RXFP3 Antagonist RNA-seq
Mark Ziemann
2022-10-13
Source: https://github.com/markziemann/rxfp3_ant
Introduction
Izel Eraslan 189 Project Summary
Project 189 title / Aim: Determine whether the RXFP3 antagonist drug alter gene expression in mice brain that consumed high-fat diet.
Methodology:
Male C57BL/6J mice were divided into two groups: (1) treatment / RXFP3-antagonist 2 (n=6), and (2) control / vehicle (n=6) and
At the end of the experiment, RNA was isolation from five brain regions of interest from each mouse: hippocampal formation (HIP), hypothalamus (HYP), amygdala (AMG), periaqueductal gray (PAG) and nucleus incertus (NI).
Total RNA’s were extracted using the Qiagen RNeasy Kit (250), (Cat# 74106, Hilden, Germany).
Nanodrop, Qubit and Tapestation were used to complete the normalisation step.
We will be comparing genes between the control vs drug within each brain areas.
Data form: FASTQ (see same distribution shown in the table below)
ID: from 105 – 164 only
hyp = hypothalamus = 1
hip = hippocampus = 2
amg = amygdala = 3
pag = periaqueductal gray = 4
ni = nucleus incertus = 5
Sample 13-3 has been removed as it appears to be an outlier.
| FileBasename | BrainRegion | Treatment |
|---|---|---|
| IE11-1 | hyp | T |
| IE12-1 | hyp | T |
| IE13-1 | hyp | T |
| IE14-1 | hyp | T |
| IE16-1 | hyp | T |
| IE20-1 | hyp | T |
| IE18-1 | hyp | C |
| IE19-1 | hyp | C |
| IE21-1 | hyp | C |
| IE22-1 | hyp | C |
| IE23-1 | hyp | C |
| IE24-1 | hyp | C |
| IE10-2 | hip | T |
| IE11-2 | hip | T |
| IE12-2 | hip | T |
| IE13-2 | hip | T |
| IE14-2 | hip | T |
| IE16-2 | hip | T |
| IE17-2 | hip | C |
| IE19-2 | hip | C |
| IE21-2 | hip | C |
| IE22-2 | hip | C |
| IE23-2 | hip | C |
| IE24-2 | hip | C |
| IE10-3 | amy | T |
| IE11-3 | amy | T |
| IE13-3 | amy | T |
| IE14-3 | amy | T |
| IE16-3 | amy | T |
| IE17-3 | amy | C |
| IE18-3 | amy | C |
| IE19-3 | amy | C |
| IE20-3 | amy | T |
| IE21-3 | amy | C |
| IE22-3 | amy | C |
| IE23-3 | amy | C |
| IE10-4 | pag | T |
| IE11-4 | pag | T |
| IE13-4 | pag | T |
| IE14-4 | pag | T |
| IE16-4 | pag | T |
| IE20-4 | pag | T |
| IE17-4 | pag | C |
| IE18-4 | pag | C |
| IE19-4 | pag | C |
| IE22-4 | pag | C |
| IE23-4 | pag | C |
| IE24-4 | pag | C |
| IE10-5 | ni | T |
| IE11-5 | ni | T |
| IE12-5 | ni | T |
| IE13-5 | ni | T |
| IE16-5 | ni | T |
| IE20-5 | ni | T |
| IE17-5 | ni | C |
| IE18-5 | ni | C |
| IE19-5 | ni | C |
| IE21-5 | ni | C |
| IE23-5 | ni | C |
| IE24-5 | ni | C |
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("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$geneid = NULL
xx <- round(xx)
xx <- xx[,which(colnames(xx)!="test")]
xx[1:6,1:6]## IE10-2_S117_L001 IE10-2_S117_L002 IE10-3_S128_L001
## ENSMUSG00000000001.5 Gnai3 248 264 204
## ENSMUSG00000000003.16 Pbsn 0 0 0
## ENSMUSG00000000028.16 Cdc45 23 22 13
## ENSMUSG00000000031.17 H19 0 0 1
## ENSMUSG00000000037.18 Scml2 15 7 8
## ENSMUSG00000000049.12 Apoh 6 7 1
## IE10-3_S128_L002 IE10-4_S140_L001 IE10-4_S140_L002
## ENSMUSG00000000001.5 Gnai3 185 200 222
## ENSMUSG00000000003.16 Pbsn 0 0 0
## ENSMUSG00000000028.16 Cdc45 21 9 12
## ENSMUSG00000000031.17 H19 1 1 0
## ENSMUSG00000000037.18 Scml2 9 13 16
## ENSMUSG00000000049.12 Apoh 1 11 10dim(xx)## [1] 55357 120Fix the sample names.
They are duplicated for lane 1 and 2, which I will aggregate.
txx <- as.data.frame(t(xx))
txx$label <- sapply(strsplit(rownames(txx),"_"),"[[",1)
txx[1:3,c(1:4,ncol(txx))]## ENSMUSG00000000001.5 Gnai3 ENSMUSG00000000003.16 Pbsn
## IE10-2_S117_L001 248 0
## IE10-2_S117_L002 264 0
## IE10-3_S128_L001 204 0
## ENSMUSG00000000028.16 Cdc45 ENSMUSG00000000031.17 H19 label
## IE10-2_S117_L001 23 0 IE10-2
## IE10-2_S117_L002 22 0 IE10-2
## IE10-3_S128_L001 13 1 IE10-3txx2 <- aggregate(. ~ label,txx,sum)
txx2[1:3,1:4]## label ENSMUSG00000000001.5 Gnai3 ENSMUSG00000000003.16 Pbsn
## 1 IE10-2 512 0
## 2 IE10-3 389 0
## 3 IE10-4 422 0
## ENSMUSG00000000028.16 Cdc45
## 1 45
## 2 34
## 3 21rownames(txx2) <- txx2[,1]
txx2[,1] = NULL
xx <- as.data.frame(t(txx2))
xx[1:4,1:5]## IE10-2 IE10-3 IE10-4 IE10-5 IE11-1
## ENSMUSG00000000001.5 Gnai3 512 389 422 554 629
## ENSMUSG00000000003.16 Pbsn 0 0 0 0 0
## ENSMUSG00000000028.16 Cdc45 45 34 21 40 33
## ENSMUSG00000000031.17 H19 0 2 1 20 3write.table(xx,file="rxfp3_ant_counts.tsv",sep="\t",quote=FALSE)
rxx <- xx/colSums(xx) *1e6
rxx[1:4,1:5]## IE10-2 IE10-3 IE10-4 IE10-5
## ENSMUSG00000000001.5 Gnai3 29.309664 16.21939158 20.07208256 26.991885
## ENSMUSG00000000003.16 Pbsn 0.000000 0.00000000 0.00000000 0.000000
## ENSMUSG00000000028.16 Cdc45 2.273196 1.72973809 1.15186817 2.048159
## ENSMUSG00000000031.17 H19 0.000000 0.09605294 0.04570268 1.139502
## IE11-1
## ENSMUSG00000000001.5 Gnai3 35.4790547
## ENSMUSG00000000003.16 Pbsn 0.0000000
## ENSMUSG00000000028.16 Cdc45 1.5395650
## ENSMUSG00000000031.17 H19 0.1378804Samplesheet.
Need to delete “IE13-4” and “IE13-3”
ss <- read.table("rxfp3_ant_samplesheet.tsv",header=TRUE)
ss <- ss[order(ss$FileBasename),]
rownames(ss) <- ss$FileBasename
ss$FileBasename=NULL
ss <- ss[!rownames(ss)=="IE13-4",]
ss <- ss[!rownames(ss)=="IE13-3",]
head(ss)## BrainRegion Treatment
## IE10-2 hip T
## IE10-3 amy T
## IE10-4 pag T
## IE10-5 ni T
## IE11-1 hyp T
## IE11-2 hip TQC analysis
Here I’ll look at a few different quality control measures.
Firstly, the number of reads assigned to genes, which should be >15M.
par(mar=c(5,8,3,1))
barplot(colSums(xx),horiz=TRUE,las=1,xlab="num reads",col=ss$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")
Ribosomal RNA.
rrna <- read.table("https://raw.githubusercontent.com/markziemann/rxfp3_ant/main/rrna_res.tsv")
myrrna <- rrna$V2/10000
names(myrrna) <- rrna$V1
barplot(myrrna,horiz=TRUE,las=1,xlab="percent rRNA reads",cex.names=0.8)
barplot(head(myrrna,10),horiz=TRUE,las=1,xlab="percent rRNA reads",cex.names=1)
This work identified IE10-5 had a high degree of rRNA reads, but that other IE10 samples also had an elevated rate of rRNA reads.
It was agreed to remove these.
MDS plot for all samples
Multidimensional scaling plot to show the variation between all samples, very similar to PCA.
mds <- cmdscale(dist(t(xx)))
cols <- as.numeric(as.factor(ss$BrainRegion))+1
pchs <- as.numeric(factor(ss$Treatment))+17
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$BrainRegion)) , fill=unique(cols), cex=1.2)
legend("left", inset=.02, title="treatment",
legend=unique(as.factor(ss$Treatment)) , pch=unique(pchs), cex=1.2)
Now would be a good time to remove IE10 from the samplesheet and matrix and re-generate the MDS plot.
ss <- ss[grep("IE10",rownames(ss),invert=TRUE),]
xx <- xx[,grep("IE10",colnames(xx),invert=TRUE)]
xx <- xx[,grep("IE13-4",colnames(xx),invert=TRUE)]
xx <- xx[,grep("IE13-3",colnames(xx),invert=TRUE)]
mds <- cmdscale(dist(t(xx)))
cols <- as.numeric(as.factor(ss$BrainRegion))+1
pchs <- as.numeric(factor(ss$Treatment))+17
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("topleft", inset=.02, title="tissue",
legend=unique(as.factor(ss$BrainRegion)) , fill=unique(cols), cex=1.2)
legend("top", inset=.02, title="treatment",
legend=unique(as.factor(ss$Treatment)) , pch=unique(pchs), cex=1.2)
As you can see, the two MDS plots look completely different.
