MBD-seq analysis: effect of acetate and fibre on heart and spleen DNA methylation

Source: https://github.com/markziemann/fran_mbd/blob/master/main_report_meth.Rmd

Introduction

In this report, I will take you through an analysis of the MBD-seq that was previosly generated. I will perform an analysis that begins with DESeq2 for differential methylation followed by pathway enrichment analysis with mitch.

suppressPackageStartupMessages({
    # to be run in R4
    library("tidyverse")
    library("reshape2")
    library("DESeq2")
    library("gplots")
    library("fgsea")
    library("mitch")
})

Import profiling data

This analysis uses the 3kbp area arount the TSS.

hm <- read.table("heart_meth_3kbpTSS.txt",header=TRUE,row.names=1)
sm <-read.table("spleen_meth_3kbpTSS.txt",header=TRUE,row.names=1)
b <- data.frame(sm,hm)

MDS plots

plot(cmdscale(dist(t(b))), bty='l', xlab="Coordinate 1", ylab="Coordinate 2", 
 type = "p", pch=19, cex.axis=1.3, cex.lab=1.3 , col="gray") 
text(cmdscale(dist(t(b))), labels=colnames(b),cex=1.3) 
mtext("spleen and heart samples")

plot(cmdscale(dist(t(hm))), bty='l', xlab="Coordinate 1", ylab="Coordinate 2",
 type = "p", pch=19, cex.axis=1.3, cex.lab=1.3 , col="gray")
text(cmdscale(dist(t(hm))), labels=colnames(hm),cex=1.3)
mtext("heart samples")

plot(cmdscale(dist(t(sm))), bty='l', xlab="Coordinate 1", ylab="Coordinate 2",
 type = "p", pch=19, cex.axis=1.3, cex.lab=1.3 , col="gray")
text(cmdscale(dist(t(sm))), labels=colnames(sm),cex=1.3)
mtext("spleen samples")

Curate the samplesheet

ss_sm <- as.data.frame(colnames(sm))
colnames(ss_sm) <- "samplename"
ss_sm$group  <- sapply(strsplit(as.character(ss_sm$samplename) , "_"),"[[",2)
ss_sm$group <- gsub("[0-9]+","",ss_sm$group)
rownames(ss_sm) <- ss_sm$samplename
ss_sm$samplename=NULL

ss_hm <- as.data.frame(colnames(hm))
colnames(ss_hm) <- "samplename"
ss_hm$group  <- sapply(strsplit(as.character(ss_hm$samplename) , "_"),"[[",2)
ss_hm$group <- gsub("[0-9]+","",ss_hm$group)
rownames(ss_hm) <- ss_hm$samplename
ss_hm$samplename=NULL

Differential analysis

We will be running some comparisons between groups:

1. HEART ctrl vs acetate

2. HEART ctrl vs hifib

3. SPLEEN ctrl vs acetate

4. SPLEEN ctrl vs hifibre

HEART ctrl vs acetate

x <- hm[,grep("hifi" , colnames(hm) ,invert=TRUE)]
x<-x[which(rowSums(x)/ncol(x)>=(10)),]
xs <- ss_hm[which( rownames(ss_hm) %in% colnames(x) ),1,drop=FALSE]
xs$trt <- factor(as.numeric(grepl("acetate",xs$group)))
dds <- DESeqDataSetFromMatrix(countData = x , colData = xs, design = ~ trt )
res <- DESeq(dds)

## estimating size factors

## estimating dispersions

## gene-wise dispersion estimates

## mean-dispersion relationship

## final dispersion estimates

## 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),])
head(dge)

##                              baseMean log2FoldChange     lfcSE      stat
## ENSMUSG00000065881_Gm22866  119.44050     -1.1822235 0.1953514 -6.051780
## ENSMUSG00000037410_Tbc1d2b  341.09596     -0.6874842 0.1693469 -4.059621
## ENSMUSG00000095041_ensembl 2011.26913     -0.4232428 0.1175254 -3.601287
## ENSMUSG00000097088_Gm26615   88.60375     -0.6820023 0.2078954 -3.280507
## ENSMUSG00000084652_Gm22717   77.77103     -0.8097174 0.2606830 -3.106138
## ENSMUSG00000001504_Irx2      43.95295      0.8353355 0.2776611  3.008472
##                                  pvalue         padj H11_acetate1 H12_acetate2
## ENSMUSG00000065881_Gm22866 1.432537e-09 4.590422e-05     7.541961     7.497128
## ENSMUSG00000037410_Tbc1d2b 4.915234e-05 7.875188e-01     8.739803     8.358340
## ENSMUSG00000095041_ensembl 3.166462e-04 9.998971e-01    10.982013    10.796732
## ENSMUSG00000097088_Gm26615 1.036206e-03 9.998971e-01     7.513994     7.357383
## ENSMUSG00000084652_Gm22717 1.895484e-03 9.998971e-01     7.165050     7.523048
## ENSMUSG00000001504_Irx2    2.625651e-03 9.998971e-01     7.362324     7.252666
##                            H17_acetate3  H1_ctrl1  H2_ctrl2  H3_ctrl3  H4_ctrl4
## ENSMUSG00000065881_Gm22866     7.348881  7.942201  8.135014  8.139167  8.128256
## ENSMUSG00000037410_Tbc1d2b     8.511046  8.800931  9.064895  9.173260  9.129819
## ENSMUSG00000095041_ensembl    10.679232 11.221358 11.141312 11.380333 11.123503
## ENSMUSG00000097088_Gm26615     7.422928  7.843455  7.702842  7.646114  7.835305
## ENSMUSG00000084652_Gm22717     7.182478  7.493675  7.709468  7.761882  7.764490
## ENSMUSG00000001504_Irx2        7.438707  6.945925  7.025067  6.997513  7.174816

write.table(dge,file="heart_ctrl_vs_acetate_meth.tsv",quote=F,sep="\t")
#some plots
sig<-subset(dge,padj<0.05)
SIG=nrow(sig)
DN=nrow(subset(sig,log2FoldChange<0))
UP=nrow(subset(sig,log2FoldChange>0))
HEADER=paste("ctrl vs acetate:", SIG , "DGEs,", UP ,"up,", DN, "down")
plot(log2(dge$baseMean),dge$log2FoldChange,cex=0.6,cex.axis=1.2,cex.lab=1.3, 
 xlab="log2 base mean",
 ,ylab="log2 fold change" ,pch=19,col="#838383")
points(log2(sig$baseMean),sig$log2FoldChange,cex=0.6,pch=19,col="red")
mtext((HEADER),cex=1.2)

top<-head(sig,20)
plot(dge$log2FoldChange, -log2(dge$pvalue)+1E-307 ,cex=0.6, cex.lab=1.3,cex.axis=1.2,
 xlim=c(-3,3),xlab="log2 fold change", ylab="-log2 p-value" ,pch=19,col="#838383")
points(sig$log2FoldChange, -log2(sig$pvalue)+1E-307, cex=0.6,pch=19,col="red")  
mtext((HEADER),cex=1.2)

