Simulation results from the supplement II (large-scale simulation)
Pedro L. Baldoni
09 October, 2024
Last updated: 2024-10-09
Checks: 7 0
Knit directory: GibbsDTE-code/analysis/
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Modified: code/pkg/R/simulation-quant.R
Modified: code/pkg/R/simulation-summary.R
Modified: code/pkg/R/simulation-utils.R
Modified: code/pkg/R/simulation.R
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 529139f | Pedro Baldoni | 2024-10-09 | NARGAB Revision from October 2024 |
Introduction
This Rmarkdown generates Supplementary Figures S11 and S12 from our Gibbs DTE paper. The results are based on the simulation designed to emulate large-scale experiments with large biological variation and sample size, a typical scenario of observational RNA-seq studies with human subjects.
Setup
Below we set options for this Rmarkdown report and load the necessary libraries.
knitr::opts_chunk$set(dev = "png",
dpi = 300,
dev.args = list(type = "cairo-png"),
root.dir = '.',
autodep = TRUE)
options(knitr.kable.NA = "-")
bs <- 8library(data.table)
library(magrittr)
library(ggplot2)
library(patchwork)
library(plyr)
library(edgeR)
library(png)
library(kableExtra)
devtools::load_all('../code/pkg')Next we set the paths to our simulation results.
path.misc <- file.path('../misc/simulation-large-supp.Rmd')
dir.create(path.misc,recursive = TRUE,showWarnings = FALSE)
path.example <- "../output/simulation-large/data/mm39/readlen-100/fc2/paired-end/9999TxPerGene/unbalanced/100libsPerGroup/simulation-1"
path.fdr <-
list.files('../output/simulation-large/summary','fdr.tsv.gz',recursive = TRUE,full.names = TRUE)
path.metrics <-
list.files('../output/simulation-large/summary','metrics.tsv.gz',recursive = TRUE,full.names = TRUE)
path.metrics.1pct <-
list.files('../output/simulation-large/summary','metrics.1pct.tsv.gz',recursive = TRUE,full.names = TRUE)
path.time <-
list.files('../output/simulation-large/summary','^time.tsv.gz',recursive = TRUE,full.names = TRUE)
path.quanttime <-
list.files('../output/simulation-large/summary','quanttime.tsv.gz',recursive = TRUE,full.names = TRUE)
path.quantile <-
list.files('../output/simulation-large/summary','quantile.tsv.gz',recursive = TRUE,full.names = TRUE)
path.pvalue <-
list.files('../output/simulation-large/summary','pvalue.tsv.gz',recursive = TRUE,full.names = TRUE)
path.overdispersion <-
list.files('../output/simulation-large/summary','overdispersion.tsv.gz',recursive = TRUE,full.names = TRUE)
path.roc <-
list.files('../output/simulation-large/summary','roc.tsv.gz',recursive = TRUE,full.names = TRUE)Data loading
Below we load the actual results and relabel some of the factor variables.