Correlation heatmap
heatmap.2(cor(xx),trace="n",main="Pearson correlation heatmap",
margin=c(6,6),cexRow=0.5,cexCol=0.5)
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.
dim(xx)## [1] 55357 54dim(ss)## [1] 54 2ss1 <- ss[which(ss$BrainRegion=="hyp"),]
xx1 <- xx[which(colnames(xx) %in% rownames(ss1))]
xx1 <- xx1[which(rowMeans(xx1)>=10),]
rpm1 <- xx1/colSums(xx1) *1e6
dim(xx1)## [1] 17535 12ss2 <- ss[which(ss$BrainRegion=="hip"),]
xx2 <- xx[which(colnames(xx) %in% rownames(ss2))]
xx2 <- xx2[which(rowMeans(xx2)>=10),]
rpm2 <- xx2/colSums(xx2) *1e6
dim(xx2)## [1] 16972 11ss3 <- ss[which(ss$BrainRegion=="amy"),]
xx3 <- xx[which(colnames(xx) %in% rownames(ss3))]
xx3 <- xx3[which(rowMeans(xx3)>=10),]
rpm3 <- xx3/colSums(xx3) *1e6
dim(xx3)## [1] 17026 10ss4 <- ss[which(ss$BrainRegion=="pag"),]
xx4 <- xx[which(colnames(xx) %in% rownames(ss4))]
xx4 <- xx4[which(rowMeans(xx4)>=10),]
rpm4 <- xx4/colSums(xx4) *1e6
dim(xx4)## [1] 17369 10ss5 <- ss[which(ss$BrainRegion=="ni"),]
xx5 <- xx[which(colnames(xx) %in% rownames(ss5))]
xx5 <- xx5[which(rowMeans(xx5)>=10),]
rpm5 <- xx5/colSums(xx5) *1e6
dim(xx5)## [1] 17337 11Differential expression with DESeq2
Contrast1 hypothalamus
dds <- DESeqDataSetFromMatrix(countData = xx1 , colData = ss1, design = ~ Treatment )## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factorsres <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testingz <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
dge[1:20,1:6] %>% kbl(caption = "Top gene expression differences for contrast 1: Effect of treatment in hypothalamus") %>%
kable_paper("hover", full_width = F)| baseMean | log2FoldChange | lfcSE | stat | pvalue | padj | |
|---|---|---|---|---|---|---|
| ENSMUSG00000020932.15 Gfap | 3463.56139 | 0.5482348 | 0.1184275 | 4.629285 | 0.0000037 | 0.0643083 |
| ENSMUSG00000023019.13 Gpd1 | 504.93923 | 0.5244407 | 0.1206264 | 4.347645 | 0.0000138 | 0.1205851 |
| ENSMUSG00000053747.10 Sox14 | 54.66721 | -0.7734123 | 0.1857589 | -4.163528 | 0.0000313 | 0.1524260 |
| ENSMUSG00000026090.17 Cracdl | 1140.99596 | 0.4251363 | 0.1026994 | 4.139617 | 0.0000348 | 0.1524260 |
| ENSMUSG00000104093.2 A330015K06Rik | 51.08307 | 0.7573175 | 0.1854097 | 4.084562 | 0.0000442 | 0.1547894 |
| ENSMUSG00000036915.18 Kirrel2 | 76.41197 | 0.8105330 | 0.2033264 | 3.986363 | 0.0000671 | 0.1754568 |
| ENSMUSG00000086040.9 Wipf3 | 751.96168 | 0.5667995 | 0.1425545 | 3.976019 | 0.0000701 | 0.1754568 |
| ENSMUSG00000048782.16 Insc | 110.41683 | -0.6272194 | 0.1595358 | -3.931527 | 0.0000844 | 0.1849167 |
| ENSMUSG00000102976.7 Zc3h11a | 945.35234 | -0.7021001 | 0.1807584 | -3.884190 | 0.0001027 | 0.1999360 |
| ENSMUSG00000021127.8 Zfp36l1 | 541.49181 | -0.2726250 | 0.0711977 | -3.829128 | 0.0001286 | 0.2082736 |
| ENSMUSG00000026751.15 Nr5a1 | 248.69604 | -0.4772473 | 0.1251652 | -3.812939 | 0.0001373 | 0.2082736 |
| ENSMUSG00000052384.15 Nrros | 107.15555 | 0.5945114 | 0.1568987 | 3.789143 | 0.0001512 | 0.2082736 |
| ENSMUSG00000022769.10 Sdf2l1 | 431.16214 | -0.5534786 | 0.1466210 | -3.774892 | 0.0001601 | 0.2082736 |
| ENSMUSG00000025056.5 Nr0b1 | 25.90191 | -0.9456400 | 0.2511482 | -3.765266 | 0.0001664 | 0.2082736 |
| ENSMUSG00000070532.6 Ccdc190 | 414.65567 | -0.3600428 | 0.0978189 | -3.680707 | 0.0002326 | 0.2717556 |
| ENSMUSG00000004891.17 Nes | 213.56808 | -0.4666380 | 0.1290344 | -3.616384 | 0.0002987 | 0.3272400 |
| ENSMUSG00000022041.11 Chrna2 | 29.67024 | 0.9396321 | 0.2619486 | 3.587086 | 0.0003344 | 0.3447405 |
| ENSMUSG00000032373.16 Car12 | 85.14698 | 0.6705937 | 0.1911167 | 3.508818 | 0.0004501 | 0.4132289 |
| ENSMUSG00000094626.2 Tmem121b | 260.83081 | 0.4419159 | 0.1263651 | 3.497137 | 0.0004703 | 0.4132289 |
| ENSMUSG00000027296.8 Itpka | 301.16462 | 0.5940839 | 0.1699125 | 3.496411 | 0.0004716 | 0.4132289 |
dge1 <- dge
d1up <- rownames(subset(dge1,padj <= 0.05 & log2FoldChange > 0))
d1dn <- rownames(subset(dge1,padj <= 0.05 & log2FoldChange < 0))
write.table(dge1,file="dge1.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$log2FoldChange,
pch=19, cex=0.5, col="red")
mtext(SUBHEADER,cex = 1)
}
make_volcano <- function(de,name) {
de <- de[which(!is.na(de$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(N_SIG,"@5%FDR,", N_UP, "up", N_DN, "dn", DET, "detected")
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_volcano2 <- function(de,name) {
de <- de[which(!is.na(de$padj)),]
de$pvalue[which(de$pvalue==0)] <- 1e-320
sig <- subset(de,padj<0.05)
N_SIG=nrow(sig)
N_UP=nrow(subset(sig,log2FoldChange>0))
N_DN=nrow(subset(sig,log2FoldChange<0))
DET=nrow(de)
HEADER=paste(N_SIG,"@5%FDR,", N_UP, "up", N_DN, "dn", DET, "detected")
top <- head(sig,30)
mylabels <- sapply(strsplit(rownames(top)," "),"[[",2)
plot(de$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$pval),cex=0.5,pch=19,col="red")
text(top$log2FoldChange+0.2,-log10(top$pval),labels=mylabels, srt=35 ,cex=0.7)
}
make_heatmap <- function(de,name,myss,mx,n=30){
colfunc <- colorRampPalette(c("blue", "white", "red"))
csc <- myss$Treatment
csc <- gsub("C","orange",csc)
csc <- gsub("T","gray",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, trt=gray")
}
make_heatmap2 <- function(de,name,myss,mx,n=30){
colfunc <- colorRampPalette(c("blue", "white", "red"))
csc <- myss$Treatment
csc <- gsub("C","orange",csc)
csc <- gsub("T","gray",csc)
mxn <- mx/rowSums(mx)*1000000
x <- mxn[which(rownames(mxn) %in% rownames(head(de,n))),]
rownames(x) <- sapply(strsplit(rownames(x)," "),"[[",2)
heatmap.2(as.matrix(x),trace="none",col=colfunc(25),scale="row", margins = c(10,15), cexRow=0.6,
main=paste("Top ranked",n,"genes in",name) , ColSideColors = csc )
mtext("ctrl=orange, trt=gray")
}
mymds <- function(de,name,myss,mx) {
mds <- cmdscale(dist(t(mx)))
csc <- myss$Treatment
csc <- gsub("C","orange",csc)
csc <- gsub("T","gray",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(ss$Treatment)) , pch=19, col=unique(csc), cex=1.4)
}
# make plots for contrast 1
maplot(dge1,"Cont1: Effect of treatment in hypothalamus")
make_volcano(dge1,"Cont1: Effect of treatment in hypothalamus")
#make_volcano2(dge1,"Cont1: Effect of treatment in hypothalamus")
make_heatmap(de=dge1,name="Cont1: Effect of treatment in hypothalamus",myss=ss1,mx=xx1,n=50)
make_heatmap2(de=dge1,name="Cont1: Effect of treatment in hypothalamus",myss=ss1,mx=xx1,n=50)
mymds(de=dge1,name="Cont1: Effect of treatment in hypothalamus",myss=ss1,mx=xx1)
Contrast 2 hippocampus
Now let’s look at hippocampus.