# top N gene heatmap
colfunc <- colorRampPalette(c("blue", "white", "red"))
heatmap.2(  as.matrix(dge[1:50,c(7:ncol(dge))]), col=colfunc(25),scale="row",
 trace="none",margins = c(6,20), cexRow=.6, cexCol=.8,  main="Top 50 genes")

heart_ctrl_vs_acetate <- dge

HEART ctrl vs hifib

x <- hm[,grep("acet" , colnames(hm) ,invert=TRUE)]
x<-x[which(rowSums(x)/ncol(x)>=(10)),]
xs <- ss_hm[which( rownames(ss_hm) %in% colnames(x) ),1,drop=FALSE]
xs$trt <- factor(as.numeric(grepl("hifi",xs$group)))
dds <- DESeqDataSetFromMatrix(countData = x , colData = xs, design = ~ trt )
res <- DESeq(dds)

## estimating size factors

## estimating dispersions

## gene-wise dispersion estimates

## mean-dispersion relationship

## final dispersion estimates

## 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),])
head(dge)

##                             baseMean log2FoldChange      lfcSE       stat
## ENSMUSG00000095041_ensembl 2024.3388     -0.7158460 0.06297373 -11.367374
## ENSMUSG00000013787_Ehmt2    419.8667     -0.9635358 0.09081939 -10.609362
## ENSMUSG00000039963_Ccdc40   701.7145     -0.8285823 0.08152785 -10.163181
## ENSMUSG00000096385_Gm11168 9806.0787     -1.7563980 0.18572160  -9.457155
## ENSMUSG00000029528_Pxn      821.2177     -0.7438034 0.07910190  -9.403104
## ENSMUSG00000003344_Btbd2    461.0102     -0.7648601 0.08228591  -9.295153
##                                  pvalue         padj H16_hifibre4  H1_ctrl1
## ENSMUSG00000095041_ensembl 6.078868e-30 1.999401e-25    10.793391 11.444947
## ENSMUSG00000013787_Ehmt2   2.695827e-26 4.433422e-22     8.870514  9.721718
## ENSMUSG00000039963_Ccdc40  2.894755e-24 3.173713e-20     9.413502 10.245066
## ENSMUSG00000096385_Gm11168 3.164357e-21 2.601972e-17    12.110791 14.021451
## ENSMUSG00000029528_Pxn     5.297691e-21 3.484927e-17     9.632690 10.442093
## ENSMUSG00000003344_Btbd2   1.469948e-20 8.058008e-17     9.065999  9.679114
##                             H2_ctrl2  H3_ctrl3  H4_ctrl4 H6_hifibre1
## ENSMUSG00000095041_ensembl 11.351463 11.594427 11.336955   10.815631
## ENSMUSG00000013787_Ehmt2    9.523461  9.599376  9.664665    9.041826
## ENSMUSG00000039963_Ccdc40  10.029892 10.238312 10.103269    9.512575
## ENSMUSG00000096385_Gm11168 13.578698 14.281076 13.642716   12.252494
## ENSMUSG00000029528_Pxn     10.164232 10.326470 10.312677    9.745816
## ENSMUSG00000003344_Btbd2    9.582249  9.718230  9.722136    9.214489
##                            H7_hifibre2 H8_hifibre3
## ENSMUSG00000095041_ensembl   10.749302   10.831877
## ENSMUSG00000013787_Ehmt2      9.033033    9.035120
## ENSMUSG00000039963_Ccdc40     9.578831    9.623038
## ENSMUSG00000096385_Gm11168   12.079331   12.373338
## ENSMUSG00000029528_Pxn        9.722490    9.837287
## ENSMUSG00000003344_Btbd2      9.184498    9.159734

write.table(dge,file="heart_ctrl_vs_hifibre_meth.tsv",quote=F,sep="\t")
#some plots
sig<-subset(dge,padj<0.05)
SIG=nrow(sig)
DN=nrow(subset(sig,log2FoldChange<0))
UP=nrow(subset(sig,log2FoldChange>0))
HEADER=paste("ctrl vs hifibre:", SIG , "DGEs,", UP ,"up,", DN, "down")
plot(log2(dge$baseMean),dge$log2FoldChange,cex=0.6,cex.axis=1.2,cex.lab=1.3,
 xlab="log2 base mean",
 ,ylab="log2 fold change" ,pch=19,col="#838383")
points(log2(sig$baseMean),sig$log2FoldChange,cex=0.6,pch=19,col="red")
mtext((HEADER),cex=1.2)

top<-head(sig,20)
plot(dge$log2FoldChange, -log2(dge$pvalue)+1E-307 ,cex=0.6, cex.lab=1.3,cex.axis=1.2,
 xlim=c(-3,3),xlab="log2 fold change", ylab="-log2 p-value" ,pch=19,col="#838383")
points(sig$log2FoldChange, -log2(sig$pvalue)+1E-307, cex=0.6,pch=19,col="red")
mtext((HEADER),cex=1.2)

# top N gene heatmap
colfunc <- colorRampPalette(c("blue", "white", "red"))
heatmap.2(  as.matrix(dge[1:50,c(7:ncol(dge))]), col=colfunc(25),scale="row",
 trace="none",margins = c(6,20), cexRow=.6, cexCol=.8,  main="Top 50 genes")

heart_ctrl_vs_hifibre <- dge

SPLEEN ctrl vs acetate

x <- sm[,grep("hifi" , colnames(sm) ,invert=TRUE)]
x<-x[which(rowSums(x)/ncol(x)>=(10)),]
xs <- ss_sm[which( rownames(ss_sm) %in% colnames(x) ),1,drop=FALSE]
xs$trt <- factor(as.numeric(grepl("acetate",xs$group)))
dds <- DESeqDataSetFromMatrix(countData = x , colData = xs, design = ~ trt )
res <- DESeq(dds)

