RNA-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 RNA-seq that was previosly generated. I will perform an analysis that begins with DESeq2 for differential expression followed by pathway enrichment analysis with mitch.

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

Import profiling data

Kallisto was used to map the reads to the transcriptome. Here I'm importing the transcript level counts and aggregating to gene level.

tmp<-read.table("3col.tsv",header=F)
x<-as.matrix(acast(tmp, V2~V1, value.var="V3"))
g<-read.table("tx2gn.tsv",header=F,row.names=1)
x<-merge(g,x,by=0)
rownames(x)=x$Row.names
x$Row.names=NULL
# aggregate to gene names
x<-aggregate(. ~ V2,x,sum)
rownames(x)=x$V2
x$V2=NULL
x$t=NULL
x <- round(x)

# separate heart and spleen data
hr <- x[,grep("H_",colnames(x) )]
sr <- x[,grep("S_",colnames(x) )]
b <- data.frame(sr,hr)

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(hr))), 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(hr))), labels=colnames(hr),cex=1.3)
mtext("heart samples")

plot(cmdscale(dist(t(sr))), 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(sr))), labels=colnames(sr),cex=1.3)
mtext("spleen samples")

Curate the samplesheet

ctrl<-as.factor(as.numeric(grepl("ctrl",colnames(x))))
hifib<-as.factor(as.numeric(grepl("hifibre",colnames(x))))
acetate<-as.factor(as.numeric(grepl("acetate",colnames(x))))
heart<-as.factor(as.numeric(grepl("H",colnames(x))))
spleen<-as.factor(as.numeric(grepl("S",colnames(x))))
ss<-cbind(heart,spleen,ctrl,hifib,acetate)-1
row.names(ss)=colnames(x)

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

ss_hr <- ss[grep("H_",rownames(ss)),]
x <- hr[,grep("hifi" , colnames(hr) ,invert=TRUE)]
x<-x[which(rowSums(x)/ncol(x)>=(10)),]
xs <- data.frame(ss_hr[which( rownames(ss_hr) %in% colnames(x) ),1,drop=FALSE])
xs$trt <- factor(as.numeric(grepl("acet",rownames(xs))))
dds <- DESeqDataSetFromMatrix(countData = x , colData = xs, design = ~ trt )

## converting counts to integer mode

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
## ENSMUSG00000059824_Dbp   1991.2105       3.054743 0.1331054  22.94981
## ENSMUSG00000055116_Arntl  565.2687      -2.629012 0.1548155 -16.98158
## ENSMUSG00000026077_Npas2  213.3276      -1.937798 0.1346362 -14.39285
## ENSMUSG00000028957_Per3   935.6254       2.156847 0.1626108  13.26386
## ENSMUSG00000022389_Tef   4566.2299       1.375636 0.1088460  12.63836
## ENSMUSG00000056749_Nfil3  437.1786      -1.164011 0.1000657 -11.63247
##                                 pvalue          padj H_acetate1_f H_acetate2_f
## ENSMUSG00000059824_Dbp   1.479878e-116 2.098171e-112    11.783665    11.733681
## ENSMUSG00000055116_Arntl  1.124185e-64  7.969350e-61     8.794148     8.428261
## ENSMUSG00000026077_Npas2  5.738187e-47  2.711867e-43     8.147724     8.236002
## ENSMUSG00000028957_Per3   3.750633e-40  1.329412e-36    10.455784    10.971571
## ENSMUSG00000022389_Tef    1.297243e-36  3.678461e-33    12.610977    12.844831
## ENSMUSG00000056749_Nfil3  2.818335e-31  6.659726e-28     9.025515     8.987172
##                          H_acetate3_f H_acetate4_f H_ctrl1_f H_ctrl2_f
## ENSMUSG00000059824_Dbp      12.224099    11.793457  9.395012  9.330056
## ENSMUSG00000055116_Arntl     8.398259     8.551378 10.172594 10.300776
## ENSMUSG00000026077_Npas2     8.211326     8.229558  9.167179  9.111096
## ENSMUSG00000028957_Per3     11.048890    10.654782  9.116020  8.981180
## ENSMUSG00000022389_Tef      12.861079    12.636268 11.360739 11.519617
## ENSMUSG00000056749_Nfil3     8.935310     8.931409  9.720595  9.710099
##                          H_ctrl3_f H_ctrl4_f
## ENSMUSG00000059824_Dbp    9.265394  9.534909
## ENSMUSG00000055116_Arntl 10.305100 10.259650
## ENSMUSG00000026077_Npas2  9.234786  9.141036
## ENSMUSG00000028957_Per3   9.248265  9.330330
## ENSMUSG00000022389_Tef   11.249615 11.648337
## ENSMUSG00000056749_Nfil3  9.815852  9.676046