dds <- DESeqDataSetFromMatrix(countData = xx2 , colData = ss2, design = ~ Treatment )## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factorsres <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testingz <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
dge[1:20,1:6] %>% kbl(caption = "Top gene expression differences for contrast 2: Effect of treatment in hippocampus") %>%
kable_paper("hover", full_width = F)| baseMean | log2FoldChange | lfcSE | stat | pvalue | padj | |
|---|---|---|---|---|---|---|
| ENSMUSG00000015090.14 Ptgds | 7631.27985 | 0.5909818 | 0.1139510 | 5.186281 | 0.0000002 | 0.0036392 |
| ENSMUSG00000025509.16 Pnpla2 | 519.97071 | 0.4695349 | 0.1000300 | 4.693942 | 0.0000027 | 0.0227296 |
| ENSMUSG00000045903.10 Npas4 | 146.14138 | -1.7190450 | 0.3856613 | -4.457395 | 0.0000083 | 0.0469093 |
| ENSMUSG00000099702.2 Gm29013 | 667.59852 | -9.5083582 | 2.2398215 | -4.245141 | 0.0000218 | 0.0764584 |
| ENSMUSG00000031762.8 Mt2 | 1726.64956 | 0.4462752 | 0.1052995 | 4.238151 | 0.0000225 | 0.0764584 |
| ENSMUSG00000031875.8 Cmtm3 | 153.09149 | 0.5149781 | 0.1229918 | 4.187092 | 0.0000283 | 0.0798821 |
| ENSMUSG00000013584.6 Aldh1a2 | 115.55744 | 0.8330679 | 0.2079264 | 4.006553 | 0.0000616 | 0.1493020 |
| ENSMUSG00000044197.9 Gpr146 | 371.45900 | 0.3013527 | 0.0758755 | 3.971674 | 0.0000714 | 0.1513298 |
| ENSMUSG00000024175.3 Tekt4 | 30.25243 | 0.8983337 | 0.2335836 | 3.845877 | 0.0001201 | 0.2264035 |
| ENSMUSG00000030711.17 Sult1a1 | 179.23207 | 0.6608995 | 0.1738967 | 3.800529 | 0.0001444 | 0.2449245 |
| ENSMUSG00000030093.8 Wnt7a | 237.36354 | -0.4342229 | 0.1204186 | -3.605945 | 0.0003110 | 0.4236380 |
| ENSMUSG00000030218.3 Mgp | 216.16121 | 0.6462407 | 0.1810330 | 3.569739 | 0.0003573 | 0.4236380 |
| ENSMUSG00000033685.14 Ucp2 | 524.43602 | 0.3425102 | 0.0960094 | 3.567466 | 0.0003605 | 0.4236380 |
| ENSMUSG00000020571.14 Pdia6 | 2113.46243 | -0.3419865 | 0.0961530 | -3.556692 | 0.0003756 | 0.4236380 |
| ENSMUSG00000049928.16 Glp2r | 45.98092 | 0.8487002 | 0.2393322 | 3.546118 | 0.0003910 | 0.4236380 |
| ENSMUSG00000079018.11 Ly6c1 | 631.22791 | 0.3475472 | 0.0981673 | 3.540356 | 0.0003996 | 0.4236380 |
| ENSMUSG00000027333.19 Smox | 1016.83922 | 0.3554873 | 0.1009305 | 3.522101 | 0.0004281 | 0.4260585 |
| ENSMUSG00000022769.10 Sdf2l1 | 348.35095 | -0.7770456 | 0.2215296 | -3.507637 | 0.0004521 | 0.4260585 |
| ENSMUSG00000031381.17 Piga | 146.63845 | -0.5018544 | 0.1443730 | -3.476096 | 0.0005088 | 0.4478808 |
| ENSMUSG00000031765.9 Mt1 | 2909.69323 | 0.2896891 | 0.0835778 | 3.466099 | 0.0005281 | 0.4478808 |
dge2 <- dge
d2up <- rownames(subset(dge2,padj <= 0.05 & log2FoldChange > 0))
d2dn <- rownames(subset(dge2,padj <= 0.05 & log2FoldChange < 0))
write.table(dge2,file="dge2.tsv",quote=FALSE,sep="\t")
maplot(dge2,"Cont2: Effect of treatment in hippocampus")
make_volcano(dge2,"Cont2: Effect of treatment in hippocampus")
make_heatmap(de=dge2,name="Cont2: Effect of treatment in hippocampus",myss=ss2,mx=xx2,n=50)
make_heatmap2(de=dge2,name="Cont2: Effect of treatment in hippocampus",myss=ss2,mx=xx2,n=50)
mymds(de=dge2,name="Cont2: Effect of treatment in hippocampus",myss=ss2,mx=xx2)
Contrast 3 amygdala
Now let’s look at amygdala.
dds <- DESeqDataSetFromMatrix(countData = xx3 , colData = ss3, design = ~ Treatment )## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factorsres <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testingz <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
dge[1:20,1:6] %>% kbl(caption = "Top gene expression differences for contrast 3: Effect of treatment in amygdala") %>%
kable_paper("hover", full_width = F)| baseMean | log2FoldChange | lfcSE | stat | pvalue | padj | |
|---|---|---|---|---|---|---|
| ENSMUSG00000022769.10 Sdf2l1 | 309.50546 | -0.6501229 | 0.1582291 | -4.108744 | 0.0000398 | 0.6768445 |
| ENSMUSG00000070436.13 Serpinh1 | 361.83671 | -0.7276918 | 0.1928168 | -3.774007 | 0.0001606 | 0.9996008 |
| ENSMUSG00000032556.12 Bfsp2 | 185.71517 | -0.5918456 | 0.1581190 | -3.743038 | 0.0001818 | 0.9996008 |
| ENSMUSG00000004951.11 Hspb1 | 261.47583 | -0.9904978 | 0.2771225 | -3.574223 | 0.0003513 | 0.9996008 |
| ENSMUSG00000062210.14 Tnfaip8 | 153.50020 | -0.4900322 | 0.1380309 | -3.550164 | 0.0003850 | 0.9996008 |
| ENSMUSG00000099702.2 Gm29013 | 509.81432 | -7.9577620 | 2.3231459 | -3.425425 | 0.0006138 | 0.9996008 |
| ENSMUSG00000028270.13 Gbp2 | 64.71070 | -0.9949274 | 0.3133708 | -3.174920 | 0.0014988 | 0.9996008 |
| ENSMUSG00000053198.15 Prx | 19.96306 | 1.0073138 | 0.3212706 | 3.135406 | 0.0017162 | 0.9996008 |
| ENSMUSG00000103529.2 A730089K16Rik | 25.79234 | -1.2975614 | 0.4163585 | -3.116453 | 0.0018304 | 0.9996008 |
| ENSMUSG00000030357.11 Fkbp4 | 3325.95495 | -0.2797075 | 0.0905011 | -3.090652 | 0.0019972 | 0.9996008 |
| ENSMUSG00000017697.4 Ada | 31.82749 | 1.3771834 | 0.4481806 | 3.072832 | 0.0021204 | 0.9996008 |
| ENSMUSG00000078606.9 Gvin2 | 45.83501 | -0.9939342 | 0.3275471 | -3.034477 | 0.0024095 | 0.9996008 |
| ENSMUSG00000000823.17 Zfp512b | 978.43700 | 0.3290312 | 0.1097176 | 2.998892 | 0.0027096 | 0.9996008 |
| ENSMUSG00000064352.1 mt-Ts1 | 46.45364 | -0.8146884 | 0.2721471 | -2.993559 | 0.0027574 | 0.9996008 |
| ENSMUSG00000032035.17 Ets1 | 143.59413 | -0.5345523 | 0.1808156 | -2.956340 | 0.0031131 | 0.9996008 |
| ENSMUSG00000070532.6 Ccdc190 | 239.92270 | -0.4264681 | 0.1467326 | -2.906430 | 0.0036558 | 0.9996008 |
| ENSMUSG00000038181.17 Chpf2 | 932.45297 | 0.3253887 | 0.1130232 | 2.878954 | 0.0039900 | 0.9996008 |
| ENSMUSG00000034858.17 Fam214a | 602.73702 | 0.3410980 | 0.1201516 | 2.838897 | 0.0045270 | 0.9996008 |
| ENSMUSG00000019916.15 P4ha1 | 1130.39268 | -0.4368366 | 0.1551578 | -2.815434 | 0.0048711 | 0.9996008 |
| ENSMUSG00000105504.5 Gbp5 | 79.78489 | -0.9931229 | 0.3545360 | -2.801191 | 0.0050914 | 0.9996008 |
dge3 <- dge
d3up <- rownames(subset(dge3,padj <= 0.05 & log2FoldChange > 0))
d3dn <- rownames(subset(dge3,padj <= 0.05 & log2FoldChange < 0))
write.table(dge3,file="dge3.tsv",quote=FALSE,sep="\t")
maplot(dge3,"Cont3: Effect of treatment in amygdala")
make_volcano(dge3,"Cont3: Effect of treatment in amygdala")
make_heatmap(de=dge3,name="Cont3: Effect of treatment in amygdala",myss=ss3,mx=xx3,n=50)
make_heatmap2(de=dge3,name="Cont3: Effect of treatment in amygdala",myss=ss3,mx=xx3,n=50)
mymds(de=dge3,name="Cont3: Effect of treatment in amygdala",myss=ss3,mx=xx3)
Contrast 4 periaqueductal gray
Now let’s look at periaqueductal gray.