relabel <- function(x){
y <- copy(x)
y$TxPerGene %<>% factor(levels = paste0(c(2, 3, 4, 5, 9999), 'TxPerGene'),labels = c(paste0("#Tx/Gene = ", c(2, 3, 4, 5)), 'All Transcripts')) %<>% droplevels()
y$LibsPerGroup %<>% factor(levels = paste0(c(3, 5, 10,100), 'libsPerGroup'),labels = paste0('#Lib/Group = ', c(3, 5, 10,100))) %<>% droplevels()
y$Quantifier %<>% factor(levels = c('salmon','salmon-gibbs','kallisto'),labels = c('Salmon-Boot','Salmon-Gibbs','kallisto')) %<>% droplevels()
y$Length %<>% factor(levels = paste0('readlen-', seq(50, 150, 25)),labels = paste0(seq(50, 150, 25), 'bp')) %<>% droplevels()
if (!is.null(x$Sample)) {
y$Sample %<>% factor(levels = c(paste0("groupA_rep",1:100,"_R1"),paste0("groupB_rep",1:100,"_R1")),labels = c(paste0("A",1:100),paste0("B",1:100))) %<>% droplevels()
}
return(y)
}
# Loading datasets
dt.fdr <- do.call(rbind,lapply(path.fdr,fread)) %>% relabel()
dt.metrics <- do.call(rbind,lapply(path.metrics,fread)) %>% relabel()
dt.metrics.1pct <- do.call(rbind,lapply(path.metrics.1pct,fread)) %>% relabel()
dt.time <- do.call(rbind,lapply(path.time,fread)) %>% relabel()
dt.quanttime <- do.call(rbind,lapply(path.quanttime,fread)) %>% relabel()
dt.quantile <- do.call(rbind,lapply(path.quantile,fread)) %>% relabel()
dt.pvalue <- do.call(rbind,lapply(path.pvalue,fread)) %>% relabel()
dt.overdispersion <- do.call(rbind,lapply(path.overdispersion,fread)) %>% relabel()
dt.roc <- do.call(rbind,lapply(path.roc,fread)) %>% relabel()BCV plots
First we compute the BCV plots from Supplementary Figure S11. These BCV plots are from a single simulated experiment and are shown as an example of the count scaling effect on the estimated BCV coefficients.
quant.gibbs <- list.dirs(file.path(path.example,'quant-salmon-gibbs'),recursive = FALSE,full.names = TRUE)
cs.gibbs <- catchSalmon(quant.gibbs,verbose = FALSE)
group <- strsplit2(basename(colnames(cs.gibbs$counts)),"_")[,1]
design <- model.matrix(~group)
dge.gibbs.raw <- DGEList(counts = cs.gibbs$counts,
group = group,
annotation = cs.gibbs$annotation)
dge.gibbs.raw <- dge.gibbs.raw[filterByExpr(dge.gibbs.raw,design),,keep.lib.sizes = FALSE]
dge.gibbs.raw <- normLibSizes(dge.gibbs.raw)
dge.gibbs.raw <- estimateDisp(dge.gibbs.raw,design = design)
dge.gibbs.scaled <- DGEList(counts = cs.gibbs$counts/cs.gibbs$annotation$Overdispersion,
group = group,
annotation = cs.gibbs$annotation)
dge.gibbs.scaled <- dge.gibbs.scaled[filterByExpr(dge.gibbs.scaled,design),,keep.lib.sizes = FALSE]
dge.gibbs.scaled <- normLibSizes(dge.gibbs.scaled)
dge.gibbs.scaled <- estimateDisp(dge.gibbs.scaled,design = design)
plotBCV2 <- function(y, xlab="Average log CPM", ylab="Biological coefficient of variation", pch=16, cex=0.2, col.common="red", col.trend="blue", col.tagwise="black",fontsize = 8,...)
{
if(!is(y,"DGEList")) stop("y must be a DGEList.")
A <- y$AveLogCPM
if(is.null(A)) A <- aveLogCPM(y$counts, offset=getOffset(y))
disp <- getDispersion(y)
if(is.null(disp)) stop("No dispersions to plot")
if(attr(disp,"type")=="common") disp <- rep_len(disp, length(A))
plot(A, sqrt(disp), xlab=xlab, ylab=ylab, type="n", cex.lab = fontsize/12,
cex.axis = fontsize/12,...)