## estimating size factors

## estimating dispersions

## gene-wise dispersion estimates

## mean-dispersion relationship

## final dispersion estimates

## 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),])
head(dge)

##                             baseMean log2FoldChange     lfcSE      stat
## ENSMUSG00000037410_Tbc1d2b 226.98895     -1.0062896 0.1536148 -6.550733
## ENSMUSG00000065881_Gm22866 105.99838     -0.9808053 0.1812587 -5.411079
## ENSMUSG00000084652_Gm22717  79.26900     -0.8786940 0.2329171 -3.772562
## ENSMUSG00000020108_Ddit4    12.11947      1.6388904 0.4621883  3.545937
## ENSMUSG00000018927_Ccl6     24.96163      1.0881223 0.3085803  3.526221
## ENSMUSG00000092638_Mir5112 118.65491     -0.5341145 0.1591893 -3.355216
##                                  pvalue         padj S11_acetate1 S12_acetate2
## ENSMUSG00000037410_Tbc1d2b 5.725534e-11 1.377277e-06     7.590616     7.731347
## ENSMUSG00000065881_Gm22866 6.264616e-08 7.534767e-04     7.088689     7.070440
## ENSMUSG00000084652_Gm22717 1.615800e-04 9.999144e-01     7.166388     6.795793
## ENSMUSG00000020108_Ddit4   3.912199e-04 9.999144e-01     6.092944     6.486539
## ENSMUSG00000018927_Ccl6    4.215347e-04 9.999144e-01     6.695451     6.726862
## ENSMUSG00000092638_Mir5112 7.930297e-04 9.999144e-01     7.400632     7.404015
##                            S17_acetate3 S18_acetate4 S1_ctrl1 S2_ctrl2 S3_ctrl3
## ENSMUSG00000037410_Tbc1d2b     7.925797     8.028534 8.586076 8.500335 8.675853
## ENSMUSG00000065881_Gm22866     7.109710     7.341191 7.836086 7.616556 7.741383
## ENSMUSG00000084652_Gm22717     7.093675     6.839659 7.106675 7.550427 7.644150
## ENSMUSG00000020108_Ddit4       6.363366     6.376970 5.919704 5.927311 6.023386
## ENSMUSG00000018927_Ccl6        6.619333     6.606533 6.162694 6.415892 6.292950
## ENSMUSG00000092638_Mir5112     7.513502     7.363802 7.740781 7.773462 7.650071
##                            S4_ctrl4
## ENSMUSG00000037410_Tbc1d2b 8.566182
## ENSMUSG00000065881_Gm22866 7.867163
## ENSMUSG00000084652_Gm22717 7.513759
## ENSMUSG00000020108_Ddit4   5.860576
## ENSMUSG00000018927_Ccl6    6.186185
## ENSMUSG00000092638_Mir5112 7.867163

write.table(dge,file="spleen_ctrl_vs_acetate_meth.tsv",quote=F,sep="\t")
#some plots
sig<-subset(dge,padj<0.05)
SIG=nrow(sig)
DN=nrow(subset(sig,log2FoldChange<0))
UP=nrow(subset(sig,log2FoldChange>0))
HEADER=paste("ctrl vs acetate:", SIG , "DGEs,", UP ,"up,", DN, "down")
plot(log2(dge$baseMean),dge$log2FoldChange,cex=0.6,cex.axis=1.2,cex.lab=1.3,
 xlab="log2 base mean",
 ,ylab="log2 fold change" ,pch=19,col="#838383")
points(log2(sig$baseMean),sig$log2FoldChange,cex=0.6,pch=19,col="red")
mtext((HEADER),cex=1.2)

top<-head(sig,20)
plot(dge$log2FoldChange, -log2(dge$pvalue)+1E-307 ,cex=0.6, cex.lab=1.3,cex.axis=1.2,
 xlim=c(-3,3),xlab="log2 fold change", ylab="-log2 p-value" ,pch=19,col="#838383")
points(sig$log2FoldChange, -log2(sig$pvalue)+1E-307, cex=0.6,pch=19,col="red")
mtext((HEADER),cex=1.2)

# top N gene heatmap
colfunc <- colorRampPalette(c("blue", "white", "red"))
heatmap.2(  as.matrix(dge[1:50,c(7:ncol(dge))]), col=colfunc(25),scale="row",
 trace="none",margins = c(6,20), cexRow=.6, cexCol=.8,  main="Top 50 genes")

spleen_ctrl_vs_acetate <- dge

SPLEEN ctrl vs hifibre

x <- sm[,grep("acet" , colnames(sm) ,invert=TRUE)]
x<-x[which(rowSums(x)/ncol(x)>=(10)),]
xs <- ss_sm[which( rownames(ss_sm) %in% colnames(x) ),1,drop=FALSE]
xs$trt <- factor(as.numeric(grepl("hifi",xs$group)))
dds <- DESeqDataSetFromMatrix(countData = x , colData = xs, design = ~ trt )
res <- DESeq(dds)