write.table(dge,file="heart_ctrl_vs_acetate_rna.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 <- hr[,grep("acet" , colnames(hr) ,invert=TRUE)]
x<-x[which(rowSums(x)/ncol(x)>=(10)),]
xs <- data.frame(ss_hr[which( rownames(ss_hr) %in% colnames(x) ),1,drop=FALSE])
xs$trt <- factor(as.numeric(grepl("hifi",rownames(xs))))
dds <- DESeqDataSetFromMatrix(countData = x , colData = xs, design = ~ trt )

## converting counts to integer mode

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
## ENSMUSG00000059824_Dbp   1669.0952      2.8742198 0.11798966  24.35993
## ENSMUSG00000055116_Arntl  559.4303     -2.1478357 0.09985574 -21.50939
## ENSMUSG00000028957_Per3   711.3601      1.7801498 0.13205677  13.48019
## ENSMUSG00000056749_Nfil3  389.8895     -1.4036126 0.10884178 -12.89590
## ENSMUSG00000026077_Npas2  205.7744     -1.7366034 0.13979843 -12.42220
## ENSMUSG00000018427_Ypel2 1124.2607     -0.9715747 0.08753790 -11.09890
##                                 pvalue          padj H_ctrl1_f H_ctrl2_f
## ENSMUSG00000059824_Dbp   4.550039e-131 6.408275e-127  9.458389  9.398489
## ENSMUSG00000055116_Arntl 1.271767e-102  8.955780e-99 10.180129 10.300264
## ENSMUSG00000028957_Per3   2.045920e-41  9.604913e-38  9.204395  9.082143
## ENSMUSG00000056749_Nfil3  4.746963e-38  1.671406e-34  9.758178  9.747871
## ENSMUSG00000026077_Npas2  1.980510e-35  5.578701e-32  9.250768  9.199453
## ENSMUSG00000018427_Ypel2  1.269930e-28  2.980949e-25 10.861469 10.847900
##                          H_ctrl3_f H_ctrl4_f H_hifibre1_f H_hifibre2_f
## ENSMUSG00000059824_Dbp    9.337700  9.585443    11.444705    11.862564
## ENSMUSG00000055116_Arntl 10.301952 10.260558     8.848009     8.911264
## ENSMUSG00000028957_Per3   9.322120  9.398026    10.482086    10.382798
## ENSMUSG00000056749_Nfil3  9.843796  9.715575     8.959444     8.886912
## ENSMUSG00000026077_Npas2  9.309866  9.225905     8.487783     8.381738
## ENSMUSG00000018427_Ypel2 10.892150 10.681646     9.992109    10.071876
##                          H_hifibre3_f H_hifibre4_f
## ENSMUSG00000059824_Dbp      11.729620    11.624837
## ENSMUSG00000055116_Arntl     8.988307     8.942390
## ENSMUSG00000028957_Per3     10.334709    10.665284
## ENSMUSG00000056749_Nfil3     8.909432     9.034288
## ENSMUSG00000026077_Npas2     8.431329     8.531002
## ENSMUSG00000018427_Ypel2    10.040897    10.148656

write.table(dge,file="heart_ctrl_vs_hifibre_rna.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

ss_sr <- ss[grep("S_",rownames(ss)),]
x <- sr[,grep("hifi" , colnames(sr) ,invert=TRUE)]
x<-x[which(rowSums(x)/ncol(x)>=(10)),]
xs <- data.frame(ss_sr[which( rownames(ss_sr) %in% colnames(x) ),1,drop=FALSE])
xs$trt <- factor(as.numeric(grepl("acet",rownames(xs))))

dds <- DESeqDataSetFromMatrix(countData = x , colData = xs, design = ~ trt )