labels <- cols <- lty <- pt <- NULL
if(!is.null(y$tagwise.dispersion)) {
points(A, sqrt(y$tagwise.dispersion), pch=pch, cex=cex, col=col.tagwise)
labels <- c(labels, "Tagwise")
cols <- c(cols, col.tagwise)
lty <- c(lty, -1)
pt <- c(pt, pch)
}
if(!is.null(y$common.dispersion)) {
abline(h=sqrt(y$common.dispersion), col=col.common, lwd=2)
labels <- c(labels, "Common")
cols <- c(cols, col.common)
lty <- c(lty, 1)
pt <- c(pt, -1)
}
if(!is.null(y$trended.dispersion)) {
o <- order(A)
lines(A[o], sqrt(y$trended.dispersion)[o], col=col.trend, lwd=2)
labels <- c(labels, "Trend")
cols <- c(cols, col.trend)
lty <- c(lty, 1)
pt <- c(pt, -1)
}
legend("topright", legend=labels, lty=lty, pch=pt, pt.cex=cex, lwd=2, col=cols,cex = fontsize/12)
invisible()
}
fig.bcv.raw <- wrap_elements(full = ~ plotBCV2(dge.gibbs.raw))
fig.bcv.scaled <- wrap_elements(full = ~ plotBCV2(dge.gibbs.scaled))
file.raw <- tempfile("raw",fileext = '.png')
png(file.raw,width = 3.5,height = 2.75,units = 'in',res = 300)
par(mar = c(3, 3, 0.25, 0.25),mgp = c(2,1,0))
fig.bcv.raw
dev.off()png
2 fig.bcv.raw <- readPNG(file.raw, native = TRUE)
file.scaled <- tempfile("scaled",fileext = '.png')
png(file.scaled,width = 3.5,height = 2.75,units = 'in',res = 300)
par(mar = c(3, 3, 0.25, 0.25),mgp = c(2,1,0))
fig.bcv.scaled
dev.off()png
2 fig.bcv.scaled <- readPNG(file.scaled, native = TRUE)
wrap_plots(A = wrap_elements(fig.bcv.raw),
B = wrap_elements(fig.bcv.scaled),
design = c(area(1,1),area(1,2))) +
plot_annotation(tag_levels = 'a',theme = theme(plot.tag = element_text(size = bs)))
Power
Below I compute the barplots of Supplementary Figure S11. Panel (c) reports the observed power and FDR under a nominal FDR threshold of 0.05.
dt.power <- dt.metrics[FC == 'fc2' &
Quantifier != 'kallisto' &
!grepl("\\.n|RC",Method) &
Scenario == 'unbalanced',]
dt.power$LibsPerGroup %<>% factor(levels = paste0('#Lib/Group = ', c(3, 5,10,100)),labels = paste0(c(3,5,10,100),' samples per group'))
dt.power$Scenario %<>% factor(levels = c('balanced','unbalanced'),labels = c('Equal library sizes','Unequal library sizes'))
dt.power$Quantifier %<>% factor(levels = c('Salmon-Boot','Salmon-Gibbs'),labels = c('Bootstrap','Gibbs'))
dt.power$Method %<>% mapvalues(from = c('edgeR-SC','edgeR.legacy-SC'),to = c('edgeR-v4','edgeR-v3'))
dt.power[, FDR := roundPretty(ifelse((FP+TP) == 0,NA,100*FP/(FP+TP)),1)]
x.melt <- melt(dt.power,id.vars = colnames(dt.power)[-which(colnames(dt.power) %in% c('P.SIG','TP','FP'))],
measure.vars = c('TP','FP'),
variable.name = 'Type',
value.name = 'Value')
x.melt$Type <-
factor(x.melt$Type,
levels = c('FP','TP'),
labels = c('False','True'))
plot.power <- function(df.bar,df.txt,scenario,library,legend = FALSE, base_size = bs){
tb.bar <- df.bar[Scenario == scenario & LibsPerGroup == library,]
tb.txt <- df.txt[Scenario == scenario & LibsPerGroup == library,][FDR != 'NA',]
gap <- 0.05*max(dt.power$TP + dt.power$FP)
ggplot(tb.bar,aes(x = Quantifier,y = Value,fill = Type)) +
facet_wrap(facets = "Method",nrow = 1) +
geom_col() +
geom_text(aes(x = Quantifier,y = (TP + FP) + gap,label = FDR),
vjust = 0,data = tb.txt,size = base_size/.pt,inherit.aes = FALSE) +
scale_fill_manual(values = c('#ff0000','#bebebe')) +
labs(x = NULL,y = paste('DE Transcripts')) +
scale_y_continuous(limits = c(0,6000)) +
theme_bw(base_size = base_size,base_family = 'sans') +
theme(panel.grid = element_blank(),
# axis.text.x = element_text(angle = 90),
axis.text = element_text(colour = 'black',size = base_size),
strip.text = element_text(size = base_size)) +
if (legend == TRUE) theme(legend.background = element_rect(fill = alpha('white', 0)),
legend.text = element_text(size = base_size),
legend.position.inside = c(0.9,0.825),
legend.position = 'inside',
legend.direction = 'horizontal',
legend.title = element_blank(),
legend.key.size = unit(0.75,"line")) else theme(legend.position = 'none')
}
fig.power <- plot.power(df.bar = x.melt,
df.txt = dt.power,
scenario = 'Unequal library sizes',
library = '100 samples per group',
legend = TRUE)
fig.power
Analogously, we create panel (d), which reports the observed power and FDR under a nominal FDR threshold of 0.01.
