Transcriptome sequencing of sedentary and trained rat cardiac tissue
Mark Ziemann
2020-12-06
Methods
Rats were kept in sedentary conditions or were trained. RNA was isolated from whole cardiac tissues ans sent to Macrogen for sequencing.
Fastqc and MultiQC were run to summarise the QC checks that were done.
Reads were then were mapped to the rat genome (Ensembl version 99) with Kallisto then imported to R for analysis with DESeq2. Pathway level analysis was then done using mitch with Reactome gene sets.
Read counts
Import the Kallisto transcript counts. We can also include some info out of the Ensembl GTF file including gene name and gene class.
# libraries
library("reshape2")
library("DESeq2")
library("mitch")
library("gplots")
# import the 3 column table
tmp<-read.table("3col.tsv.gz",header=F)
# convert the 3 col table into a standard count matrix
x<-as.matrix(acast(tmp, V2~V1, value.var="V3"))
# tidy up the column headers
colnames(x)<-sapply(strsplit(colnames(x),"_"),"[[",1)
head(x)## 1 2 3 4 5 6
## ENSRNOT00000000008.4 0.0000 15.4106 0.0000 27.2368 0.0281 10.3616
## ENSRNOT00000000009.5 14.7119 20.8913 19.6714 22.1082 14.5293 3.3838
## ENSRNOT00000000010.5 2.0000 3.0000 0.0000 2.0000 4.0000 3.0000
## ENSRNOT00000000011.5 4.0000 0.0000 0.0000 11.0000 7.0000 15.0000
## ENSRNOT00000000013.5 530.6630 445.4550 358.2370 576.9690 507.1610 381.8840
## ENSRNOT00000000018.7 5.0000 6.0000 0.0000 0.0000 4.0000 0.0000
## 7 8 9 10 11 12
## ENSRNOT00000000008.4 5.67987 39.7862 23.0690 21.2870 0.00000 0.00000
## ENSRNOT00000000009.5 27.40780 16.3483 16.9056 13.0002 6.72661 9.20012
## ENSRNOT00000000010.5 0.00000 3.0000 0.0000 9.0000 2.00000 0.00000
## ENSRNOT00000000011.5 9.00000 8.0000 12.0000 0.0000 4.00000 6.00000
## ENSRNOT00000000013.5 534.89700 607.2480 463.8250 483.4110 613.76100 433.39100
## ENSRNOT00000000018.7 0.00000 3.0000 5.0000 1.0000 0.00000 1.00000
## 13 14 15 16
## ENSRNOT00000000008.4 0.0000 19.00000 27.1574 0.0000
## ENSRNOT00000000009.5 10.8224 7.89748 11.1867 11.7551
## ENSRNOT00000000010.5 1.0000 1.00000 0.0000 2.0000
## ENSRNOT00000000011.5 9.0000 2.00000 9.0000 3.0000
## ENSRNOT00000000013.5 432.0940 427.33000 543.8830 434.8460
## ENSRNOT00000000018.7 1.0000 1.00000 0.0000 1.0000#dont forget gene names
g<-read.table("../ref/Rattus_norvegicus.Rnor_6.0.cdna+ncrna.gene_names.tsv",row.names=1)
g$gene_ID <- paste(g$V2,g$V3,g$V4)
head(g)## V2 V3 V4