## estimating size factors

## estimating dispersions

## gene-wise dispersion estimates

## mean-dispersion relationship

## final dispersion estimates

## 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),])
head(dge)

##                            baseMean log2FoldChange     lfcSE      stat
## ENSMUSG00000039981_Zc3h12d 73.18839      0.6309846 0.1876804  3.362016
## ENSMUSG00000080942_Gm14556 14.74263      1.3626636 0.4253281  3.203794
## ENSMUSG00000025787_Tgm4    78.97001     -0.5572925 0.1832009 -3.041975
## ENSMUSG00000094296_Gm21798 13.15073      1.2869057 0.4387960  2.932811
## ENSMUSG00000084173_Gm5287   9.66525     -1.5581613 0.5316754 -2.930663
## ENSMUSG00000044499_Hs3st5  11.91063     -1.3481831 0.4621133 -2.917430
##                                  pvalue      padj S16_hifibre4 S1_ctrl1
## ENSMUSG00000039981_Zc3h12d 0.0007737558 0.9999214     7.474536 7.102333
## ENSMUSG00000080942_Gm14556 0.0013562957 0.9999214     6.648586 6.280875
## ENSMUSG00000025787_Tgm4    0.0023503166 0.9999214     7.133190 7.443201
## ENSMUSG00000094296_Gm21798 0.0033590813 0.9999214     6.549603 6.242652
## ENSMUSG00000084173_Gm5287  0.0033823953 0.9999214     6.200005 6.242652
## ENSMUSG00000044499_Hs3st5  0.0035292928 0.9999214     6.200005 6.382504
##                            S2_ctrl2 S3_ctrl3 S4_ctrl4 S6_hifibre1 S7_hifibre2
## ENSMUSG00000039981_Zc3h12d 7.241232 7.101976 7.113652    7.404460    7.555634
## ENSMUSG00000080942_Gm14556 6.100215 6.401909 6.195419    6.453389    6.688841
## ENSMUSG00000025787_Tgm4    7.518715 7.451092 7.611951    7.196180    7.256745
## ENSMUSG00000094296_Gm21798 6.271671 6.087016 6.309320    6.358335    6.570930
## ENSMUSG00000084173_Gm5287  6.590346 6.356228 6.513678    6.143523    6.118058
## ENSMUSG00000044499_Hs3st5  6.559407 6.576987 6.461234    6.059247    6.333354
##                            S8_hifibre3
## ENSMUSG00000039981_Zc3h12d    7.427906
## ENSMUSG00000080942_Gm14556    6.565581
## ENSMUSG00000025787_Tgm4       7.285646
## ENSMUSG00000094296_Gm21798    6.647078
## ENSMUSG00000084173_Gm5287     6.008600
## ENSMUSG00000044499_Hs3st5     6.197138

write.table(dge,file="spleen_ctrl_vs_hifibre_meth.tsv",quote=F,sep="\t")
#some plots
sig<-subset(dge,padj<0.05)
SIG=nrow(sig)
DN=nrow(subset(sig,log2FoldChange<0))
UP=nrow(subset(sig,log2FoldChange>0))
HEADER=paste("ctrl vs hifibre:", SIG , "DGEs,", UP ,"up,", DN, "down")
plot(log2(dge$baseMean),dge$log2FoldChange,cex=0.6,cex.axis=1.2,cex.lab=1.3,
 xlab="log2 base mean",
 ,ylab="log2 fold change" ,pch=19,col="#838383")
points(log2(sig$baseMean),sig$log2FoldChange,cex=0.6,pch=19,col="red")
mtext((HEADER),cex=1.2)

top<-head(sig,20)
plot(dge$log2FoldChange, -log2(dge$pvalue)+1E-307 ,cex=0.6, cex.lab=1.3,cex.axis=1.2,
 xlim=c(-3,3),xlab="log2 fold change", ylab="-log2 p-value" ,pch=19,col="#838383")
points(sig$log2FoldChange, -log2(sig$pvalue)+1E-307, cex=0.6,pch=19,col="red")
mtext((HEADER),cex=1.2)

# top N gene heatmap
colfunc <- colorRampPalette(c("blue", "white", "red"))
heatmap.2(  as.matrix(dge[1:50,c(7:ncol(dge))]), col=colfunc(25),scale="row",
 trace="none",margins = c(6,20), cexRow=.6, cexCol=.8,  main="Top 50 genes")

spleen_ctrl_vs_hifibre <- dge

Pathway level analysis with mitch

First fetch gene sets from Reactome.

library("mitch")
# gene sets
download.file("https://reactome.org/download/current/ReactomePathways.gmt.zip", 
    destfile="ReactomePathways.gmt.zip")
unzip("ReactomePathways.gmt.zip",overwrite = TRUE)
genesets <- gmt_import("ReactomePathways.gmt")

Now I will run enrichment analysis with mitch. Firstly each contrast individually but also all at once.

m2h <- read.table("mouse2human.txt.sort")
m2h[,1]=NULL

Heart: ctrl vs acetate

rownames(heart_ctrl_vs_acetate) <- sapply(strsplit(rownames(heart_ctrl_vs_acetate),"_"),"[[",1)
m <- mitch_import(x=heart_ctrl_vs_acetate, DEtype="deseq2",geneTable=m2h)

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

## Note: Mean no. genes in input = 32044

## Note: no. genes in output = 16173

## Note: estimated proportion of input genes in output = 0.505

capture.output(
    res <- mitch_calc(m,genesets=genesets,priority="effect")
    , file = "/dev/null", append = FALSE,
    type = c("output", "message"), split = FALSE)

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

head(res$enrichment_result,20)

##                                                                                           set
## 972                                                              Regulation of IFNA signaling
## 873                                             Presynaptic nicotinic acetylcholine receptors
## 1237                                                           TRAF6 mediated IRF7 activation
## 1196                                          Synthesis of PIPs at the late endosome membrane
## 1314 Transport of nucleosides and free purine and pyrimidine bases across the plasma membrane
## 684                                                                                 Mitophagy
## 214                                        Constitutive Signaling by NOTCH1 HD Domain Mutants
## 1132                                          Signaling by NOTCH1 HD Domain Mutants in Cancer
## 197                                         Classical antibody-mediated complement activation
## 259                           Defective C1GALT1C1 causes Tn polyagglutination syndrome (TNPS)
## 770                                                    Nucleotide-like (purinergic) receptors
## 132                                                 CLEC7A (Dectin-1) induces NFAT activation
## 804                                                             PINK1-PRKN Mediated Mitophagy
## 470                                                                           Glucuronidation
## 846                                                     Platelet Adhesion to exposed collagen
## 30                                                Acetylcholine binding and downstream events
## 863                                            Postsynaptic nicotinic acetylcholine receptors
## 781                                                                Organic anion transporters
## 1183                                                                    Surfactant metabolism
## 782                                                                  Organic cation transport
##      setSize       pANOVA     s.dist p.adjustANOVA
## 972       25 3.236096e-06  0.5378177   0.001097286
## 873       12 2.406663e-03 -0.5059609   0.180195110
## 1237      28 5.736823e-06  0.4952130   0.001329031
## 1196      10 1.040592e-02  0.4678958   0.401784109
## 1314      11 7.956673e-03 -0.4621278   0.345617978
## 684       20 3.749286e-04 -0.4594534   0.047377342
## 214       11 1.158505e-02  0.4396058   0.403299813
## 1132      11 1.158505e-02  0.4396058   0.403299813
## 197       10 1.627977e-02 -0.4387799   0.437582814
## 259       13 7.019007e-03 -0.4318545   0.345617978
## 770       12 1.000783e-02  0.4294082   0.397453942
## 132       10 1.952472e-02  0.4265050   0.463154532
## 804       14 7.087402e-03 -0.4156596   0.345617978
## 470       17 5.885066e-03  0.3858410   0.327209664
## 846       13 1.602716e-02  0.3857864   0.437582814
## 30        14 1.369875e-02 -0.3805266   0.418585416
## 863       14 1.369875e-02 -0.3805266   0.418585416
## 781       10 4.422094e-02 -0.3674565   0.660936703
## 1183      21 4.997417e-03 -0.3538881   0.302017786
## 782       10 5.328026e-02  0.3529667   0.694817138

unlink("heart_ctrl_vs_acetate_meth.html")
    capture.output(
        mitch_report(res,outfile=paste("heart_ctrl_vs_acetate_meth.html"))
        , file = "/dev/null", append = FALSE,
        type = c("output", "message"), split = FALSE)