dds <- DESeqDataSetFromMatrix(countData = xx4 , colData = ss4, design = ~ Treatment )## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factorsres <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testingz <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
dge[1:20,1:6] %>% kbl(caption = "Top gene expression differences for contrast 4: Effect of treatment in periaqueductal gray") %>%
kable_paper("hover", full_width = F)| baseMean | log2FoldChange | lfcSE | stat | pvalue | padj | |
|---|---|---|---|---|---|---|
| ENSMUSG00000017697.4 Ada | 40.92107 | 1.7619972 | 0.2288479 | 7.699426 | 0.00e+00 | 0.0000000 |
| ENSMUSG00000025509.16 Pnpla2 | 791.54588 | 0.4968724 | 0.0852030 | 5.831627 | 0.00e+00 | 0.0000476 |
| ENSMUSG00000025739.14 Gng13 | 310.39452 | 0.8950549 | 0.1641595 | 5.452351 | 0.00e+00 | 0.0002876 |
| ENSMUSG00000031762.8 Mt2 | 1909.58045 | 0.9234951 | 0.1823564 | 5.064233 | 4.00e-07 | 0.0017793 |
| ENSMUSG00000002910.12 Arrdc2 | 568.19773 | 0.9997845 | 0.2065038 | 4.841483 | 1.30e-06 | 0.0044737 |
| ENSMUSG00000015090.14 Ptgds | 7835.31904 | 0.5048295 | 0.1074418 | 4.698631 | 2.60e-06 | 0.0075767 |
| ENSMUSG00000022018.8 Rgcc | 290.16112 | 0.6079672 | 0.1331140 | 4.567266 | 4.90e-06 | 0.0122522 |
| ENSMUSG00000052384.15 Nrros | 116.91691 | 0.9208285 | 0.2067898 | 4.452969 | 8.50e-06 | 0.0175467 |
| ENSMUSG00000030218.3 Mgp | 132.12498 | 1.0026931 | 0.2259559 | 4.437562 | 9.10e-06 | 0.0175467 |
| ENSMUSG00000031765.9 Mt1 | 3088.70566 | 0.6611353 | 0.1518318 | 4.354393 | 1.33e-05 | 0.0231605 |
| ENSMUSG00000062960.11 Kdr | 200.52005 | -0.7789281 | 0.1826241 | -4.265199 | 2.00e-05 | 0.0294020 |
| ENSMUSG00000004891.17 Nes | 241.06430 | -0.6058504 | 0.1424922 | -4.251814 | 2.12e-05 | 0.0294020 |
| ENSMUSG00000034579.18 Pla2g3 | 111.04202 | 0.8890157 | 0.2095083 | 4.243343 | 2.20e-05 | 0.0294020 |
| ENSMUSG00000050856.17 Atp5k | 514.90551 | 0.8855506 | 0.2118830 | 4.179431 | 2.92e-05 | 0.0362313 |
| ENSMUSG00000045232.5 Rln3 | 132.25654 | 0.8708868 | 0.2099734 | 4.147604 | 3.36e-05 | 0.0388765 |
| ENSMUSG00000017734.16 Dbndd2 | 4314.99233 | 0.3894214 | 0.0952994 | 4.086294 | 4.38e-05 | 0.0449163 |
| ENSMUSG00000090223.3 Pcp4 | 2813.35885 | 0.4306233 | 0.1056196 | 4.077116 | 4.56e-05 | 0.0449163 |
| ENSMUSG00000024222.18 Fkbp5 | 624.31891 | 0.6625456 | 0.1629774 | 4.065262 | 4.80e-05 | 0.0449163 |
| ENSMUSG00000047721.9 Bola2 | 584.10179 | 0.4719667 | 0.1162609 | 4.059547 | 4.92e-05 | 0.0449163 |
| ENSMUSG00000072692.8 Rpl37rt | 33.23180 | 2.1558220 | 0.5339995 | 4.037123 | 5.41e-05 | 0.0469599 |
dge4 <- dge
d4up <- rownames(subset(dge4,padj <= 0.05 & log2FoldChange > 0))
d4dn <- rownames(subset(dge4,padj <= 0.05 & log2FoldChange < 0))
write.table(dge4,file="dge4.tsv",quote=FALSE,sep="\t")
maplot(dge4,"Cont4: Effect of treatment in PAG")
make_volcano(dge4,"Cont4: Effect of treatment in PAG")
make_heatmap(de=dge4,name="Cont4: Effect of treatment in PAG",myss=ss4,mx=xx4,n=50)
make_heatmap2(de=dge4,name="Cont4: Effect of treatment in PAG",myss=ss4,mx=xx4,n=50)
mymds(de=dge4,name="Cont4: Effect of treatment in PAG",myss=ss4,mx=xx4)
Contrast 5 nucleus incertus
Now let’s look at nucleus incertus.
dds <- DESeqDataSetFromMatrix(countData = xx5 , colData = ss5, design = ~ Treatment )## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factorsres <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testingz <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
dge[1:20,1:6] %>% kbl(caption = "Top gene expression differences for contrast 5: Effect of treatment in nucleus incertus") %>%
kable_paper("hover", full_width = F)| baseMean | log2FoldChange | lfcSE | stat | pvalue | padj | |
|---|---|---|---|---|---|---|
| ENSMUSG00000024066.10 Xdh | 358.57733 | 0.6606023 | 0.1362459 | 4.848603 | 0.0000012 | 0.0186405 |
| ENSMUSG00000034579.18 Pla2g3 | 171.42852 | 0.9161040 | 0.1938807 | 4.725091 | 0.0000023 | 0.0186405 |
| ENSMUSG00000052384.15 Nrros | 110.60212 | 0.7375600 | 0.1585631 | 4.651523 | 0.0000033 | 0.0186405 |
| ENSMUSG00000034858.17 Fam214a | 527.75078 | 0.4093762 | 0.0905696 | 4.520016 | 0.0000062 | 0.0262365 |
| ENSMUSG00000041870.18 Ankrd13a | 1547.61555 | 0.3112764 | 0.0721955 | 4.311576 | 0.0000162 | 0.0550216 |
| ENSMUSG00000023019.13 Gpd1 | 1811.26150 | 0.4878561 | 0.1164400 | 4.189764 | 0.0000279 | 0.0682234 |
| ENSMUSG00000094472.2 Gm21897 | 163.47623 | -0.6280835 | 0.1507068 | -4.167585 | 0.0000308 | 0.0682234 |
| ENSMUSG00000004328.16 Hif3a | 126.36762 | 0.9588049 | 0.2328713 | 4.117317 | 0.0000383 | 0.0682234 |
| ENSMUSG00000028655.12 Mfsd2a | 660.57378 | 0.5692433 | 0.1390711 | 4.093182 | 0.0000425 | 0.0682234 |
| ENSMUSG00000025915.15 Sgk3 | 342.60759 | 0.5739088 | 0.1403590 | 4.088864 | 0.0000433 | 0.0682234 |
| ENSMUSG00000103502.2 9330121J05Rik | 14.40034 | 1.7195658 | 0.4210226 | 4.084261 | 0.0000442 | 0.0682234 |
| ENSMUSG00000031700.12 Gpt2 | 1226.89261 | 0.3563861 | 0.0896024 | 3.977416 | 0.0000697 | 0.0985339 |
| ENSMUSG00000001768.17 Rin2 | 667.34146 | 0.3441543 | 0.0876778 | 3.925214 | 0.0000867 | 0.1070151 |
| ENSMUSG00000074892.10 B3galt5 | 2250.00197 | 0.4796864 | 0.1223457 | 3.920745 | 0.0000883 | 0.1070151 |
| ENSMUSG00000112012.2 Gm47025 | 13.42103 | -2.2531635 | 0.5807365 | -3.879838 | 0.0001045 | 0.1182676 |
| ENSMUSG00000031833.11 Mast3 | 2139.86296 | 0.3456701 | 0.0896976 | 3.853729 | 0.0001163 | 0.1233997 |
| ENSMUSG00000021750.17 Fam107a | 2837.53976 | 0.4995966 | 0.1303382 | 3.833077 | 0.0001266 | 0.1263417 |
| ENSMUSG00000020422.15 Tns3 | 1907.89029 | 0.3177555 | 0.0847845 | 3.747800 | 0.0001784 | 0.1682043 |
| ENSMUSG00000004891.17 Nes | 274.64773 | -0.4653472 | 0.1248765 | -3.726460 | 0.0001942 | 0.1734610 |
| ENSMUSG00000050423.12 Ppp1r3g | 108.07189 | 0.8103904 | 0.2190030 | 3.700362 | 0.0002153 | 0.1826973 |
dge5 <- dge
d5up <- rownames(subset(dge5,padj <= 0.05 & log2FoldChange > 0))
d5dn <- rownames(subset(dge5,padj <= 0.05 & log2FoldChange < 0))
write.table(dge5,file="dge5.tsv",quote=FALSE,sep="\t")
maplot(dge5,"Cont5: Effect of treatment in NI")
make_volcano(dge5,"Cont5: Effect of treatment in NI")
make_heatmap(de=dge5,name="Cont5: Effect of treatment in NI",myss=ss5,mx=xx5,n=50)
make_heatmap2(de=dge5,name="Cont5: Effect of treatment in NI",myss=ss5,mx=xx5,n=50)
mymds(de=dge5,name="Cont5: Effect of treatment in NI",myss=ss5,mx=xx5)
Single contrast pathway analysis with mitch
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)Now run all the contrasts
# contrast1
m1 <- mitch_import(dge1, 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 = 17535## Note: no. genes in output = 17516## Note: estimated proportion of input genes in output = 0.999mres1 <- 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 contrast 1") %>% kable_paper("hover", full_width = F)| set | setSize | pANOVA | s.dist | p.adjustANOVA | |
|---|---|---|---|---|---|
| 443 | INCRETIN SYNTHESIS SECRETION AND INACTIVATION | 13 | 0.0002959 | -0.5796325 | 0.0033926 |
| 1057 | SYNTHESIS SECRETION AND INACTIVATION OF GLUCAGON LIKE PEPTIDE 1 GLP 1 | 12 | 0.0008603 | -0.5555778 | 0.0085383 |