## ENSRNOT00000047550.4 MT ENSRNOG00000030644.4 Mt-nd1
## ENSRNOT00000040993.4 MT ENSRNOG00000031033.4 Mt-nd2
## ENSRNOT00000050156.3 MT ENSRNOG00000034234.3 Mt-co1
## ENSRNOT00000043693.3 MT ENSRNOG00000030371.3 Mt-co2
## ENSRNOT00000046201.3 MT ENSRNOG00000033299.3 Mt-atp8
## ENSRNOT00000046108.3 MT ENSRNOG00000031979.3 Mt-atp6
## gene_ID
## ENSRNOT00000047550.4 MT ENSRNOG00000030644.4 Mt-nd1
## ENSRNOT00000040993.4 MT ENSRNOG00000031033.4 Mt-nd2
## ENSRNOT00000050156.3 MT ENSRNOG00000034234.3 Mt-co1
## ENSRNOT00000043693.3 MT ENSRNOG00000030371.3 Mt-co2
## ENSRNOT00000046201.3 MT ENSRNOG00000033299.3 Mt-atp8
## ENSRNOT00000046108.3 MT ENSRNOG00000031979.3 Mt-atp6g[,1:3]=NULL
x<-merge(g,x,by=0)
rownames(x) <- x[,1]
x[,1]=NULL
# aggregate Tx data to genes
xx <- aggregate(. ~ gene_ID,x,sum)
# now round to integers so that DESeq2 doesn't fail
rownames(xx) <- xx[,1]
xx[,1]=NULL
x <- round(xx)
head(x)## 1 2 3 4 5 6 7 8
## 1 ENSRNOG00000000417.7 Numa1 5138 7295 4700 5957 7371 4460 6152 7405
## 1 ENSRNOG00000001466.6 LOC100361492 0 0 0 0 0 0 0 4
## 1 ENSRNOG00000001488.6 Psmb1 7439 6405 4778 7201 6766 5830 7015 7392
## 1 ENSRNOG00000001489.5 Tbp 532 465 304 398 420 358 439 460
## 1 ENSRNOG00000001490.4 Pdcd2 513 515 375 541 518 441 556 610
## 1 ENSRNOG00000001492.7 Slc8a2 864 1075 922 966 948 640 852 954
## 9 10 11 12 13 14 15 16
## 1 ENSRNOG00000000417.7 Numa1 5746 5402 5825 5609 5861 5221 5501 5653
## 1 ENSRNOG00000001466.6 LOC100361492 0 0 0 0 0 0 0 0
## 1 ENSRNOG00000001488.6 Psmb1 7930 6160 7579 6787 6854 6943 7344 6261
## 1 ENSRNOG00000001489.5 Tbp 359 426 388 356 463 388 438 425
## 1 ENSRNOG00000001490.4 Pdcd2 524 436 517 511 437 491 623 516
## 1 ENSRNOG00000001492.7 Slc8a2 882 888 931 885 899 731 791 932Samplesheet
samplesheet <- read.table("samplesheet.tsv",header=TRUE)Overall clustering with multidimensional scaling
This indicates that there is no clear clustering of samples by treatment group.
ss <- samplesheet
colours = c('pink', 'lightblue','lightgreen','gray')
mds <- cmdscale(dist(t(x)))
XMAX=max(mds[,1])*1.1
XMIN=min(mds[,1])*1.1
plot( mds*1.05 , cex=2, pch=19, xlab="Coordinate 1", ylab="Coordinate 2",
col = colours[as.factor(ss$Group)] , type = "p" ,
xlim=c(XMIN,XMAX),main="MDS plot",bty="n")
text(mds, labels=colnames(x) )
legend('topright', col=colours, legend=levels(as.factor(ss$Group)), pch = 16, cex = 1)
Number of reads is 60 to 80 M reads which is really comprehensive.
par(mar=c(5,10,5,3))
barplot(colSums(x),horiz=TRUE,las=2,main="number of reads per sample",cex.names=1)
par(mai=c(1.02,0.82,0.82,0.42))Check purity of mito fraction samples
Here I'm quantifying the mitochondrial read fraction. That is the number of mt reads divided by the total number of reads. There is no significant difference between groups. The mito frac is slightly larger here as compared to the skeletal muscle.