dt.power.1pct <- dt.metrics.1pct[FC == 'fc2' &
Quantifier != 'kallisto' &
!grepl("\\.n|RC",Method) &
Scenario == 'unbalanced',]
dt.power.1pct$LibsPerGroup %<>% factor(levels = paste0('#Lib/Group = ', c(3, 5,10,100)),labels = paste0(c(3,5,10,100),' samples per group'))
dt.power.1pct$Scenario %<>% factor(levels = c('balanced','unbalanced'),labels = c('Equal library sizes','Unequal library sizes'))
dt.power.1pct$Quantifier %<>% factor(levels = c('Salmon-Boot','Salmon-Gibbs'),labels = c('Bootstrap','Gibbs'))
dt.power.1pct$Method %<>% mapvalues(from = c('edgeR-SC','edgeR.legacy-SC'),to = c('edgeR-v4','edgeR-v3'))
dt.power.1pct[, FDR := roundPretty(ifelse((FP+TP) == 0,NA,100*FP/(FP+TP)),1)]
x.melt.1pct <- melt(dt.power.1pct,id.vars = colnames(dt.power.1pct)[-which(colnames(dt.power.1pct) %in% c('P.SIG','TP','FP'))],
measure.vars = c('TP','FP'),
variable.name = 'Type',
value.name = 'Value')
x.melt.1pct$Type <-
factor(x.melt.1pct$Type,
levels = c('FP','TP'),
labels = c('False','True'))
plot.power <- function(df.bar,df.txt,scenario,library,legend = FALSE, base_size = bs){
tb.bar <- df.bar[Scenario == scenario & LibsPerGroup == library,]
tb.txt <- df.txt[Scenario == scenario & LibsPerGroup == library,][FDR != 'NA',]
gap <- 0.05*max(dt.power.1pct$TP + dt.power.1pct$FP)
ggplot(tb.bar,aes(x = Quantifier,y = Value,fill = Type)) +
facet_wrap(facets = "Method",nrow = 1) +
geom_col() +
geom_text(aes(x = Quantifier,y = (TP + FP) + gap,label = FDR),
vjust = 0,data = tb.txt,size = base_size/.pt,inherit.aes = FALSE) +
scale_fill_manual(values = c('#ff0000','#bebebe')) +
labs(x = NULL,y = paste('DE Transcripts')) +
scale_y_continuous(limits = c(0,6000)) +
theme_bw(base_size = base_size,base_family = 'sans') +
theme(panel.grid = element_blank(),
# axis.text.x = element_text(angle = 90),
axis.text = element_text(colour = 'black',size = base_size),
strip.text = element_text(size = base_size)) +
if (legend == TRUE) theme(legend.background = element_rect(fill = alpha('white', 0)),
legend.text = element_text(size = base_size),
legend.position.inside = c(0.9,0.825),
legend.position = 'inside',
legend.direction = 'horizontal',
legend.title = element_blank(),
legend.key.size = unit(0.75,"line")) else theme(legend.position = 'none')
}
fig.power.1pct <- plot.power(df.bar = x.melt.1pct,
df.txt = dt.power.1pct,
scenario = 'Unequal library sizes',
library = '100 samples per group',
legend = TRUE)
fig.power.1pct
ROC curves
Below I generate the plots showing observed power and FDR under different nominal FDR threshold values and number of resamples. This is panel (e) from Supplementary Figure S11.