## Dataset saved as " /tmp/RtmpLCUDmi/./heart_ctrl_vs_acetate_meth.RData ".

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

## Contour plot does not apply to unidimensional analysis.

## output file: /mnt/bfx6/bfx/francine/fran_methylome/redo2020/mitch.knit.md

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

Heart: ctrl vs high fibre

rownames(heart_ctrl_vs_hifibre) <- sapply(strsplit(rownames(heart_ctrl_vs_hifibre),"_"),"[[",1)
m <- mitch_import(x=heart_ctrl_vs_hifibre, DEtype="deseq2",geneTable=m2h)

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

## Note: Mean no. genes in input = 32894

## Note: no. genes in output = 16348

## Note: estimated proportion of input genes in output = 0.497

capture.output(
    res <- mitch_calc(m,genesets=genesets,priority="effect")
    , file = "/dev/null", append = FALSE,
    type = c("output", "message"), split = FALSE)

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

head(res$enrichment_result,20)

##                                                                         set
## 775                                             Olfactory Signaling Pathway
## 469                                                         Glucuronidation
## 820                                         Passive transport by Aquaporins
## 1238                                         TRAF6 mediated IRF7 activation
## 417                                           G alpha (s) signalling events
## 193                  Class C/3 (Metabotropic glutamate/pheromone receptors)
## 802                                          PI3K events in ERBB4 signaling
## 1017                                       Removal of the Flap Intermediate
## 1058                                         SHC1 events in ERBB4 signaling
## 973                                            Regulation of IFNA signaling
## 292           Diseases associated with glycosylation precursor biosynthesis
## 19                                   ATF6 (ATF6-alpha) activates chaperones
## 474                                     Glutathione synthesis and recycling
## 525  IRAK2 mediated activation of TAK1 complex upon TLR7/8 or 9 stimulation
## 445                                          GRB2 events in ERBB2 signaling
## 139       CREB1 phosphorylation through the activation of Adenylate Cyclase
## 1192                                                        Synthesis of PA
## 692                                           Mitotic Telophase/Cytokinesis
## 65                                          Adherens junctions interactions
## 444                                           GRB2 events in EGFR signaling
##      setSize       pANOVA     s.dist p.adjustANOVA
## 775      279 4.490041e-71  0.6185005  6.245647e-68
## 469       17 1.529558e-04  0.5303771  2.331817e-02
## 820       12 4.598322e-03 -0.4724688  2.974873e-01
## 1238      28 2.211553e-05  0.4631871  3.845338e-03
## 417      408 4.616993e-50  0.4292308  3.211118e-47
## 193       34 1.615340e-05  0.4273414  3.209911e-03
## 802       10 2.042804e-02  0.4234056  4.211617e-01
## 1017      11 1.603854e-02 -0.4193215  4.211617e-01
## 1058      14 7.622016e-03  0.4119016  3.744693e-01
## 973       25 4.861035e-04  0.4030583  5.201307e-02
## 292       16 5.473999e-03 -0.4011105  3.310579e-01
## 19        10 2.938017e-02  0.3978333  4.394389e-01
## 474       12 1.798684e-02 -0.3944560  4.211617e-01
## 525       12 1.945663e-02  0.3895844  4.211617e-01
## 445       16 7.191486e-03  0.3881337  3.744693e-01
## 139       11 2.691887e-02  0.3853105  4.211617e-01
## 1192      30 2.679244e-04 -0.3844671  3.388026e-02
## 692       10 3.578363e-02  0.3834129  4.689097e-01
## 65        24 1.200952e-03  0.3819274  1.113683e-01
## 444       13 1.715706e-02  0.3817852  4.211617e-01

unlink("heart_ctrl_vs_hifibre_meth.html")
    capture.output(
        mitch_report(res,outfile=paste("heart_ctrl_vs_hifibre_meth.html"))
        , file = "/dev/null", append = FALSE,
        type = c("output", "message"), split = FALSE)

## Dataset saved as " /tmp/RtmpLCUDmi/./heart_ctrl_vs_hifibre_meth.RData ".

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

## Contour plot does not apply to unidimensional analysis.

## output file: /mnt/bfx6/bfx/francine/fran_methylome/redo2020/mitch.knit.md

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

Spleen: ctrl vs acetate

rownames(spleen_ctrl_vs_acetate) <- sapply(strsplit(rownames(spleen_ctrl_vs_acetate),"_"),"[[",1)
m <- mitch_import(x=spleen_ctrl_vs_acetate, DEtype="deseq2",geneTable=m2h)

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

## Note: Mean no. genes in input = 24055

## Note: no. genes in output = 13888

## Note: estimated proportion of input genes in output = 0.577

capture.output(
    res <- mitch_calc(m,genesets=genesets,priority="effect")
    , file = "/dev/null", append = FALSE,
    type = c("output", "message"), split = FALSE)

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

head(res$enrichment_result,20)