par(mar=c(5,10,5,3))
mtfrac <- colSums(x[grep("^MT",rownames(x)),]) / colSums(x)
barplot(mtfrac,horiz=TRUE,las=2,main="Proportion mitochondrial reads",cex.names=1)
par(mai=c(1.02,0.82,0.82,0.42))
mylevels <- levels(as.factor(ss$Group))
mylevels## [1] "S" "T"y <- x[,which(ss$Group==mylevels[1])]
wholeS <- colSums(y[grep("^MT",rownames(y)),]) / colSums(y)
y <- x[,which(ss$Group==mylevels[2])]
wholeT <- colSums(y[grep("^MT",rownames(y)),]) / colSums(y)
boxplot(wholeS,wholeT,names=mylevels,ylab="mito frac")
t.test(wholeS,wholeT)##
## Welch Two Sample t-test
##
## data: wholeS and wholeT
## t = 0.066164, df = 13.806, p-value = 0.9482
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.03314957 0.03525704
## sample estimates:
## mean of x mean of y
## 0.4467534 0.4456996Functions
run_de <- function(ss,xx){
y <- round(xx)
# MDS
colours = c('yellow', 'orange')
mds <- cmdscale(dist(t(y)))
XMAX=max(mds[,1])*1.1
XMIN=min(mds[,1])*1.1
plot( mds*1.05 , cex=2 , pch=19, xlab="Coordinate 1", ylab="Coordinate 2",
col = colours[as.factor(ss$trt)] , type = "p" ,
xlim=c(XMIN,XMAX),main="MDS plot",bty="n")
text(mds, labels=colnames(y) )
legend('topright', col=colours, legend=c("ctrl","trt"), pch = 16, cex = 1.5)
# DE
dds <- DESeqDataSetFromMatrix(countData=y, colData = ss, design = ~ trt)
dds <- DESeq(dds)
de <- DESeq2::results(dds)
de <- de[order(de$pvalue),]
up <- rownames(subset(de, log2FoldChange>0 & padj<0.05 ))
dn <- rownames(subset(de, log2FoldChange<0 & padj<0.05 ))
str(up)
str(dn)
# MA plot
sig <-subset(de, padj < 0.05 )
GENESUP <- length(up)
GENESDN <- length(dn)
SUBHEADER = paste(GENESUP, "up, ", GENESDN, "down")
ns <-subset(de, padj > 0.05 )
plot(log2(de$baseMean),de$log2FoldChange,
xlab="log2 basemean", ylab="log2 foldchange",
pch=19, cex=0.5, col="dark gray",
main="smear plot")
points(log2(sig$baseMean),sig$log2FoldChange,
pch=19, cex=0.5, col="red")
mtext(SUBHEADER)
# heatmap
yn <- y/colSums(y)*1000000
yf <- yn[which(rownames(yn) %in% rownames(de)[1:50]),]
mycols <- gsub("0","yellow",ss$trt)
mycols <- gsub("1","orange",mycols)
colfunc <- colorRampPalette(c("blue", "white", "red"))
heatmap.2( as.matrix(yf), col=colfunc(25),scale="row",
ColSideColors =mycols ,trace="none",
margin = c(10,15), cexRow=0.6, cexCol=0.8 , main="Top 50 genes by p-val")
mtext("yellow=ctrl, orange=trt")
return(de)
}Set up contrasts and perform DE analysis
There is just 1 contrast: Whole tissue S versus T
We are using DESeq2 to perform differential expression analysis for the specified contrasts. The run_de function does the analysis and generate the charts. Here we actually run the analysis.
Here I show there are no individual genes with a statistically significant difference between groups.
ss$trt <- as.numeric(as.factor(ss$Group))-1
ss## rat_ID Macrogen_ID Group trt
## 1 10 1 T 1
## 2 14 2 T 1
## 3 15 3 S 0
## 4 16 4 S 0
## 5 17 5 T 1
## 6 18 6 T 1
## 7 19 7 S 0
## 8 20 8 S 0
## 9 25 9 T 1
## 10 26 10 T 1
## 11 27 11 T 1
## 12 28 12 T 1
## 13 29 13 S 0
## 14 30 14 S 0
## 15 31 15 S 0
## 16 32 16 S 0de1 <- run_de(ss,x)## converting counts to integer mode## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() function## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing## -- replacing outliers and refitting for 318 genes
## -- DESeq argument 'minReplicatesForReplace' = 7
## -- original counts are preserved in counts(dds)## estimating dispersions## fitting model and testing
## chr(0)
## chr(0)
as.data.frame(de1[1:20,])## baseMean log2FoldChange lfcSE
