heatmap.2(mr7,trace="none",col=colfunc(25),scale="none", margins = c(10,21), cexRow=0.7,
  main="Top ranked pathways OVA all tissues" , cexCol=0.9 )
dev.off()

## X11cairo 
##        2

Conclusion

There was some variability among samples in the hypothalamus, in particular some OVA samples could be classified as outliers (EA9-1 and EA3-1). Further investigation would be required to understand the origin of this variability. LPS resulted in the differential expression of just 30 genes in the hypothalamus. OVA was variable, so no significant genes were identified. Analysis with mitch identified some interesting pathways as seen in the mitch heatmap (see the mitch reports for details). The mitch software can perform multi-contrast pathway analysis and will be important to contrast the effect of the treatments in different tissues.

References

Bibliography

1.  Babraham bioinformatics - FastQC A quality control tool for high throughput sequence data. Babraham.ac.uk, https://www.bioinformatics.babraham.ac.uk/projects/fastqc/ (accessed February 23, 2022).

2.  Frankish A, Diekhans M, Jungreis I, et al. GENCODE 2021. Nucleic Acids Res 2021; 49: D916–D923.

3.  Jiang H, Lei R, Ding S-W, et al. Skewer: a fast and accurate adapter trimmer for next-generation sequencing paired-end reads. BMC Bioinformatics 2014; 15: 182.

4.  Bray NL, Pimentel H, Melsted P, et al. Near-optimal probabilistic RNA-seq quantification. Nat Biotechnol 2016; 34: 525–527.

5.  Ewels P, Magnusson M, Lundin S, et al. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics 2016; 32: 3047–3048.

6.  Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 2014; 15: 550.

7.  Liberzon A, Birger C, Thorvaldsdóttir H, et al. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst 2015; 1: 417–425.

8.  Jassal B, Matthews L, Viteri G, et al. The reactome pathway knowledgebase. Nucleic Acids Res 2020; 48: D498–D503.

9.  Dolgalev I. MSigDB Gene Sets for Multiple Organisms in a Tidy Data Format [R package msigdbr version 7.4.1], https://cran.r-project.org/web/packages/msigdbr/index.html (2021, accessed February 23, 2022).

10. Kaspi A, Ziemann M. Mitch: Multi-contrast pathway enrichment for multi-omics and single-cell profiling data. BMC Genomics 2020; 21: 447.

Session information

For reproducibility.

sessionInfo()

## R version 4.4.0 (2024-04-24)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.4 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0 
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## time zone: Australia/Melbourne
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] GGally_2.2.1                ggplot2_3.5.1              
##  [3] gtools_3.9.5                tibble_3.2.1               
##  [5] dplyr_1.1.4                 echarts4r_0.4.5            
##  [7] beeswarm_0.4.0              pkgload_1.3.4              
##  [9] vioplot_0.4.0               sm_2.2-6.0                 
## [11] kableExtra_1.4.0            topconfects_1.20.0         
## [13] limma_3.60.0                eulerr_7.0.2               
## [15] mitch_1.16.0                MASS_7.3-60.2              
## [17] fgsea_1.30.0                gplots_3.1.3.1             
## [19] DESeq2_1.44.0               SummarizedExperiment_1.34.0
## [21] Biobase_2.64.0              MatrixGenerics_1.16.0      
## [23] matrixStats_1.3.0           GenomicRanges_1.56.0       
## [25] GenomeInfoDb_1.40.0         IRanges_2.38.0             
## [27] S4Vectors_0.42.0            BiocGenerics_0.50.0        
## [29] reshape2_1.4.4              zoo_1.8-12                 
## 
## loaded via a namespace (and not attached):
##  [1] bitops_1.0-7            gridExtra_2.3           rlang_1.1.3            
##  [4] magrittr_2.0.3          compiler_4.4.0          polylabelr_0.2.0       
##  [7] systemfonts_1.1.0       vctrs_0.6.5             stringr_1.5.1          
## [10] pkgconfig_2.0.3         crayon_1.5.2            fastmap_1.2.0          
## [13] XVector_0.44.0          labeling_0.4.3          caTools_1.18.2         
## [16] utf8_1.2.4              promises_1.3.0          rmarkdown_2.27         
## [19] UCSC.utils_1.0.0        purrr_1.0.2             xfun_0.44              
## [22] zlibbioc_1.50.0         cachem_1.1.0            jsonlite_1.8.8         
## [25] progress_1.2.3          highr_0.11              later_1.3.2            
## [28] DelayedArray_0.30.1     BiocParallel_1.38.0     prettyunits_1.2.0      
## [31] parallel_4.4.0          R6_2.5.1                bslib_0.7.0            
## [34] stringi_1.8.4           RColorBrewer_1.1-3      jquerylib_0.1.4        
## [37] Rcpp_1.0.12             assertthat_0.2.1        knitr_1.47             
## [40] httpuv_1.6.15           Matrix_1.7-0            tidyselect_1.2.1       
## [43] rstudioapi_0.16.0       abind_1.4-5             yaml_2.3.8             
## [46] codetools_0.2-20        lattice_0.22-6          plyr_1.8.9             
## [49] withr_3.0.0             shiny_1.8.1.1           evaluate_0.23          
## [52] polyclip_1.10-6         ggstats_0.6.0           xml2_1.3.6             
## [55] pillar_1.9.0            KernSmooth_2.23-24      generics_0.1.3         
## [58] hms_1.1.3               munsell_0.5.1           scales_1.3.0           
## [61] xtable_1.8-4            glue_1.7.0              tools_4.4.0            
## [64] data.table_1.15.4       locfit_1.5-9.9          fastmatch_1.1-4        
## [67] cowplot_1.1.3           grid_4.4.0              tidyr_1.3.1            
## [70] colorspace_2.1-0        GenomeInfoDbData_1.2.12 cli_3.6.2              
## [73] fansi_1.0.6             S4Arrays_1.4.0          viridisLite_0.4.2      
## [76] svglite_2.1.3           gtable_0.3.5            sass_0.4.9             
## [79] digest_0.6.35           SparseArray_1.4.3       farver_2.1.2           
## [82] htmlwidgets_1.6.4       htmltools_0.5.8.1       lifecycle_1.0.4        
## [85] httr_1.4.7              statmod_1.5.0           mime_0.12