##                                                                  set setSize
## 1184                                  TRAF6 mediated IRF7 activation      28
## 201                             Condensation of Prophase Chromosomes      10
## 933                                     Regulation of IFNA signaling      23
## 1303  alpha-linolenic (omega3) and linoleic (omega6) acid metabolism      11
## 1304                           alpha-linolenic acid (ALA) metabolism      11
## 494                                                 Heme degradation      15
## 311                       E2F mediated regulation of DNA replication      18
## 1295          WNT5A-dependent internalization of FZD2, FZD5 and ROR2      12
## 974                                 Regulation of signaling by NODAL      10
## 940                              Regulation of PTEN mRNA translation      11
## 523  Inhibition of replication initiation of damaged DNA by RB1/E2F1      12
## 468              Golgi Cisternae Pericentriolar Stack Reorganization      11
## 1190                           Tandem pore domain potassium channels      11
## 1034                                  Scavenging of heme from plasma      10
## 632                                         Metabolism of porphyrins      25
## 1268        Tristetraprolin (TTP, ZFP36) binds and destabilizes mRNA      11
## 371                  FOXO-mediated transcription of cell death genes      13
## 183                                    Citric acid cycle (TCA cycle)      15
## 216          Cyclin A/B1/B2 associated events during G2/M transition      13
## 739                           Nucleotide-like (purinergic) receptors      11
##            pANOVA     s.dist p.adjustANOVA
## 1184 1.256910e-06  0.5291486   0.001675461
## 201  6.772364e-03  0.4945381   0.637202860
## 933  1.212925e-04  0.4630051   0.080841420
## 1303 9.082411e-03  0.4543227   0.637202860
## 1304 9.082411e-03  0.4543227   0.637202860
## 494  2.323474e-03  0.4541964   0.362325767
## 311  1.093519e-03  0.4445926   0.291532198
## 1295 1.289766e-02  0.4146007   0.747503748
## 974  4.409771e-02 -0.3676899   0.850825232
## 940  3.480525e-02  0.3675604   0.850825232
## 523  3.156661e-02  0.3584667   0.850825232
## 468  4.170029e-02  0.3546549   0.850825232
## 1190 4.260676e-02  0.3530957   0.850825232
## 1034 5.391751e-02 -0.3520464   0.850825232
## 632  2.446311e-03  0.3501522   0.362325767
## 1268 5.144077e-02  0.3392075   0.850825232
## 371  3.694760e-02  0.3342426   0.850825232
## 183  2.718699e-02  0.3294505   0.850825232
## 216  4.044545e-02  0.3282883   0.850825232
## 739  6.134019e-02 -0.3258433   0.850825232

unlink("spleen_ctrl_vs_acetate_meth.html")
    capture.output(
        mitch_report(res,outfile=paste("spleen_ctrl_vs_acetate_meth.html"))
        , file = "/dev/null", append = FALSE,
        type = c("output", "message"), split = FALSE)

## Dataset saved as " /tmp/RtmpLCUDmi/./spleen_ctrl_vs_acetate_meth.RData ".

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

## Contour plot does not apply to unidimensional analysis.

## output file: /mnt/bfx6/bfx/francine/fran_methylome/redo2020/mitch.knit.md

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

Spleen: ctrl vs high fibre

rownames(spleen_ctrl_vs_hifibre) <- sapply(strsplit(rownames(spleen_ctrl_vs_hifibre),"_"),"[[",1)
m <- mitch_import(x=spleen_ctrl_vs_hifibre, DEtype="deseq2",geneTable=m2h)

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

## Note: Mean no. genes in input = 23743

## Note: no. genes in output = 13738

## Note: estimated proportion of input genes in output = 0.579

capture.output(
    res <- mitch_calc(m,genesets=genesets,priority="effect")
    , file = "/dev/null", append = FALSE,
    type = c("output", "message"), split = FALSE)

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

head(res$enrichment_result,20)

##                                                                                      set
## 1176                                                      TRAF6 mediated IRF7 activation
## 1298                                                                     eNOS activation
## 625                           Metabolism of nitric oxide: NOS3 activation and regulation
## 987                                                   Retrograde neurotrophin signalling
## 1162                                                TNFR1-induced proapoptotic signaling
## 553                                                            Interleukin-35 Signalling
## 978  Resolution of D-loop Structures through Synthesis-Dependent Strand Annealing (SDSA)
## 674            NF-kB activation through FADD/RIP-1 pathway mediated by caspase-8 and -10
## 1008                                                       SHC1 events in EGFR signaling
## 324                      ERCC6 (CSB) and EHMT2 (G9a) positively regulate rRNA expression
## 946                      Regulation of TP53 Activity through Association with Co-factors
## 926                                                  Regulation of FZD by ubiquitination
## 929                                                         Regulation of IFNA signaling
## 1169                         TP53 Regulates Transcription of Death Receptors and Ligands
## 1043                                       Signal regulatory protein family interactions
## 189                                    Classical antibody-mediated complement activation
## 319                                                ER Quality Control Compartment (ERQC)
## 267                     Deposition of new CENPA-containing nucleosomes at the centromere
## 732                                                                  Nucleosome assembly
## 942                                               Regulation of TLR by endogenous ligand
##      setSize       pANOVA     s.dist p.adjustANOVA
## 1176      27 1.457675e-06 -0.5355662   0.001244751
## 1298      10 5.060284e-03 -0.5119464   0.394404478
## 625       14 1.242570e-03 -0.4984386   0.252783007
## 987       12 4.204641e-03 -0.4772451   0.394404478
## 1162      12 7.399621e-03 -0.4465613   0.412456247
## 553       11 1.130956e-02  0.4411015   0.414555775
## 978       19 9.559751e-04 -0.4377717   0.252783007
## 674       10 2.369446e-02 -0.4131410   0.436043800
## 1008      10 2.782831e-02 -0.4017555   0.454063585
## 324       13 1.278511e-02 -0.3988511   0.414555775
## 946       11 2.241676e-02 -0.3976172   0.436043800
## 926       13 1.450996e-02  0.3915931   0.414555775
## 929       23 1.335457e-03 -0.3865143   0.252783007
## 1169      10 3.442218e-02 -0.3863054   0.498674880
## 1043      11 2.736943e-02 -0.3842063   0.454063585
## 189       10 3.906004e-02 -0.3768939   0.539109896
## 319       12 2.434263e-02 -0.3754432   0.441835318
## 267       16 9.649920e-03 -0.3737702   0.412456247
## 732       16 9.649920e-03 -0.3737702   0.412456247
## 942       13 2.245701e-02 -0.3656719   0.436043800

unlink("spleen_ctrl_vs_hifibre_meth.html")
    capture.output(
        mitch_report(res,outfile=paste("spleen_ctrl_vs_hifibre_meth.html"))
        , file = "/dev/null", append = FALSE,
        type = c("output", "message"), split = FALSE)