## 1 ENSRNOG00000015159.5 Slc9a3 494.793935 0.6049250 0.13481430
## 9 ENSRNOG00000050669.2 LOC100911515 185.462196 -0.4447389 0.10143027
## 4 ENSRNOG00000030870.1 LOC102550396 1309.219092 -0.2344309 0.05698012
## 5 ENSRNOG00000049272.2 AABR07048303.1 147.229842 -0.9886772 0.24305854
## 16 ENSRNOG00000018233.6 Gas6 15552.691397 0.1440238 0.03653203
## X ENSRNOG00000059506.1 AC124926.2 2261.195338 -0.1982355 0.05029631
## 15 ENSRNOG00000035649.1 Mir92a1 2.515525 3.9934257 1.02518327
## 7 ENSRNOG00000042496.4 Cyp4f5 660.889516 0.2223138 0.05730230
## 13 ENSRNOG00000007887.6 Elk4 4502.746382 -0.1263282 0.03352866
## 7 ENSRNOG00000053692.1 AABR07058706.1 3.658260 3.1178459 0.83951580
## 12 ENSRNOG00000001099.7 Rbak 424.871437 -0.2419566 0.06523409
## X ENSRNOG00000060289.1 7SK 19011.363609 -0.4514183 0.12198723
## 1 ENSRNOG00000021017.4 Ca11 502.539670 0.2541628 0.06941464
## 16 ENSRNOG00000032297.2 Msmo1 728.556765 -0.2417691 0.06605013
## 4 ENSRNOG00000012337.7 Pde1c 129.468152 -0.4773449 0.13064519
## 13 ENSRNOG00000030481.2 LOC100362110 83.721720 -0.5847814 0.16076500
## 7 ENSRNOG00000007934.4 Elfn2 49.425186 0.9108554 0.25065659
## 1 ENSRNOG00000015085.8 Dmpk 14917.503531 0.1667698 0.04605139
## 3 ENSRNOG00000008534.8 Dusp15 468.208933 0.9207670 0.25881554
## 10 ENSRNOG00000004159.5 Flii 7983.953424 0.1340516 0.03829426
## stat pvalue padj
## 1 ENSRNOG00000015159.5 Slc9a3 4.487098 7.219987e-06 0.1528416
## 9 ENSRNOG00000050669.2 LOC100911515 -4.384676 1.161587e-05 0.1528416
## 4 ENSRNOG00000030870.1 LOC102550396 -4.114258 3.884267e-05 0.3124335
## 5 ENSRNOG00000049272.2 AABR07048303.1 -4.067651 4.748952e-05 0.3124335
## 16 ENSRNOG00000018233.6 Gas6 3.942397 8.067136e-05 0.3440807
## X ENSRNOG00000059506.1 AC124926.2 -3.941353 8.102332e-05 0.3440807
## 15 ENSRNOG00000035649.1 Mir92a1 3.895329 9.806570e-05 0.3440807
## 7 ENSRNOG00000042496.4 Cyp4f5 3.879667 1.045997e-04 0.3440807
## 13 ENSRNOG00000007887.6 Elk4 -3.767768 1.647139e-04 0.4284108
## 7 ENSRNOG00000053692.1 AABR07058706.1 3.713862 2.041201e-04 0.4284108
## 12 ENSRNOG00000001099.7 Rbak -3.709052 2.080366e-04 0.4284108
## X ENSRNOG00000060289.1 7SK -3.700537 2.151435e-04 0.4284108
## 1 ENSRNOG00000021017.4 Ca11 3.661516 2.507271e-04 0.4284108
## 16 ENSRNOG00000032297.2 Msmo1 -3.660388 2.518340e-04 0.4284108
## 4 ENSRNOG00000012337.7 Pde1c -3.653750 2.584380e-04 0.4284108
## 13 ENSRNOG00000030481.2 LOC100362110 -3.637492 2.753060e-04 0.4284108
## 7 ENSRNOG00000007934.4 Elfn2 3.633878 2.791934e-04 0.4284108
## 1 ENSRNOG00000015085.8 Dmpk 3.621384 2.930307e-04 0.4284108
## 3 ENSRNOG00000008534.8 Dusp15 3.557619 3.742322e-04 0.5183313
## 10 ENSRNOG00000004159.5 Flii 3.500566 4.642708e-04 0.6108875write.table(de1,file="heart1.tsv",quote=FALSE,sep="\t")Pathway analysis with mitch
Here we are doing a gene set analysis with my R package called mitch. I'm using gene sets downloaded from Reactome 5th Dec 2020.
We lost 46% of genes after converting from rat to human.
There were 259 differentially regulated gene sets (FDR<0.05). Of these 81 were downregulated and 178 were upregulated.