## converting counts to integer mode

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
## ENSMUSG00000028773_Fabp3  360.17321      -4.665301 0.5798727 -8.045388
## ENSMUSG00000059741_Myl3   438.56578      -4.428624 0.5517539 -8.026448
## ENSMUSG00000027022_Xirp2   70.50732      -5.519004 0.7293318 -7.567207
## ENSMUSG00000021622_Ckmt2  256.91914      -4.425830 0.6079797 -7.279569
## ENSMUSG00000002100_Mybpc3 411.94615      -3.772669 0.5196614 -7.259861
## ENSMUSG00000030399_Ckm    214.43251      -4.318029 0.6028334 -7.162890
##                                 pvalue         padj S_acetate1_f S_acetate2_f
## ENSMUSG00000028773_Fabp3  8.597276e-16 7.810575e-12     6.893963     6.851574
## ENSMUSG00000059741_Myl3   1.003350e-15 7.810575e-12     7.204358     7.017253
## ENSMUSG00000027022_Xirp2  3.813349e-14 1.979001e-10     6.232617     6.320624
## ENSMUSG00000021622_Ckmt2  3.348895e-13 1.206564e-09     7.153729     6.637052
## ENSMUSG00000002100_Mybpc3 3.874891e-13 1.206564e-09     7.356553     7.223692
## ENSMUSG00000030399_Ckm    7.899358e-13 2.049752e-09     6.982604     6.659803
##                           S_acetate3_f S_acetate4_f S_ctrl1_f S_ctrl2_f
## ENSMUSG00000028773_Fabp3      6.970493     7.049107 10.929836  8.725937
## ENSMUSG00000059741_Myl3       7.131995     7.094742 11.157591  9.045833
## ENSMUSG00000027022_Xirp2      6.345065     6.418924  8.799987  7.463914
## ENSMUSG00000021622_Ckmt2      6.817074     6.773389 10.397079  8.466959
## ENSMUSG00000002100_Mybpc3     7.398444     7.294586 10.990608  8.921187
## ENSMUSG00000030399_Ckm        6.817074     6.805900 10.218850  8.268514
##                           S_ctrl3_f S_ctrl4_f
## ENSMUSG00000028773_Fabp3   8.517438  9.022884
## ENSMUSG00000059741_Myl3    8.720093  9.257726
## ENSMUSG00000027022_Xirp2   7.304322  7.728428
## ENSMUSG00000021622_Ckmt2   8.143922  8.845060
## ENSMUSG00000002100_Mybpc3  8.791634  9.233237
## ENSMUSG00000030399_Ckm     8.047163  8.425414

write.table(dge,file="spleen_ctrl_vs_acetate_rna.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 <- sr[,grep("acet" , colnames(sr) ,invert=TRUE)]
x<-x[which(rowSums(x)/ncol(x)>=(10)),]
xs <- data.frame(ss_sr[which( rownames(ss_sr) %in% colnames(x) ),1,drop=FALSE])
xs$trt <- factor(as.numeric(grepl("hifi",rownames(xs))))
dds <- DESeqDataSetFromMatrix(countData = x , colData = xs, design = ~ trt )