| 434 | HSF1 ACTIVATION | 27 | 0.0000009 | -0.5463349 | 0.0000309 |
| 82 | ATF6 ATF6 ALPHA ACTIVATES CHAPERONE GENES | 10 | 0.0047833 | -0.5152176 | 0.0324659 |
| 84 | ATTENUATION PHASE | 25 | 0.0000084 | -0.5145252 | 0.0001892 |
| 306 | EUKARYOTIC TRANSLATION ELONGATION | 87 | 0.0000000 | -0.5080797 | 0.0000000 |
| 621 | NEUROTOXICITY OF CLOSTRIDIUM TOXINS | 10 | 0.0057566 | 0.5042728 | 0.0371507 |
| 172 | COMPLEX I BIOGENESIS | 56 | 0.0000000 | -0.5009982 | 0.0000000 |
| 630 | NITRIC OXIDE STIMULATES GUANYLATE CYCLASE | 21 | 0.0000999 | 0.4904122 | 0.0013885 |
| 181 | COOPERATION OF PREFOLDIN AND TRIC CCT IN ACTIN AND TUBULIN FOLDING | 26 | 0.0000181 | -0.4857105 | 0.0003501 |
| 9 | ACETYLCHOLINE NEUROTRANSMITTER RELEASE CYCLE | 17 | 0.0005282 | 0.4855303 | 0.0056392 |
| 148 | CHOLESTEROL BIOSYNTHESIS | 24 | 0.0000384 | -0.4854314 | 0.0006299 |
| 841 | RESPONSE OF EIF2AK4 GCN2 TO AMINO ACID DEFICIENCY | 95 | 0.0000000 | -0.4820927 | 0.0000000 |
| 1014 | SRP DEPENDENT COTRANSLATIONAL PROTEIN TARGETING TO MEMBRANE | 106 | 0.0000000 | -0.4776359 | 0.0000000 |
| 144 | CGMP EFFECTS | 15 | 0.0017745 | 0.4661029 | 0.0152970 |
| 341 | FORMATION OF TUBULIN FOLDING INTERMEDIATES BY CCT TRIC | 19 | 0.0007399 | -0.4471473 | 0.0074682 |
| 787 | REDUCTION OF CYTOSOLIC CA LEVELS | 11 | 0.0103992 | 0.4461634 | 0.0623422 |
| 28 | ACTIVATION OF THE MRNA UPON BINDING OF THE CAP BINDING COMPLEX AND EIFS AND SUBSEQUENT BINDING TO 43S | 59 | 0.0000000 | -0.4452966 | 0.0000002 |
| 75 | ASSEMBLY OF ACTIVE LPL AND LIPC LIPASE COMPLEXES | 10 | 0.0147959 | 0.4451160 | 0.0808978 |
| 522 | LONG TERM POTENTIATION | 23 | 0.0002446 | 0.4417941 | 0.0028887 |
m1top <- subset(mres1$enrichment_result,p.adjustANOVA<0.05)
m1up <- subset(m1top,s.dist>0)$set
m1dn <- subset(m1top,s.dist<0)$set
mitch_report(mres1,outfile="mitch1.html",overwrite=TRUE)## Note: overwriting existing report## Dataset saved as " /tmp/RtmpCa0Xdc/mitch1.rds ".##
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## Output created: /tmp/RtmpCa0Xdc/mitch_report.html## [1] TRUEwrite.table(mres1$enrichment_result,file="mitch1.tsv",quote=FALSE,sep="\t",row.names=FALSE)
m1top_up <- head(subset(m1top,s.dist>0),10)[,"s.dist"]
names(m1top_up) <- head(subset(m1top,s.dist>0),10)[,"set"]
m1top_dn <- head(subset(m1top,s.dist<0),10)[,"s.dist"]
names(m1top_dn) <- head(subset(m1top,s.dist<0),10)[,"set"]
m1top_updn <- c(m1top_up,m1top_dn)
m1top_updn <- m1top_updn[order(m1top_updn)]
par(mar=c(5,25,3,1))
barplot(m1top_updn,horiz=TRUE,las=1,col="darkgray",
xlab="Enrichment score",cex.names=0.8,xlim=c(-1,1),
main="Pathway changes in hypothalamus")
grid()
# contrast2
m2 <- mitch_import(dge2, DEtype="deseq2",geneTable=gt)## The input is a single dataframe; one contrast only. Converting
## it to a list for you.## Note: Mean no. genes in input = 16972## Note: no. genes in output = 16956## Note: estimated proportion of input genes in output = 0.999mres2 <- mitch_calc(m2, genesets, priority="effect")## Note: Enrichments with large effect sizes may not be
## statistically significant.head(mres2$enrichment_result,20) %>% kbl(caption = "Top gene pathway differences in contrast 2") %>% kable_paper("hover", full_width = F)| set | setSize | pANOVA | s.dist | p.adjustANOVA | |
|---|---|---|---|---|---|
| 82 | ATF6 ATF6 ALPHA ACTIVATES CHAPERONE GENES | 10 | 0.0002874 | -0.6621976 | 0.0151479 |
| 299 | ERYTHROPOIETIN ACTIVATES PHOSPHOINOSITIDE 3 KINASE PI3K | 11 | 0.0002642 | -0.6351833 | 0.0151479 |
| 330 | FORMATION OF FIBRIN CLOT CLOTTING CASCADE | 19 | 0.0000025 | 0.6238879 | 0.0012174 |
| 169 | COMMON PATHWAY OF FIBRIN CLOT FORMATION | 10 | 0.0007101 | 0.6182580 | 0.0252396 |
| 490 | INTRINSIC PATHWAY OF FIBRIN CLOT FORMATION | 12 | 0.0003324 | 0.5982452 | 0.0155979 |
| 101 | BIOTIN TRANSPORT AND METABOLISM | 11 | 0.0016015 | 0.5494622 | 0.0408392 |
| 45 | AKT PHOSPHORYLATES TARGETS IN THE NUCLEUS | 10 | 0.0087381 | -0.4788623 | 0.1116788 |
| 84 | ATTENUATION PHASE | 24 | 0.0000872 | -0.4626693 | 0.0073060 |
| 521 | LOSS OF FUNCTION OF MECP2 IN RETT SYNDROME | 13 | 0.0042532 | -0.4579200 | 0.0744624 |
| 757 | PURINE CATABOLISM | 16 | 0.0016898 | 0.4533943 | 0.0415736 |
| 676 | P38MAPK EVENTS | 12 | 0.0068113 | -0.4511331 | 0.0939955 |
| 1098 | TRAF6 MEDIATED IRF7 ACTIVATION | 15 | 0.0026196 | -0.4487535 | 0.0543124 |
| 913 | SEMA3A PLEXIN REPULSION SIGNALING BY INHIBITING INTEGRIN ADHESION | 14 | 0.0043859 | -0.4397693 | 0.0746235 |
| 300 | ERYTHROPOIETIN ACTIVATES RAS | 13 | 0.0063885 | -0.4368448 | 0.0892103 |
| 817 | REGULATION OF RUNX1 EXPRESSION AND ACTIVITY | 17 | 0.0021877 | -0.4291628 | 0.0484176 |
| 956 | SIGNALING BY FGFR4 IN DISEASE | 10 | 0.0187956 | -0.4290924 | 0.1688991 |
| 539 | MET ACTIVATES RAP1 AND RAC1 | 11 | 0.0139341 | -0.4281821 | 0.1472491 |
| 590 | MUCOPOLYSACCHARIDOSES | 11 | 0.0142189 | 0.4269160 | 0.1475992 |
| 187 | CREB1 PHOSPHORYLATION THROUGH THE ACTIVATION OF ADENYLATE CYCLASE | 11 | 0.0199680 | -0.4051986 | 0.1774432 |
| 83 | ATF6 ATF6 ALPHA ACTIVATES CHAPERONES | 12 | 0.0153164 | -0.4042729 | 0.1548803 |
m2top <- subset(mres2$enrichment_result,p.adjustANOVA<0.05)
m2up <- subset(m2top,s.dist>0)$set
m2dn <- subset(m2top,s.dist<0)$set
mitch_report(mres2,outfile="mitch2.html",overwrite=TRUE)## Note: overwriting existing report## Dataset saved as " /tmp/RtmpCa0Xdc/mitch2.rds ".##
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## Output created: /tmp/RtmpCa0Xdc/mitch_report.html
## [1] TRUEwrite.table(mres2$enrichment_result,file="mitch2.tsv",quote=FALSE,sep="\t",row.names=FALSE)
m2top_up <- head(subset(m2top,s.dist>0),10)[,"s.dist"]
names(m2top_up) <- head(subset(m2top,s.dist>0),10)[,"set"]
m2top_dn <- head(subset(m2top,s.dist<0),10)[,"s.dist"]
names(m2top_dn) <- head(subset(m2top,s.dist<0),10)[,"set"]
m2top_updn <- c(m2top_up,m2top_dn)
m2top_updn <- m2top_updn[order(m2top_updn)]
par(mar=c(5,25,3,1))
barplot(m2top_updn,horiz=TRUE,las=1,col="darkgray",
xlab="Enrichment score",cex.names=0.8,xlim=c(-1,1),
main="Pathway changes in hippocampus")
grid()
# contrast3
m3 <- mitch_import(dge3, 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 = 17026## Note: no. genes in output = 17009## Note: estimated proportion of input genes in output = 0.999mres3 <- 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 contrast 3") %>% kable_paper("hover", full_width = F)| set | setSize | pANOVA | s.dist | p.adjustANOVA | |
|---|---|---|---|---|---|
| 784 | REGULATION OF COMMISSURAL AXON PATHFINDING BY SLIT AND ROBO | 10 | 0.0019552 | 0.5655509 | 0.0528268 |
| 1095 | TRAFFICKING OF GLUR2 CONTAINING AMPA RECEPTORS | 17 | 0.0000794 | 0.5527861 | 0.0092376 |
| 1022 | SYNAPTIC ADHESION LIKE MOLECULES | 21 | 0.0000389 | 0.5185229 | 0.0050330 |
| 167 | COMMON PATHWAY OF FIBRIN CLOT FORMATION | 11 | 0.0032301 | -0.5127876 | 0.0767314 |
| 1161 | WNT5A DEPENDENT INTERNALIZATION OF FZD4 | 15 | 0.0007088 | 0.5049547 | 0.0343750 |
| 80 | ATF6 ATF6 ALPHA ACTIVATES CHAPERONE GENES | 10 | 0.0059235 | -0.5025707 | 0.1227313 |
| 109 | CAMK IV MEDIATED PHOSPHORYLATION OF CREB | 10 | 0.0067302 | 0.4948879 | 0.1238489 |