#download.file("https://reactome.org/download/current/ReactomePathways.gmt.zip",
# destfile="ReactomePathways.gmt.zip")
#unzip("ReactomePathways.gmt.zip")
genesets<-gmt_import("ReactomePathways.gmt")
# i need to get some data from biomart to link rat and human gene IDs
mart <- read.table("mart_export.txt",header=TRUE,sep="\t")
gt <- mart[,c("Gene.stable.ID","Human.gene.name")]
rownames(de1) <- sapply( strsplit(rownames(de1)," ") , "[[", 2)
rownames(de1) <- sapply( strsplit(rownames(de1),"\\.") , "[[", 1)
m <- mitch_import(as.data.frame(de1),"DESeq2",geneTable=gt)## The input is a single dataframe; one contrast only. Converting
## it to a list for you.## Note: Mean no. genes in input = 32852## Note: no. genes in output = 17707## Note: estimated proportion of input genes in output = 0.539res<-mitch_calc(m,genesets,priority="significance")## Note: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.nrow(subset(res$enrichment_result,p.adjustANOVA<0.05))## [1] 259head(res$enrichment_result,20)## set
## 710 Mitochondrial translation elongation
## 709 Mitochondrial translation
## 712 Mitochondrial translation termination
## 1343 Translation
## 711 Mitochondrial translation initiation
## 1303 The citric acid (TCA) cycle and respiratory electron transport
## 664 Macroautophagy
## 103 Autophagy
## 925 Protein localization
## 688 Metabolism of proteins
## 272 Defective CFTR causes cystic fibrosis
## 2 ABC transporter disorders
## 673 Metabolism
## 1070 Respiratory electron transport
## 1127 Selective autophagy
## 4 ABC-family proteins mediated transport
## 534 Hh mutants that don't undergo autocatalytic processing are degraded by ERAD
## 533 Hh mutants abrogate ligand secretion
## 587 Interleukin-1 signaling
## 1440 tRNA Aminoacylation
## setSize pANOVA s.dist p.adjustANOVA
## 710 85 6.412365e-21 0.58831140 7.057158e-18
## 709 91 9.760938e-21 0.56599894 7.057158e-18
## 712 85 3.676449e-20 0.57668556 1.772048e-17
## 1343 263 2.856601e-19 0.32154301 1.032661e-16
## 711 85 7.749816e-19 0.55581726 2.241247e-16
## 1303 165 3.558952e-13 0.32813473 8.577073e-11
## 664 108 3.979599e-12 0.38638454 8.220714e-10
## 103 122 1.977157e-11 0.35163259 3.573712e-09
## 925 156 4.445177e-11 0.30573464 7.141918e-09
## 688 1810 1.061367e-10 0.09244512 1.534737e-08
## 272 58 2.329892e-09 0.45335576 3.062749e-07
## 2 74 3.619245e-09 0.39669324 4.361191e-07
## 673 1943 4.823253e-09 0.08121814 5.364942e-07
## 1070 96 5.355341e-09 0.34466858 5.531302e-07
## 1127 56 1.248351e-08 0.43974947 1.195550e-06
## 4 97 1.322877e-08 0.33393573 1.195550e-06
## 534 53 2.632772e-08 0.44177385 2.239405e-06
## 533 56 3.100625e-08 0.42760730 2.490835e-06
## 587 94 4.172970e-08 0.32728425 3.175850e-06
## 1440 42 5.935994e-08 0.48332322 4.240588e-06mitch_barplot <- function(res){
sig <- head(subset(res$enrichment_result,p.adjustANOVA<0.05),30)
sig <- sig[order(sig$s.dist),]
par(mar=c(3,25,1,1)); barplot(sig$s.dist,horiz=TRUE,las=2,cex.names = 0.6,cex.axis = 0.6,
names.arg=sig$set,main="Enrichment score") ;grid()
}
mitch_barplot(res)
nrow(subset(res$enrichment_result,p.adjustANOVA<0.05&s.dist<0))## [1] 81nrow(subset(res$enrichment_result,p.adjustANOVA<0.05&s.dist>0))## [1] 178unlink("heart_mitch1.html")