## converting counts to integer mode

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
## ENSMUSG00000022584_Ly6c2  1970.10361      0.5370495 0.07356343  7.300496
## ENSMUSG00000038239_Hrc     132.08028     -4.3351541 0.62548407 -6.930879
## ENSMUSG00000059824_Dbp     798.20275      0.6989401 0.10485080  6.666045
## ENSMUSG00000016349_Eef1a2  129.58078     -4.0583969 0.63534066 -6.387749
## ENSMUSG00000027022_Xirp2    81.87583     -3.9334461 0.62861292 -6.257342
## ENSMUSG00000028116_Myoz2    95.11368     -3.4796347 0.55706622 -6.246357
##                                 pvalue         padj S_ctrl1_f S_ctrl2_f
## ENSMUSG00000022584_Ly6c2  2.867084e-13 4.496734e-09 10.789633 10.972202
## ENSMUSG00000038239_Hrc    4.182351e-12 3.279800e-08  9.892288  8.475712
## ENSMUSG00000059824_Dbp    2.627888e-11 1.373860e-07  9.844596  9.862844
## ENSMUSG00000016349_Eef1a2 1.683450e-10 6.600809e-07  9.855751  8.376223
## ENSMUSG00000027022_Xirp2  3.915938e-10 1.098233e-06  9.307792  8.274237
## ENSMUSG00000028116_Myoz2  4.201350e-10 1.098233e-06  9.422798  8.301980
##                           S_ctrl3_f S_ctrl4_f S_hifibre1_f S_hifibre2_f
## ENSMUSG00000022584_Ly6c2  10.922067 10.885454    11.435098    11.354769
## ENSMUSG00000038239_Hrc     8.263886  8.633795     7.697761     7.724031
## ENSMUSG00000059824_Dbp     9.676735  9.796189    10.283411    10.380107
## ENSMUSG00000016349_Eef1a2  8.276574  8.681010     7.787681     7.618244
## ENSMUSG00000027022_Xirp2   8.162112  8.467599     7.683274     7.561172
## ENSMUSG00000028116_Myoz2   8.257472  8.537348     7.683274     7.667116
##                           S_hifibre3_f S_hifibre4_f
## ENSMUSG00000022584_Ly6c2     11.313278    11.342283
## ENSMUSG00000038239_Hrc        7.645062     7.628433
## ENSMUSG00000059824_Dbp       10.410718    10.193476
## ENSMUSG00000016349_Eef1a2     7.645062     7.763855
## ENSMUSG00000027022_Xirp2      7.499124     7.737247
## ENSMUSG00000028116_Myoz2      7.765908     7.776619

write.table(dge,file="spleen_ctrl_vs_hifibre_rna.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.

# 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 = 14193

## Note: no. genes in output = 13161

## Note: estimated proportion of input genes in output = 0.927

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
## 339                                    Establishment of Sister Chromatid Cohesion
## 1278                                                             Unwinding of DNA
## 663                                                 Mitotic Telophase/Cytokinesis
## 1289                                                       Viral mRNA Translation
## 790                                                      Peptide chain elongation
## 986                           Response of EIF2AK4 (GCN2) to amino acid deficiency
## 53                                      Activation of the pre-replicative complex
## 1037                                                     Selenocysteine synthesis
## 344                                            Eukaryotic Translation Termination
## 440                                                               Glucuronidation
## 342                                             Eukaryotic Translation Elongation
## 244                                                         DNA strand elongation
## 203                                          Condensation of Prophase Chromosomes
## 202                                      Condensation of Prometaphase Chromosomes
## 294                                                    Dissolution of Fibrin Clot
## 814                                              Polo-like kinase mediated events
## 220                       Cyclin A/B1/B2 associated events during G2/M transition
## 725  Nonsense Mediated Decay (NMD) independent of the Exon Junction Complex (EJC)
## 476                                                HSF1-dependent transactivation
## 382                                      Formation of a pool of free 40S subunits
##      setSize       pANOVA     s.dist p.adjustANOVA
## 339       10 8.937520e-05  0.7154133  1.477449e-03
## 1278      12 2.560732e-05  0.7017137  5.117643e-04
## 663       12 5.574661e-05  0.6718508  1.036732e-03
## 1289      55 4.865484e-15  0.6102160  2.171628e-12
## 790       55 1.217561e-14  0.6011847  2.717189e-12
## 986       65 2.692012e-16  0.5870001  1.802302e-13
## 53        31 1.941682e-08  0.5828612  8.666372e-07
## 1037      58 6.678544e-14  0.5688247  1.117821e-11
## 344       58 1.858344e-13  0.5585665  2.764803e-11
## 440       11 1.345161e-03  0.5582993  1.488571e-02
## 342       59 6.457273e-13  0.5412034  7.205241e-11
## 244       32 1.238822e-07  0.5400925  4.483196e-06
## 203       13 8.826691e-04  0.5327046  1.010166e-02
## 202       11 2.381756e-03  0.5290218  2.344979e-02
## 294       10 4.576141e-03  0.5178466  3.902836e-02
## 814       15 5.452779e-04  0.5156347  6.823617e-03
## 220       22 3.200718e-05  0.5122086  6.211249e-04
## 725       60 9.091481e-12  0.5091087  5.533406e-10
## 476       32 6.857435e-07 -0.5072026  2.135373e-05
## 382       65 2.799398e-12  0.5012041  2.342747e-10