| 141 | CGMP EFFECTS | 15 | 0.0009398 | 0.4932957 | 0.0364639 |
| 251 | DNA METHYLATION | 20 | 0.0004418 | -0.4538466 | 0.0244860 |
| 511 | LGI ADAM INTERACTIONS | 14 | 0.0034807 | 0.4509982 | 0.0810306 |
| 1117 | TRANSLATION OF REPLICASE AND ASSEMBLY OF THE REPLICATION TRANSCRIPTION COMPLEX | 12 | 0.0069224 | 0.4502363 | 0.1238489 |
| 913 | SEROTONIN NEUROTRANSMITTER RELEASE CYCLE | 18 | 0.0010177 | 0.4473087 | 0.0378767 |
| 1093 | TRAFFICKING AND PROCESSING OF ENDOSOMAL TLR | 11 | 0.0111935 | -0.4416990 | 0.1434856 |
| 234 | DISEASES ASSOCIATED WITH GLYCOSYLATION PRECURSOR BIOSYNTHESIS | 18 | 0.0012611 | 0.4390233 | 0.0407742 |
| 653 | NUCLEOTIDE LIKE PURINERGIC RECEPTORS | 12 | 0.0085232 | -0.4385774 | 0.1298460 |
| 583 | MUCOPOLYSACCHARIDOSES | 11 | 0.0119957 | 0.4374632 | 0.1481193 |
| 1094 | TRAFFICKING OF AMPA RECEPTORS | 31 | 0.0000284 | 0.4344142 | 0.0045620 |
| 917 | SHC MEDIATED CASCADE FGFR4 | 11 | 0.0151782 | -0.4228091 | 0.1749246 |
| 953 | SIGNALING BY HIPPO | 20 | 0.0012357 | -0.4172700 | 0.0407742 |
| 483 | INTRINSIC PATHWAY OF FIBRIN CLOT FORMATION | 13 | 0.0100981 | -0.4120698 | 0.1369165 |
m3top <- subset(mres3$enrichment_result,p.adjustANOVA<0.05)
m3up <- subset(m3top,s.dist>0)$set
m3dn <- subset(m3top,s.dist<0)$set
mitch_report(mres3,outfile="mitch3.html",overwrite=TRUE)## Note: overwriting existing report## Dataset saved as " /tmp/RtmpCa0Xdc/mitch3.rds ".##
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## Output created: /tmp/RtmpCa0Xdc/mitch_report.html
## [1] TRUEwrite.table(mres3$enrichment_result,file="mitch3.tsv",quote=FALSE,sep="\t",row.names=FALSE)
m3top_up <- head(subset(m3top,s.dist>0),10)[,"s.dist"]
names(m3top_up) <- head(subset(m3top,s.dist>0),10)[,"set"]
m3top_dn <- head(subset(m3top,s.dist<0),10)[,"s.dist"]
names(m3top_dn) <- head(subset(m3top,s.dist<0),10)[,"set"]
m3top_updn <- c(m3top_up,m3top_dn)
m3top_updn <- m3top_updn[order(m3top_updn)]
par(mar=c(5,25,3,1))
barplot(m3top_updn,horiz=TRUE,las=1,col="darkgray",
xlab="Enrichment score",cex.names=0.8,xlim=c(-1,1),
main="Pathway changes in amygdala")
grid()
# contrast4
m4 <- mitch_import(dge4, DEtype="deseq2",geneTable=gt)## The input is a single dataframe; one contrast only. Converting
## it to a list for you.## Note: Mean no. genes in input = 17369## Note: no. genes in output = 17352## Note: estimated proportion of input genes in output = 0.999mres4 <- mitch_calc(m4, genesets, priority="effect")## Note: Enrichments with large effect sizes may not be
## statistically significant.head(mres4$enrichment_result,20) %>% kbl(caption = "Top gene pathway differences in contrast 4") %>% kable_paper("hover", full_width = F)| set | setSize | pANOVA | s.dist | p.adjustANOVA | |
|---|---|---|---|---|---|
| 305 | EUKARYOTIC TRANSLATION ELONGATION | 87 | 0.00e+00 | 0.8621755 | 0.0000000 |
| 840 | RESPONSE OF EIF2AK4 GCN2 TO AMINO ACID DEFICIENCY | 95 | 0.00e+00 | 0.7588054 | 0.0000000 |
| 1013 | SRP DEPENDENT COTRANSLATIONAL PROTEIN TARGETING TO MEMBRANE | 106 | 0.00e+00 | 0.7587726 | 0.0000000 |
| 572 | MITOCHONDRIAL TRANSLATION | 93 | 0.00e+00 | 0.7530439 | 0.0000000 |
| 306 | EUKARYOTIC TRANSLATION INITIATION | 114 | 0.00e+00 | 0.7481034 | 0.0000000 |
| 331 | FORMATION OF ATP BY CHEMIOSMOTIC COUPLING | 18 | 0.00e+00 | 0.7466892 | 0.0000008 |
| 36 | ADENYLATE CYCLASE ACTIVATING PATHWAY | 10 | 4.94e-05 | -0.7410679 | 0.0004382 |
| 172 | COMPLEX I BIOGENESIS | 56 | 0.00e+00 | 0.7245379 | 0.0000000 |
| 914 | SELENOAMINO ACID METABOLISM | 108 | 0.00e+00 | 0.7196899 | 0.0000000 |
| 837 | RESPIRATORY ELECTRON TRANSPORT | 102 | 0.00e+00 | 0.6995328 | 0.0000000 |
| 189 | CRISTAE FORMATION | 31 | 0.00e+00 | 0.6924971 | 0.0000000 |
| 838 | RESPIRATORY ELECTRON TRANSPORT ATP SYNTHESIS BY CHEMIOSMOTIC COUPLING AND HEAT PRODUCTION BY UNCOUPLING PROTEINS | 125 | 0.00e+00 | 0.6886980 | 0.0000000 |
| 635 | NONSENSE MEDIATED DECAY NMD | 109 | 0.00e+00 | 0.6719573 | 0.0000000 |
| 37 | ADENYLATE CYCLASE INHIBITORY PATHWAY | 13 | 2.94e-05 | -0.6691585 | 0.0002843 |
| 28 | ACTIVATION OF THE MRNA UPON BINDING OF THE CAP BINDING COMPLEX AND EIFS AND SUBSEQUENT BINDING TO 43S | 59 | 0.00e+00 | 0.6626890 | 0.0000000 |
| 571 | MITOCHONDRIAL PROTEIN IMPORT | 63 | 0.00e+00 | 0.6473379 | 0.0000000 |
| 570 | MITOCHONDRIAL IRON SULFUR CLUSTER BIOGENESIS | 13 | 6.83e-05 | 0.6378018 | 0.0005796 |
| 1131 | TRANSLATION | 286 | 0.00e+00 | 0.6210619 | 0.0000000 |
| 711 | PKA MEDIATED PHOSPHORYLATION OF CREB | 19 | 3.10e-06 | -0.6181303 | 0.0000408 |
| 883 | RNA POLYMERASE III CHAIN ELONGATION | 18 | 8.70e-06 | 0.6053036 | 0.0000999 |
m4top <- subset(mres4$enrichment_result,p.adjustANOVA<0.05)
m43up <- subset(m4top,s.dist>0)$set
m4dn <- subset(m4top,s.dist<0)$set
mitch_report(mres4,outfile="mitch4.html",overwrite=TRUE)## Note: overwriting existing report## Dataset saved as " /tmp/RtmpCa0Xdc/mitch4.rds ".##
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## ordinary text without R code## output file: /media/mdz/bfx101/projects/craig_smith/RXFP3_antagonist_izel/dge/mitch.knit.md## /usr/bin/pandoc +RTS -K512m -RTS /media/mdz/bfx101/projects/craig_smith/RXFP3_antagonist_izel/dge/mitch.knit.md --to html4 --from markdown+autolink_bare_uris+tex_math_single_backslash --output /tmp/RtmpCa0Xdc/mitch_report.html --lua-filter /usr/local/lib/R/site-library/rmarkdown/rmarkdown/lua/pagebreak.lua --lua-filter /usr/local/lib/R/site-library/rmarkdown/rmarkdown/lua/latex-div.lua --self-contained --variable bs3=TRUE --section-divs --template /usr/local/lib/R/site-library/rmarkdown/rmd/h/default.html --no-highlight --variable highlightjs=1 --variable theme=bootstrap --mathjax --variable 'mathjax-url=https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML' --include-in-header /tmp/RtmpCa0Xdc/rmarkdown-str1ea61290e9cf2.html##
## Output created: /tmp/RtmpCa0Xdc/mitch_report.html
## [1] TRUEwrite.table(mres4$enrichment_result,file="mitch4.tsv",quote=FALSE,sep="\t",row.names=FALSE)
m4top_up <- head(subset(m4top,s.dist>0),10)[,"s.dist"]
names(m4top_up) <- head(subset(m4top,s.dist>0),10)[,"set"]
m4top_dn <- head(subset(m4top,s.dist<0),10)[,"s.dist"]
names(m4top_dn) <- head(subset(m4top,s.dist<0),10)[,"set"]
m4top_updn <- c(m4top_up,m4top_dn)
m4top_updn <- m4top_updn[order(m4top_updn)]
par(mar=c(5,25,3,1))
barplot(m4top_updn,horiz=TRUE,las=1,col="darkgray",
xlab="Enrichment score",cex.names=0.8,xlim=c(-1,1),
main="Pathway changes in periaqueductal gray")
grid()
# contrast5
m5 <- mitch_import(dge5, 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 = 17337## Note: no. genes in output = 17319## Note: estimated proportion of input genes in output = 0.999mres5 <- 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 contrast 5") %>% kable_paper("hover", full_width = F)| set | setSize | pANOVA | s.dist | p.adjustANOVA | |
|---|---|---|---|---|---|
| 306 | EUKARYOTIC TRANSLATION ELONGATION | 88 | 0.0000000 | -0.6622749 | 0.0000000 |
| 840 | RESPONSE OF EIF2AK4 GCN2 TO AMINO ACID DEFICIENCY | 96 | 0.0000000 | -0.6436354 | 0.0000000 |