## Dataset saved as " /tmp/RtmpLCUDmi/./spleen_ctrl_vs_hifibre_meth.RData ".

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

## Contour plot does not apply to unidimensional analysis.

## output file: /mnt/bfx6/bfx/francine/fran_methylome/redo2020/mitch.knit.md

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

Multi enrichment

x <- list("heart_ctrl_vs_acetate"=heart_ctrl_vs_acetate,
    "heart_ctrl_vs_hifibre"=heart_ctrl_vs_hifibre,
    "spleen_ctrl_vs_acetate"=spleen_ctrl_vs_acetate,
    "spleen_ctrl_vs_hifibre"=spleen_ctrl_vs_hifibre)

m <- mitch_import(x=x, DEtype="deseq2",geneTable=m2h)

## Note: Mean no. genes in input = 28184

## Note: no. genes in output = 13629

## Note: estimated proportion of input genes in output = 0.484

capture.output(
    res <- mitch_calc(m,genesets=genesets,priority="effect")
    , file = "/dev/null", append = FALSE,
    type = c("output", "message"), split = FALSE)

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

head(res$enrichment_result,20)

##                                                                                           set
## 1170                                                           TRAF6 mediated IRF7 activation
## 924                                                              Regulation of IFNA signaling
## 446                                                                           Glucuronidation
## 451                                                       Glutathione synthesis and recycling
## 937                                                    Regulation of TLR by endogenous ligand
## 1003                                                            SHC1 events in EGFR signaling
## 1163                              TP53 Regulates Transcription of Death Receptors and Ligands
## 732                                                    Nucleotide-like (purinergic) receptors
## 478                                                        HIV elongation arrest and recovery
## 778                                                    Pausing and recovery of HIV elongation
## 779                                       Pausing and recovery of Tat-mediated HIV elongation
## 1177                                          Tat-mediated HIV elongation arrest and recovery
## 736                                                               Olfactory Signaling Pathway
## 1244 Transport of nucleosides and free purine and pyrimidine bases across the plasma membrane
## 803                                                     Platelet Adhesion to exposed collagen
## 366                                           FOXO-mediated transcription of cell death genes
## 322                           ERCC6 (CSB) and EHMT2 (G9a) positively regulate rRNA expression
## 128                                                 CLEC7A (Dectin-1) induces NFAT activation
## 931                                                       Regulation of PTEN mRNA translation
## 189                                         Classical antibody-mediated complement activation
##      setSize      pMANOVA s.heart_ctrl_vs_acetate s.heart_ctrl_vs_hifibre
## 1170      27 1.290453e-20              0.52011414              0.55101374
## 924       23 1.502388e-13              0.58758923              0.46915683
## 446       16 2.902601e-04              0.37230956              0.63291523
## 451       11 3.580270e-02             -0.34501128             -0.41441141
## 937       13 1.017227e-02              0.34302404              0.34295625
## 1003      10 1.796068e-02              0.07463837              0.44562009
## 1163      10 3.186887e-03             -0.18786989              0.28924297
## 732       11 7.404562e-03              0.44564013              0.14811947
## 478       15 7.048445e-03             -0.14077665             -0.44324960
## 778       15 7.048445e-03             -0.14077665             -0.44324960
## 779       15 7.048445e-03             -0.14077665             -0.44324960
## 1177      15 7.048445e-03             -0.14077665             -0.44324960
## 736      142 2.233345e-27              0.29344324              0.51502438
## 1244      11 4.634713e-02             -0.45530648             -0.19589047
## 803       11 5.960624e-02              0.44212873              0.19897462
## 366       13 3.687957e-02              0.32573669              0.15731492
## 322       13 3.076482e-02             -0.11744102             -0.34465109
## 128       10 1.257608e-01              0.43813790              0.35788237
## 931       11 1.148624e-01              0.21240604              0.27196625
## 189       10 3.123448e-02             -0.43230781             -0.09104927
##      s.spleen_ctrl_vs_acetate s.spleen_ctrl_vs_hifibre p.heart_ctrl_vs_acetate
## 1170               0.52601197              -0.53439854            2.899172e-06
## 924                0.47192415              -0.38541500            1.069256e-06
## 446                0.08505656               0.12389811            9.937410e-03
## 451               -0.24784043              -0.31378256            4.758007e-02
## 937               -0.26223674              -0.36383666            3.226224e-02
## 1003              -0.17792055              -0.40021294            6.828149e-01
## 1163               0.32310742              -0.38525589            3.036894e-01
## 732               -0.32015114               0.18647779            1.049595e-02
## 478               -0.16067773              -0.33687870            3.452885e-01
## 778               -0.16067773              -0.33687870            3.452885e-01
## 779               -0.16067773              -0.33687870            3.452885e-01
## 1177              -0.16067773              -0.33687870            3.452885e-01
## 736               -0.05227935               0.02873033            1.655594e-09
## 1244               0.27184609               0.18775952            8.934012e-03
## 803                0.22298028               0.23893510            1.112085e-02
## 366                0.34718205               0.30076607            4.203007e-02
## 322               -0.22499548              -0.39715719            4.635529e-01
## 128                0.12883472               0.04948968            1.644293e-02
## 931                0.37793562               0.27565121            2.226132e-01
## 189               -0.01866510              -0.37538733            1.793270e-02
##      p.heart_ctrl_vs_hifibre p.spleen_ctrl_vs_acetate p.spleen_ctrl_vs_hifibre
## 1170            7.196397e-07             2.234998e-06             1.536599e-06
## 924             9.838282e-05             8.946059e-05             1.378563e-03
## 446             1.167696e-05             5.559331e-01             3.909918e-01
## 451             1.732766e-02             1.547122e-01             7.158348e-02
## 937             3.229638e-02             1.016687e-01             2.314202e-02
## 1003            1.469097e-02             3.300163e-01             2.843412e-02
## 1163            1.132858e-01             7.689017e-02             3.491543e-02
## 732             3.950631e-01             6.601360e-02             2.842930e-01
## 478             2.958850e-03             2.813955e-01             2.390757e-02
## 778             2.958850e-03             2.813955e-01             2.390757e-02
## 779             2.958850e-03             2.813955e-01             2.390757e-02
## 1177            2.958850e-03             2.813955e-01             2.390757e-02
## 736             3.134131e-26             2.831267e-01             5.552981e-01
## 1244            2.606849e-01             1.185395e-01             2.809958e-01
## 803             2.532532e-01             2.004297e-01             1.700805e-01
## 366             3.261540e-01             3.022686e-02             6.047196e-02
## 322             3.145221e-02             1.602152e-01             1.317089e-02
## 128             5.006110e-02             4.805989e-01             7.864355e-01
## 931             1.183768e-01             2.999268e-02             1.134707e-01
## 189             6.181521e-01             9.186090e-01             3.985029e-02
##         s.dist         SD p.adjustMANOVA
## 1170 1.0660231 0.53355739   8.510540e-18
## 924  0.9677946 0.45087628   4.954123e-11
## 446  0.7495208 0.25378699   2.473412e-02
## 451  0.6713077 0.06919430   4.216407e-01
## 937  0.6606283 0.38102165   2.625760e-01
## 1003 0.6292649 0.36292189   3.483844e-01
## 1163 0.6097353 0.35184867   1.616733e-01
## 732  0.5981682 0.31879219   2.123178e-01
## 478  0.5963161 0.14507192   2.065978e-01
## 778  0.5963161 0.14507192   2.065978e-01
## 779  0.5963161 0.14507192   2.065978e-01
## 1177 0.5963161 0.14507192   2.065978e-01
## 736  0.5957496 0.25877487   2.945782e-24
## 1244 0.5956769 0.33943786   4.375749e-01
## 803  0.5847041 0.11212541   4.975989e-01
## 366  0.5846777 0.08574746   4.304792e-01
## 322  0.5838952 0.12521566   4.132627e-01
## 128  0.5823163 0.18416961   6.236036e-01
## 931  0.5812924 0.06870990   6.218268e-01
## 189  0.5800380 0.20496666   4.132627e-01