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

## Dataset saved as " /tmp/RtmpLCUDmi/./heart_ctrl_vs_acetate_rna.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 = 14098

## Note: no. genes in output = 13088

## Note: estimated proportion of input genes in output = 0.928

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
## 1274                                                         Unwinding of DNA
## 189                         Classical antibody-mediated complement activation
## 797                                Phosphorylation of CD3 and TCR zeta chains
## 531                                           Interferon alpha/beta signaling
## 514                                          Initial triggering of complement
## 572                                                     LGI-ADAM interactions
## 216                                          Creation of C4 and C2 activators
## 786                                                  Peptide chain elongation
## 703  Negative regulation of TCF-dependent signaling by WNT ligand antagonists
## 1033                                                 Selenocysteine synthesis
## 982                       Response of EIF2AK4 (GCN2) to amino acid deficiency
## 1285                                                   Viral mRNA Translation
## 202                                  Condensation of Prometaphase Chromosomes
## 244                                                     DNA strand elongation
## 439                                                           Glucuronidation
## 762                                                       PI-3K cascade:FGFR2
## 341                                         Eukaryotic Translation Elongation
## 764                                                       PI-3K cascade:FGFR4
## 835                                Processive synthesis on the lagging strand
## 448                                                 Glycogen storage diseases
##      setSize       pANOVA     s.dist p.adjustANOVA
## 1274      12 2.242771e-05  0.7066891  5.649244e-04
## 189       10 1.725606e-04  0.6859153  2.991797e-03
## 797       11 2.845729e-04  0.6318867  4.653556e-03
## 531       45 5.906310e-13  0.6204162  1.971231e-10
## 514       19 3.476612e-06  0.6148942  1.105066e-04
## 572       11 5.434043e-04 -0.6022023  7.342224e-03
## 216       12 4.394709e-04  0.5860482  6.273436e-03
## 786       55 1.571567e-13  0.5752740  9.734157e-11
## 703       11 1.185784e-03 -0.5645860  1.375145e-02
## 1033      58 2.187451e-13  0.5569190  9.734157e-11
## 982       65 1.038050e-14  0.5546790  1.385797e-11
## 1285      55 2.131771e-12  0.5476294  5.691828e-10
## 202       11 1.851714e-03  0.5420899  1.790058e-02
## 244       32 1.252125e-07  0.5398955  4.865614e-06
## 439       11 2.685844e-03  0.5226873  2.241001e-02
## 762       14 7.589116e-04 -0.5198103  9.294926e-03
## 341       59 5.351060e-12  0.5190806  1.020523e-09
## 764       11 2.921686e-03 -0.5182103  2.392914e-02
## 835       15 6.146091e-04  0.5108085  8.205031e-03
## 448       11 3.447403e-03 -0.5093120  2.727013e-02

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

## Dataset saved as " /tmp/RtmpLCUDmi/./heart_ctrl_vs_hifibre_rna.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 = 15612