| 911 | SCAVENGING OF HEME FROM PLASMA | 12 | 0.0002038 | 0.6191811 | 0.0043759 |
| 1014 | SRP DEPENDENT COTRANSLATIONAL PROTEIN TARGETING TO MEMBRANE | 107 | 0.0000000 | -0.5959035 | 0.0000000 |
| 4 | ABC TRANSPORTERS IN LIPID HOMEOSTASIS | 13 | 0.0003330 | 0.5747229 | 0.0066649 |
| 85 | ATTENUATION PHASE | 25 | 0.0000013 | -0.5599260 | 0.0000463 |
| 633 | NONSENSE MEDIATED DECAY NMD | 110 | 0.0000000 | -0.5576775 | 0.0000000 |
| 1050 | SYNTHESIS OF PIPS AT THE LATE ENDOSOME MEMBRANE | 11 | 0.0015760 | 0.5502763 | 0.0218966 |
| 816 | REGULATION OF PYRUVATE DEHYDROGENASE PDH COMPLEX | 15 | 0.0004372 | 0.5243874 | 0.0081957 |
| 915 | SELENOAMINO ACID METABOLISM | 110 | 0.0000000 | -0.5127645 | 0.0000000 |
| 307 | EUKARYOTIC TRANSLATION INITIATION | 115 | 0.0000000 | -0.4877824 | 0.0000000 |
| 927 | SEROTONIN RECEPTORS | 10 | 0.0086186 | 0.4797158 | 0.0714138 |
| 74 | ASPARTATE AND ASPARAGINE METABOLISM | 11 | 0.0061866 | 0.4767107 | 0.0575306 |
| 524 | LYSOSPHINGOLIPID AND LPA RECEPTORS | 13 | 0.0031592 | -0.4728196 | 0.0351983 |
| 1042 | SYNTHESIS OF IP2 IP AND INS IN THE CYTOSOL | 13 | 0.0041035 | 0.4597338 | 0.0428871 |
| 162 | CLEC7A DECTIN 1 INDUCES NFAT ACTIVATION | 11 | 0.0107090 | 0.4443873 | 0.0786204 |
| 793 | REGULATION OF EXPRESSION OF SLITS AND ROBOS | 165 | 0.0000000 | -0.4419473 | 0.0000000 |
| 105 | BRANCHED CHAIN AMINO ACID CATABOLISM | 21 | 0.0004803 | 0.4401114 | 0.0087263 |
| 325 | FGFR2 ALTERNATIVE SPLICING | 25 | 0.0001648 | -0.4353001 | 0.0036727 |
| 1048 | SYNTHESIS OF PIPS AT THE EARLY ENDOSOME MEMBRANE | 16 | 0.0026045 | 0.4347656 | 0.0307590 |
m5top <- subset(mres5$enrichment_result,p.adjustANOVA<0.05)
m5up <- subset(m5top,s.dist>0)$set
m5dn <- subset(m5top,s.dist<0)$set
mitch_report(mres5,outfile="mitch5.html",overwrite=TRUE)## Note: overwriting existing report## Dataset saved as " /tmp/RtmpCa0Xdc/mitch5.rds ".##
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## ordinary text without R code## output file: /media/mdz/bfx101/projects/craig_smith/RXFP3_antagonist_izel/dge/mitch.knit.md## /usr/bin/pandoc +RTS -K512m -RTS /media/mdz/bfx101/projects/craig_smith/RXFP3_antagonist_izel/dge/mitch.knit.md --to html4 --from markdown+autolink_bare_uris+tex_math_single_backslash --output /tmp/RtmpCa0Xdc/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/RtmpCa0Xdc/rmarkdown-str1ea6169d11b95.html##
## Output created: /tmp/RtmpCa0Xdc/mitch_report.html
## [1] TRUEwrite.table(mres5$enrichment_result,file="mitch5.tsv",quote=FALSE,sep="\t",row.names=FALSE)
m5top_up <- head(subset(m5top,s.dist>0),10)[,"s.dist"]
names(m5top_up) <- head(subset(m5top,s.dist>0),10)[,"set"]
m5top_dn <- head(subset(m5top,s.dist<0),10)[,"s.dist"]
names(m5top_dn) <- head(subset(m5top,s.dist<0),10)[,"set"]
m5top_updn <- c(m5top_up,m5top_dn)
m5top_updn <- m5top_updn[order(m5top_updn)]
par(mar=c(5,25,3,1))
barplot(m5top_updn,horiz=TRUE,las=1,col="darkgray",
xlab="Enrichment score",cex.names=0.8,xlim=c(-1,1),
main="Pathway changes in nucleus incertus")
grid()
multi-contrast mitch
dl <- list("hyp"=dge1,"hip"=dge2,"amy"=dge3,"pag"=dge4,"ni"=dge5)
mm <- mitch_import(dl, DEtype="deseq2",geneTable=gt)## Note: Mean no. genes in input = 17247.8## Note: no. genes in output = 15984## Note: estimated proportion of input genes in output = 0.927mmres1 <- mitch_calc(mm, genesets, priority="effect")## Note: Enrichments with large effect sizes may not be
## statistically significant.head(mmres1$enrichment_result,20) %>%
kbl(caption = "Top multi contrast enrichment results") %>%
kable_paper("hover", full_width = F)| set | setSize | pMANOVA | s.hyp | s.hip | s.amy | s.pag | s.ni | p.hyp | p.hip | p.amy | p.pag | p.ni | s.dist | SD | p.adjustMANOVA | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 298 | EUKARYOTIC TRANSLATION ELONGATION | 87 | 0.0000000 | -0.5117838 | 0.1370106 | -0.2038055 | 0.8573807 | -0.6781204 | 0.0000000 | 0.0272835 | 0.0010238 | 0.0000000 | 0.0000000 | 1.2317387 | 0.6093622 | 0.0000000 |
| 820 | RESPONSE OF EIF2AK4 GCN2 TO AMINO ACID DEFICIENCY | 94 | 0.0000000 | -0.4870466 | 0.0737932 | -0.2113721 | 0.7619231 | -0.6598088 | 0.0000000 | 0.2166509 | 0.0004009 | 0.0000000 | 0.0000000 | 1.1415833 | 0.5587058 | 0.0000000 |
| 990 | SRP DEPENDENT COTRANSLATIONAL PROTEIN TARGETING TO MEMBRANE | 106 | 0.0000000 | -0.4811226 | 0.0605252 | -0.2064088 | 0.7549416 | -0.6105683 | 0.0000000 | 0.2820752 | 0.0002434 | 0.0000000 | 0.0000000 | 1.1047521 | 0.5417311 | 0.0000000 |
| 80 | ATTENUATION PHASE | 23 | 0.0000000 | -0.5548279 | -0.4740332 | -0.3901929 | -0.3817648 | -0.5939232 | 0.0000041 | 0.0000830 | 0.0011989 | 0.0015289 | 0.0000008 | 1.0877873 | 0.0952933 | 0.0000000 |
| 78 | ATF6 ATF6 ALPHA ACTIVATES CHAPERONE GENES | 10 | 0.0004533 | -0.5162890 | -0.6619757 | -0.5029047 | -0.1705772 | -0.4140729 | 0.0046975 | 0.0002888 | 0.0058913 | 0.3503363 | 0.0233740 | 1.0762120 | 0.1812765 | 0.0022510 |
| 299 | EUKARYOTIC TRANSLATION INITIATION | 114 | 0.0000000 | -0.4262406 | 0.1062260 | -0.1351596 | 0.7438232 | -0.5010513 | 0.0000000 | 0.0502993 | 0.0127507 | 0.0000000 | 0.0000000 | 1.0077492 | 0.5016312 | 0.0000000 |
| 892 | SELENOAMINO ACID METABOLISM | 107 | 0.0000000 | -0.4229106 | 0.1276172 | -0.1583982 | 0.7171598 | -0.5253488 | 0.0000000 | 0.0226795 | 0.0046759 | 0.0000000 | 0.0000000 | 1.0052557 | 0.4992050 | 0.0000000 |
| 619 | NONSENSE MEDIATED DECAY NMD | 109 | 0.0000000 | -0.3847657 | 0.0938450 | -0.1199122 | 0.6687816 | -0.5718662 | 0.0000000 | 0.0908064 | 0.0306976 | 0.0000000 | 0.0000000 | 0.9723838 | 0.4810981 | 0.0000000 |
| 166 | COMPLEX I BIOGENESIS | 56 | 0.0000000 | -0.5039127 | 0.0518359 | -0.0644059 | 0.7216851 | -0.4024472 | 0.0000000 | 0.5024468 | 0.4046890 | 0.0000000 | 0.0000002 | 0.9713682 | 0.4836773 | 0.0000000 |
| 420 | HSF1 ACTIVATION | 25 | 0.0000001 | -0.5865405 | -0.3994160 | -0.4184047 | -0.1862849 | -0.4171013 | 0.0000004 | 0.0005468 | 0.0002933 | 0.1069725 | 0.0003064 | 0.9419665 | 0.1423825 | 0.0000014 |
| 26 | ACTIVATION OF THE MRNA UPON BINDING OF THE CAP BINDING COMPLEX AND EIFS AND SUBSEQUENT BINDING TO 43S | 59 | 0.0000000 | -0.4483825 | 0.0814570 | -0.1129308 | 0.6585882 | -0.4117276 | 0.0000000 | 0.2794087 | 0.1337162 | 0.0000000 | 0.0000000 | 0.9075757 | 0.4507871 | 0.0000000 |
| 557 | MITOCHONDRIAL TRANSLATION | 93 | 0.0000000 | -0.3173494 | 0.2559473 | -0.0358673 | 0.7476654 | -0.2666438 | 0.0000001 | 0.0000201 | 0.5502979 | 0.0000000 | 0.0000089 | 0.8930893 | 0.4382224 | 0.0000000 |
| 817 | RESPIRATORY ELECTRON TRANSPORT | 102 | 0.0000000 | -0.4396036 | 0.0841624 | -0.0591470 | 0.6972809 | -0.2975264 | 0.0000000 | 0.1422483 | 0.3024096 | 0.0000000 | 0.0000002 | 0.8823580 | 0.4411665 | 0.0000000 |
| 33 | ADENYLATE CYCLASE ACTIVATING PATHWAY | 10 | 0.0011788 | 0.3019532 | -0.2608990 | 0.1772255 | -0.7361462 | 0.1803556 | 0.0982719 | 0.1531511 | 0.3318777 | 0.0000554 | 0.3234089 | 0.8746955 | 0.4307869 | 0.0049745 |
| 555 | MITOCHONDRIAL IRON SULFUR CLUSTER BIOGENESIS | 13 | 0.0002229 | -0.3983326 | 0.2205488 | -0.1612008 | 0.6305515 | -0.3487330 | 0.0128963 | 0.1686051 | 0.3143066 | 0.0000826 | 0.0294849 | 0.8674712 | 0.4335472 | 0.0011835 |