mitch_report(res,outfile="heart_mitch1.html")## Dataset saved as " /tmp/RtmpU1aNQ3/heart_mitch1.rds ".##
##
## processing file: mitch.Rmd##
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## $ fig.width : num 6
## $ out.width : chr "80%"
## $ comment : logi NA
## $ message : logi FALSE
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## ordinary text without R code
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## label: session_info (with options)
## List of 3
## $ include: logi TRUE
## $ echo : logi TRUE
## $ results: chr "markup"
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## ordinary text without R code## output file: /mnt/bfx6/bfx/adam_trewin/set3/mitch.knit.md## /usr/bin/pandoc +RTS -K512m -RTS /mnt/bfx6/bfx/adam_trewin/set3/mitch.utf8.md --to html4 --from markdown+autolink_bare_uris+tex_math_single_backslash --output /tmp/RtmpU1aNQ3/mitch_report.html --email-obfuscation none --self-contained --standalone --section-divs --template /usr/local/lib/R/site-library/rmarkdown/rmd/h/default.html --no-highlight --variable highlightjs=1 --variable 'theme:bootstrap' --include-in-header /tmp/RtmpU1aNQ3/rmarkdown-str4b464efb824d.html --mathjax --variable 'mathjax-url:https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'##
## Output created: /tmp/RtmpU1aNQ3/mitch_report.html## [1] TRUESession info
sessionInfo()## R version 4.0.3 (2020-10-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.5 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] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] pkgload_1.1.0 GGally_2.0.0
## [3] ggplot2_3.3.2 beeswarm_0.2.3
## [5] gtools_3.8.2 tibble_3.0.4
## [7] dplyr_1.0.2 echarts4r_0.3.3
## [9] gplots_3.1.0 mitch_1.2.2
## [11] DESeq2_1.30.0 SummarizedExperiment_1.20.0
## [13] Biobase_2.50.0 MatrixGenerics_1.2.0
## [15] matrixStats_0.57.0 GenomicRanges_1.42.0
## [17] GenomeInfoDb_1.26.0 IRanges_2.24.0
## [19] S4Vectors_0.28.0 BiocGenerics_0.36.0
## [21] reshape2_1.4.4
##
## loaded via a namespace (and not attached):
## [1] httr_1.4.2 jsonlite_1.7.1 bit64_4.0.5
## [4] splines_4.0.3 assertthat_0.2.1 shiny_1.5.0
## [7] highr_0.8 blob_1.2.1 GenomeInfoDbData_1.2.4
## [10] yaml_2.2.1 backports_1.2.0 pillar_1.4.6
## [13] RSQLite_2.2.1 lattice_0.20-41 glue_1.4.2
## [16] digest_0.6.27 RColorBrewer_1.1-2 promises_1.1.1
## [19] XVector_0.30.0 colorspace_2.0-0 htmltools_0.5.0
## [22] httpuv_1.5.4 Matrix_1.2-18 plyr_1.8.6
## [25] XML_3.99-0.5 pkgconfig_2.0.3 genefilter_1.72.0
## [28] zlibbioc_1.36.0 purrr_0.3.4 xtable_1.8-4
## [31] scales_1.1.1 later_1.1.0.1 BiocParallel_1.24.1
## [34] annotate_1.68.0 generics_0.1.0 ellipsis_0.3.1
## [37] withr_2.3.0 survival_3.2-7 magrittr_1.5
## [40] crayon_1.3.4 mime_0.9 evaluate_0.14
## [43] memoise_1.1.0 MASS_7.3-53 tools_4.0.3
## [46] lifecycle_0.2.0 stringr_1.4.0 munsell_0.5.0
## [49] locfit_1.5-9.4 DelayedArray_0.16.0 AnnotationDbi_1.52.0
## [52] compiler_4.0.3 caTools_1.18.0 rlang_0.4.8
## [55] grid_4.0.3 RCurl_1.98-1.2 htmlwidgets_1.5.2
## [58] rmarkdown_2.5 bitops_1.0-6 testthat_3.0.0
## [61] gtable_0.3.0 DBI_1.1.0 reshape_0.8.8
## [64] R6_2.5.0 gridExtra_2.3 knitr_1.30
## [67] fastmap_1.0.1 bit_4.0.4 rprojroot_1.3-2
## [70] desc_1.2.0 KernSmooth_2.23-18 stringi_1.5.3
## [73] Rcpp_1.0.5 vctrs_0.3.4 geneplotter_1.68.0
## [76] tidyselect_1.1.0 xfun_0.19