## Note: no. genes in output = 13952

## Note: estimated proportion of input genes in output = 0.894

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
## 187                Classical antibody-mediated complement activation      10
## 1293                                                Unwinding of DNA      12
## 241                                            DNA strand elongation      32
## 589                                         Leading Strand Synthesis      14
## 826                                             Polymerase switching      14
## 585                                         Lagging Strand Synthesis      20
## 851                       Processive synthesis on the lagging strand      15
## 984                                 Removal of the Flap Intermediate      14
## 423                                  GP1b-IX-V activation signalling      10
## 653          Mismatch repair (MMR) directed by MSH2:MSH6 (MutSalpha)      14
## 522  Inhibition of replication initiation of damaged DNA by RB1/E2F1      12
## 770                   PCNA-Dependent Long Patch Base Excision Repair      21
## 675                                            Mucopolysaccharidoses      11
## 414                                      G1/S-Specific Transcription      27
## 652           Mismatch repair (MMR) directed by MSH2:MSH3 (MutSbeta)      14
## 1322                                                    cGMP effects      13
## 51                         Activation of the pre-replicative complex      32
## 88                           Assembly of the pre-replicative complex      62
## 277                                                        Digestion      13
## 651                                                  Mismatch Repair      15
##            pANOVA     s.dist p.adjustANOVA
## 187  8.954711e-07  0.8970592  7.940842e-06
## 1293 2.189175e-07  0.8637853  2.115681e-06
## 241  8.377536e-15  0.7923042  3.988530e-13
## 589  3.276838e-07  0.7880922  3.036686e-06
## 826  3.276838e-07  0.7880922  3.036686e-06
## 585  6.914230e-09  0.7480261  8.278719e-08
## 851  1.441968e-06  0.7185430  1.219364e-05
## 984  3.417990e-06  0.7167251  2.673145e-05
## 423  8.818648e-05 -0.7159948  5.302947e-04
## 653  6.501860e-06  0.6959699  4.860230e-05
## 522  6.435849e-05  0.6661944  4.012767e-04
## 770  1.266779e-07  0.6658976  1.251060e-06
## 675  1.854942e-04  0.6508533  1.032813e-03
## 414  8.566268e-09  0.6399548  1.007840e-07
## 652  4.474616e-05  0.6299736  2.882931e-04
## 1322 1.029655e-04 -0.6220345  6.137106e-04
## 51   1.532775e-09  0.6169046  2.033180e-08
## 88   2.224440e-16  0.6026046  1.881042e-14
## 277  1.758486e-04 -0.6008874  9.872332e-04
## 651  6.241937e-05  0.5970056  3.928065e-04

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

## Dataset saved as " /tmp/RtmpLCUDmi/./spleen_ctrl_vs_acetate_rna.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 = 15714

## Note: no. genes in output = 14009

## Note: estimated proportion of input genes in output = 0.891

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
## 187            Classical antibody-mediated complement activation      11
## 524                             Initial triggering of complement      22
## 214                             Creation of C4 and C2 activators      15
## 675                                        Mucopolysaccharidoses      11
## 277                                                    Digestion      14
## 1237                 Trafficking and processing of endosomal TLR      11
## 358                                              FCGR activation      17
## 472                                    HDMs demethylate histones      21
## 278                                     Digestion and absorption      15
## 1030 SRP-dependent cotranslational protein targeting to membrane      77
## 174                                     Cholesterol biosynthesis      23
## 1305                                      Viral mRNA Translation      56
## 801                                     Peptide chain elongation      56
## 402                          G beta:gamma signalling through BTK      14
## 1050                                    Selenocysteine synthesis      59
## 855        Prostacyclin signalling through prostacyclin receptor      15
## 421            GABA synthesis, release, reuptake and degradation      15
## 349                           Eukaryotic Translation Termination      59
## 347                            Eukaryotic Translation Elongation      60
## 1345                        rRNA processing in the mitochondrion      17
##            pANOVA     s.dist p.adjustANOVA
## 187  6.004912e-07  0.8688644  2.084782e-05
## 524  7.107880e-08  0.6635057  3.007522e-06
## 214  9.428308e-06  0.6605498  2.239637e-04
## 675  1.814349e-04  0.6518204  2.506764e-03
## 277  2.593725e-05 -0.6492523  5.241648e-04
## 1237 1.925252e-04  0.6492226  2.606791e-03
## 358  5.393940e-06  0.6371750  1.352481e-04
## 472  1.175747e-06 -0.6125182  3.626412e-05
## 278  4.092292e-05 -0.6117193  7.590360e-04
## 1030 1.668382e-18  0.5783807  1.129495e-15
## 174  5.328064e-06 -0.5482252  1.352481e-04
## 1305 2.100165e-12  0.5428530  3.554529e-10
## 801  3.563044e-12  0.5371349  4.824362e-10
## 402  5.235811e-04  0.5353851  5.763649e-03
## 1050 3.323289e-12  0.5240945  4.824362e-10
## 855  5.962657e-04  0.5120194  6.307373e-03
## 421  6.210517e-04 -0.5103711  6.468493e-03
## 349  1.463514e-11  0.5081757  1.646599e-09
## 347  2.698309e-11  0.4972853  2.435674e-09
## 1345 4.055656e-04 -0.4954344  4.748988e-03