unlink("multi_enrichment_meth.html")
    capture.output(
        mitch_report(res,outfile=paste("multi_enrichment_meth.html"))
        , file = "/dev/null", append = FALSE,
        type = c("output", "message"), split = FALSE)

## Dataset saved as " /tmp/RtmpLCUDmi/./multi_enrichment_meth.RData ".

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

## 
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output file: /mnt/bfx6/bfx/francine/fran_methylome/redo2020/mitch.knit.md
## 
## 
## Output created: /tmp/RtmpLCUDmi/mitch_report.html

End of report

Session information

sessionInfo()

## R version 4.0.2 (2020-06-22)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.4 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1
## 
## 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    parallel  stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] pkgload_1.1.0               GGally_2.0.0               
##  [3] beeswarm_0.2.3              gtools_3.8.2               
##  [5] echarts4r_0.3.2             mitch_1.0.6                
##  [7] fgsea_1.14.0                gplots_3.0.3               
##  [9] DESeq2_1.28.1               SummarizedExperiment_1.18.1
## [11] DelayedArray_0.14.0         matrixStats_0.56.0         
## [13] Biobase_2.48.0              GenomicRanges_1.40.0       
## [15] GenomeInfoDb_1.24.2         IRanges_2.22.2             
## [17] S4Vectors_0.26.1            BiocGenerics_0.34.0        
## [19] reshape2_1.4.4              forcats_0.5.0              
## [21] stringr_1.4.0               dplyr_1.0.0                
## [23] purrr_0.3.4                 readr_1.3.1                
## [25] tidyr_1.1.0                 tibble_3.0.1               
## [27] ggplot2_3.3.2               tidyverse_1.3.0            
## [29] locfit_1.5-9.4              statmod_1.4.34             
## [31] plyr_1.8.6                 
## 
## loaded via a namespace (and not attached):
##  [1] colorspace_1.4-1       ellipsis_0.3.1         rprojroot_1.3-2       
##  [4] XVector_0.28.0         fs_1.4.2               rstudioapi_0.11       
##  [7] farver_2.0.3           bit64_0.9-7            AnnotationDbi_1.50.1  
## [10] fansi_0.4.1            lubridate_1.7.9        xml2_1.3.2            
## [13] splines_4.0.2          geneplotter_1.66.0     knitr_1.29            
## [16] jsonlite_1.7.0         broom_0.5.6            annotate_1.66.0       
## [19] dbplyr_1.4.4           shiny_1.5.0            compiler_4.0.2        
## [22] httr_1.4.1             backports_1.1.8        assertthat_0.2.1      
## [25] Matrix_1.2-18          fastmap_1.0.1          cli_2.0.2             
## [28] later_1.1.0.1          prettyunits_1.1.1      htmltools_0.5.0       
## [31] tools_4.0.2            gtable_0.3.0           glue_1.4.1            
## [34] GenomeInfoDbData_1.2.3 fastmatch_1.1-0        Rcpp_1.0.4.6          
## [37] cellranger_1.1.0       vctrs_0.3.1            gdata_2.18.0          
## [40] nlme_3.1-148           xfun_0.15              testthat_2.3.2        
## [43] rvest_0.3.5            mime_0.9               lifecycle_0.2.0       
## [46] XML_3.99-0.3           zlibbioc_1.34.0        MASS_7.3-51.6         
## [49] scales_1.1.1           hms_0.5.3              promises_1.1.1        
## [52] RColorBrewer_1.1-2     yaml_2.2.1             memoise_1.1.0         
## [55] gridExtra_2.3          reshape_0.8.8          stringi_1.4.6         
## [58] RSQLite_2.2.0          highr_0.8              genefilter_1.70.0     
## [61] desc_1.2.0             caTools_1.18.0         BiocParallel_1.22.0   
## [64] rlang_0.4.6            pkgconfig_2.0.3        bitops_1.0-6          
## [67] evaluate_0.14          lattice_0.20-41        labeling_0.3          
## [70] htmlwidgets_1.5.1      bit_1.1-15.2           tidyselect_1.1.0      
## [73] magrittr_1.5           R6_2.4.1               generics_0.0.2        
## [76] DBI_1.1.0              pillar_1.4.4           haven_2.3.1           
## [79] withr_2.2.0            survival_3.2-3         RCurl_1.98-1.2        
## [82] modelr_0.1.8           crayon_1.3.4           KernSmooth_2.23-17    
## [85] rmarkdown_2.3          progress_1.2.2         grid_4.0.2            
## [88] readxl_1.3.1           data.table_1.12.8      blob_1.2.1            
## [91] reprex_0.3.0           digest_0.6.25          pbmcapply_1.5.0       
## [94] xtable_1.8-4           httpuv_1.5.4           munsell_0.5.0