| 775 | REGULATION OF EXPRESSION OF SLITS AND ROBOS | 159 | 0.0000000 | -0.4125035 | 0.0692849 | -0.0752217 | 0.5934353 | -0.4661898 | 0.0000000 | 0.1321709 | 0.1021296 | 0.0000000 | 0.0000000 | 0.8660925 | 0.4281230 | 0.0000000 |
| 320 | FORMATION OF ATP BY CHEMIOSMOTIC COUPLING | 18 | 0.0000002 | -0.3067978 | -0.0183167 | -0.1286623 | 0.7443039 | -0.2651189 | 0.0242448 | 0.8929966 | 0.3447213 | 0.0000000 | 0.0515246 | 0.8574909 | 0.4287078 | 0.0000019 |
| 818 | RESPIRATORY ELECTRON TRANSPORT ATP SYNTHESIS BY CHEMIOSMOTIC COUPLING AND HEAT PRODUCTION BY UNCOUPLING PROTEINS | 125 | 0.0000000 | -0.3893375 | 0.0804374 | -0.0696947 | 0.6866557 | -0.2704095 | 0.0000000 | 0.1207826 | 0.1788588 | 0.0000000 | 0.0000002 | 0.8411471 | 0.4204893 | 0.0000000 |
| 980 | SLBP DEPENDENT PROCESSING OF REPLICATION DEPENDENT HISTONE PRE MRNAS | 11 | 0.0011628 | -0.4418308 | 0.1654781 | 0.1988298 | 0.5367296 | -0.3636876 | 0.0111709 | 0.3420049 | 0.2535657 | 0.0020532 | 0.0367540 | 0.8261223 | 0.4125086 | 0.0049463 |
| 774 | REGULATION OF COMMISSURAL AXON PATHFINDING BY SLIT AND ROBO | 10 | 0.0010666 | 0.2328409 | -0.1032177 | 0.5667460 | -0.4900088 | 0.2075372 | 0.2023603 | 0.5719863 | 0.0019128 | 0.0072934 | 0.2558282 | 0.8180770 | 0.3984306 | 0.0046365 |
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## List of 3
## $ include: logi TRUE
## $ echo : logi TRUE
## $ results: chr "markup"
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## ordinary text without R code## output file: /media/mdz/bfx101/projects/craig_smith/RXFP3_antagonist_izel/dge/mitch.knit.md## /usr/bin/pandoc +RTS -K512m -RTS /media/mdz/bfx101/projects/craig_smith/RXFP3_antagonist_izel/dge/mitch.knit.md --to html4 --from markdown+autolink_bare_uris+tex_math_single_backslash --output /tmp/RtmpCa0Xdc/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/RtmpCa0Xdc/rmarkdown-str1ea61624a17f7.html##
## Output created: /tmp/RtmpCa0Xdc/mitch_report.html## [1] TRUEwrite.table(mmres1$enrichment_result,file="multimitch1.tsv",quote=FALSE,sep="\t",row.names=FALSE)
mmtop <- subset(mmres1$enrichment_result,p.adjustMANOVA<0.05)
mmtop <- head(mmtop,25)
mmx <- as.matrix(mmtop[,4:8])
colnames(mmx) <- gsub("s.","",colnames(mmx))
rownames(mmx) <- mmtop$set
colfunc <- colorRampPalette(c("blue", "white", "red"))
heatmap.2(mmx,trace="none",col=colfunc(25), margins = c(10,25), cexRow=0.7,
cexCol=0.8, main=paste("Top ranked pathways" ) )
Conclusion
IE10 was removed as the rRNA content was too high.
With a FDR<0.05 threshold here are the number of DEGs
Hypothalamus: 0
Hippocampus: 4
Amygdala: 1
PAG: 20
NI: 4
These numbers make sense as the MDS plots show some overlap between sample groups, ie: samples don’t appear to cluster in distinct groups.
There is some variability between samples, which I am guessing is related to the precision with which the different tissues can be excised. This was the case in another experiment that I was involved with that was examining the hypothalamus in sand rats, which showed some pituitary gland contamination.
This is a source of variation that can be problematic, although if it is identified, can be used to remove samples which suffer highest levels of contamination.
If the contamination is less extensive, then it can be incorporated into the model so it can be corrected for.
Still, mitch was able to detect some trends.
References
Bibliography
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).
Frankish A, Diekhans M, Jungreis I, et al. GENCODE 2021. Nucleic Acids Res 2021; 49: D916–D923.
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.
Bray NL, Pimentel H, Melsted P, et al. Near-optimal probabilistic RNA-seq quantification. Nat Biotechnol 2016; 34: 525–527.
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.
Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 2014; 15: 550.
Liberzon A, Birger C, Thorvaldsdóttir H, et al. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst 2015; 1: 417–425.
Jassal B, Matthews L, Viteri G, et al. The reactome pathway knowledgebase. Nucleic Acids Res 2020; 48: D498–D503.
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).
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 4.2.1 (2022-06-23)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.1 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so
##
## 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
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] pkgload_1.3.0 GGally_2.1.2
## [3] gtools_3.9.3 echarts4r_0.4.4
## [5] beeswarm_0.4.0 vioplot_0.3.7
## [7] sm_2.2-5.7.1 kableExtra_1.3.4
## [9] topconfects_1.12.0 limma_3.52.4
## [11] eulerr_6.1.1 mitch_1.8.0
## [13] MASS_7.3-58.1 fgsea_1.22.0
## [15] gplots_3.1.3 DESeq2_1.36.0
## [17] SummarizedExperiment_1.26.1 Biobase_2.56.0
## [19] MatrixGenerics_1.8.1 matrixStats_0.62.0
## [21] GenomicRanges_1.48.0 GenomeInfoDb_1.32.4
## [23] IRanges_2.30.1 S4Vectors_0.34.0
## [25] BiocGenerics_0.42.0 reshape2_1.4.4
## [27] forcats_0.5.2 stringr_1.4.1
## [29] dplyr_1.0.10 purrr_0.3.5
## [31] readr_2.1.3 tidyr_1.2.1
## [33] tibble_3.1.8 ggplot2_3.3.6
## [35] tidyverse_1.3.2 zoo_1.8-11
##
## loaded via a namespace (and not attached):
## [1] readxl_1.4.1 backports_1.4.1 fastmatch_1.1-3
## [4] systemfonts_1.0.4 plyr_1.8.7 splines_4.2.1
## [7] BiocParallel_1.30.3 digest_0.6.29 htmltools_0.5.3
## [10] fansi_1.0.3 magrittr_2.0.3 memoise_2.0.1
## [13] googlesheets4_1.0.1 tzdb_0.3.0 Biostrings_2.64.1
## [16] annotate_1.74.0 modelr_0.1.9 svglite_2.1.0
## [19] prettyunits_1.1.1 colorspace_2.0-3 blob_1.2.3
## [22] rvest_1.0.3 haven_2.5.1 xfun_0.33
## [25] crayon_1.5.2 RCurl_1.98-1.9 jsonlite_1.8.2
## [28] genefilter_1.78.0 survival_3.4-0 glue_1.6.2
## [31] gtable_0.3.1 gargle_1.2.1 zlibbioc_1.42.0
## [34] XVector_0.36.0 webshot_0.5.4 DelayedArray_0.22.0
## [37] scales_1.2.1 DBI_1.1.3 Rcpp_1.0.9
## [40] viridisLite_0.4.1 xtable_1.8-4 progress_1.2.2
## [43] bit_4.0.4 htmlwidgets_1.5.4 httr_1.4.4
## [46] RColorBrewer_1.1-3 ellipsis_0.3.2 pkgconfig_2.0.3
## [49] reshape_0.8.9 XML_3.99-0.11 farver_2.1.1
## [52] sass_0.4.2 dbplyr_2.2.1 locfit_1.5-9.6
## [55] utf8_1.2.2 tidyselect_1.2.0 labeling_0.4.2
## [58] rlang_1.0.6 later_1.3.0 AnnotationDbi_1.58.0
## [61] munsell_0.5.0 cellranger_1.1.0 tools_4.2.1
## [64] cachem_1.0.6 cli_3.4.1 generics_0.1.3
## [67] RSQLite_2.2.18 broom_1.0.1 evaluate_0.17
## [70] fastmap_1.1.0 yaml_2.3.5 knitr_1.40
## [73] bit64_4.0.5 fs_1.5.2 caTools_1.18.2
## [76] KEGGREST_1.36.3 mime_0.12 xml2_1.3.3
## [79] compiler_4.2.1 rstudioapi_0.14 png_0.1-7
## [82] reprex_2.0.2 geneplotter_1.74.0 bslib_0.4.0
## [85] stringi_1.7.8 highr_0.9 lattice_0.20-45
## [88] Matrix_1.5-1 vctrs_0.4.2 pillar_1.8.1
## [91] lifecycle_1.0.3 jquerylib_0.1.4 data.table_1.14.2
## [94] bitops_1.0-7 httpuv_1.6.6 R6_2.5.1
## [97] promises_1.2.0.1 KernSmooth_2.23-20 gridExtra_2.3
## [100] codetools_0.2-18 assertthat_0.2.1 withr_2.5.0
## [103] GenomeInfoDbData_1.2.8 parallel_4.2.1 hms_1.1.2
## [106] grid_4.2.1 rmarkdown_2.17 googledrive_2.0.0
## [109] shiny_1.7.2 lubridate_1.8.0