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

## Dataset saved as " /tmp/RtmpLCUDmi/./spleen_ctrl_vs_hifibre_rna.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 = 14904.25

## Note: no. genes in output = 12665

## Note: estimated proportion of input genes in output = 0.85

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
## 183            Classical antibody-mediated complement activation      10
## 1252                                            Unwinding of DNA      12
## 210                             Creation of C4 and C2 activators      11
## 504                             Initial triggering of complement      18
## 773                                     Peptide chain elongation      55
## 1263                                      Viral mRNA Translation      55
## 237                                        DNA strand elongation      32
## 1016                                    Selenocysteine synthesis      58
## 335                           Eukaryotic Translation Termination      58
## 333                            Eukaryotic Translation Elongation      59
## 344                                              FCGR activation      16
## 821                   Processive synthesis on the lagging strand      15
## 50                     Activation of the pre-replicative complex      31
## 997  SRP-dependent cotranslational protein targeting to membrane      76
## 966          Response of EIF2AK4 (GCN2) to amino acid deficiency      65
## 372                     Formation of a pool of free 40S subunits      65
## 953                             Removal of the Flap Intermediate      14
## 562                                     Lagging Strand Synthesis      20
## 566                                     Leading Strand Synthesis      14
## 797                                         Polymerase switching      14
##           pMANOVA s.heart_ctrl_vs_acetate s.heart_ctrl_vs_hifibre
## 183  6.319598e-09               0.2558514               0.6844567
## 1252 1.208545e-13               0.7054585               0.7054058
## 210  7.679442e-08               0.2648390               0.7099875
## 504  3.066713e-11               0.3494636               0.6919779
## 773  3.623099e-26               0.6040170               0.5732218
## 1263 1.206591e-25               0.6131497               0.5454372
## 237  5.301198e-28               0.5424681               0.5377335
## 1016 5.521193e-25               0.5714532               0.5547465
## 335  5.658800e-24               0.5611333               0.5062513
## 333  9.439921e-24               0.5438680               0.5168833
## 344  8.319678e-07               0.2528411               0.5054698
## 821  4.644124e-11               0.4934387               0.5085323
## 50   1.526693e-23               0.5856853               0.4809041
## 997  5.077658e-30               0.4805616               0.4446384
## 966  4.867499e-24               0.5897192               0.5525812
## 372  2.178159e-24               0.5034640               0.4681893
## 953  6.563275e-10               0.4720011               0.4809219
## 562  2.912267e-14               0.4435824               0.4360459
## 566  7.290689e-10               0.4072744               0.3958919
## 797  7.290689e-10               0.4072744               0.3958919
##      s.spleen_ctrl_vs_acetate s.spleen_ctrl_vs_hifibre p.heart_ctrl_vs_acetate
## 183                 0.8962465                0.9483366            1.612982e-01
## 1252                0.8637609               -0.2311705            2.318041e-05
## 210                 0.7420722                0.7937842            1.283495e-01
## 504                 0.6732646                0.7473182            1.027965e-02
## 773                 0.5486468                0.5565049            9.168146e-15
## 1263                0.5233884                0.5622695            3.610445e-15
## 237                 0.7912165               -0.2105201            1.091488e-07
## 1016                0.5079636                0.5425448            5.137399e-14
## 335                 0.5243474                0.5263688            1.444985e-13
## 333                 0.5249962                0.5149982            4.990823e-13
## 344                 0.5440895                0.6974316            8.002155e-02
## 821                 0.7160580               -0.2233887            9.379205e-04
## 50                  0.6131483               -0.3247509            1.659323e-08
## 997                 0.5211233                0.5943576            4.444300e-13
## 966                 0.4404225                0.4180122            1.968594e-16
## 372                 0.5439621                0.4971978            2.241730e-12
## 953                 0.7141389               -0.2161094            2.232006e-03
## 562                 0.7461605               -0.1977224            5.953564e-04
## 566                 0.7870524               -0.1633185            8.337624e-03
## 797                 0.7870524               -0.1633185            8.337624e-03
##      p.heart_ctrl_vs_hifibre p.spleen_ctrl_vs_acetate p.spleen_ctrl_vs_hifibre
## 183             1.781699e-04             9.160906e-07             2.053192e-07
## 1252            2.321304e-05             2.191134e-07             1.656610e-01
## 210             4.547713e-05             2.024818e-05             5.130406e-06
## 504             3.706314e-07             7.581344e-07             4.008757e-08
## 773             1.919540e-13             1.945140e-12             9.375866e-13
## 1263            2.613019e-12             1.896987e-11             5.454141e-13
## 237             1.406552e-07             9.122421e-15             3.939579e-02
## 1016            2.715263e-13             2.225801e-11             8.885815e-13
## 335             2.596732e-11             4.964752e-12             4.112648e-12
## 333             6.586173e-12             3.073177e-12             7.849394e-12
## 344             4.647595e-04             1.646500e-04             1.360928e-06
## 821             6.501922e-04             1.568303e-06             1.342456e-01
## 50              3.595386e-06             3.436976e-09             1.758535e-03
## 997             2.098676e-11             4.032152e-15             3.219211e-19
## 966             1.310362e-14             8.339833e-10             5.692445e-09
## 372             6.757263e-11             3.341368e-14             4.177723e-12
## 953             1.837602e-03             3.708409e-06             1.616060e-01
## 562             7.372056e-04             7.541139e-09             1.259521e-01
## 566             1.033740e-02             3.396942e-07             2.901654e-01
## 797             1.033740e-02             3.396942e-07             2.901654e-01
##         s.dist         SD p.adjustMANOVA
## 183  1.4955070 0.31497950   4.737893e-08
## 1252 1.3396984 0.50028823   1.887632e-12
## 210  1.3247590 0.24433877   4.497959e-07
## 504  1.2699323 0.18012681   3.352940e-10
## 773  1.1419855 0.02452335   2.066742e-24
## 1263 1.1240715 0.03820134   6.596029e-24
## 237  1.1197192 0.43363795   3.311987e-26
## 1016 1.0893504 0.02689351   2.897522e-23
## 335  1.0597949 0.02293116   2.320108e-22
## 333  1.0506212 0.01318811   3.753084e-22
## 344  1.0497012 0.18442753   4.214447e-06
## 821  1.0318568 0.41078825   4.835786e-10
## 50   1.0274775 0.44598683   5.271107e-22
## 997  1.0263884 0.06423547   3.918758e-28
## 966  1.0108515 0.08383646   2.060051e-22
## 372  1.0078579 0.03121645   9.854290e-23
## 953  1.0053699 0.40185104   5.897957e-09
## 562  0.9913380 0.39703386   4.836575e-13
## 566  0.9842394 0.39145721   6.419720e-09
## 797  0.9842394 0.39145721   6.419720e-09

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

## Dataset saved as " /tmp/RtmpLCUDmi/./multi_enrichment_rna.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