dt.rcurve <- dt.roc[FC == 'fc2' &
grepl("edgeR-SC|edgeR-SC\\.n",Method) &
Scenario == 'unbalanced' &
Quantifier != 'kallisto',]
dt.rcurve$LibsPerGroup %<>% factor(levels = paste0('#Lib/Group = ', c(3, 5, 10,100)),labels = paste0(c('Three','Five', 'Ten','Hundred'),' samples per group'))
dt.rcurve$Scenario %<>% factor(levels = c('balanced','unbalanced'),labels = c('Equal library sizes','Unequal library sizes'))
dt.rcurve$Quantifier %<>% factor(levels = c('Salmon-Boot','Salmon-Gibbs'),labels = c('Bootstrap','Gibbs'))
dt.rcurve$Method %<>%
factor(levels = c(paste0('edgeR-SC.n',c(2,3)),'edgeR-SC')) %<>%
mapvalues(from = c(paste0('edgeR-SC.n',c(2,3)),'edgeR-SC'),to = paste0(c(2,3,20)))
dt.rcurve.long <- melt(dt.rcurve,
id.vars = c('Genome','Length','FC','Reads','TxPerGene','Scenario','LibsPerGroup','Quantifier','Method','nFDR'),
measure.vars = c('oTPR','oFDR'),
variable.name = 'Type',
value.name = 'Value')
foo <- function(x,lib,type,ylab,title = NULL,base_size = bs,legend = TRUE,...){
sub.x <- x[LibsPerGroup == lib & Type == type,]
sub.x$nFDR_char <- roundPretty(sub.x$nFDR,digits = 2)
sub.x$nFDR_char %<>% factor(levels = c('0.01','0.05','0.10','0.15','0.20'))
shape <- 21:25
names(shape) <- c('0.01','0.05','0.10','0.15','0.20')
p <- ggplot(data = sub.x, aes(x = Method,y = Value,group = interaction(nFDR_char,Quantifier))) +
geom_line(aes(color = Quantifier),linewidth = 0.25) +
geom_point(aes(shape = nFDR_char,color = Quantifier,fill = Quantifier),size = 1.25) +
theme_bw(base_size = bs,base_family = 'sans') +
scale_color_manual(values = c('Bootstrap' = 'red','Gibbs' = 'blue')) +
scale_fill_manual(values = c('Bootstrap' = 'red','Gibbs' = 'blue'),guide = "none") +
scale_shape_manual(values = shape) +
coord_cartesian(...) +
theme(panel.grid = element_blank(),
plot.title = element_text(hjust = 0.5,size = base_size),
axis.text = element_text(colour = 'black',size = base_size),
strip.text = element_text(colour = 'black',size = base_size)) +
labs(x = 'Number of Resamples',y = ylab,shape = 'Nominal FDR',color = 'Method',title = title) +
if (legend == TRUE) {theme(legend.background = element_rect(fill = alpha('white', 0)),
legend.text = element_text(size = base_size),
legend.title = element_text(size = base_size),
legend.margin = margin(0, 0, 0, 0))} else theme(legend.position = 'none')
p
}
fig.roc.hundred.tpr <- foo(dt.rcurve.long,lib = 'Hundred samples per group',type = 'oTPR',ylab = 'Observed Power',ylim = c(0.9,1))
fig.roc.hundred.fdr <- foo(dt.rcurve.long,lib = 'Hundred samples per group',type = 'oFDR',ylab = 'Observed FDR',ylim = c(0,0.2))
fig.roc <- wrap_plots(A = fig.roc.hundred.tpr,
B = fig.roc.hundred.fdr,
design = c(area(1,1),area(1,2))) +
plot_layout(guides = 'collect') & theme(
legend.position = 'bottom',
legend.direction = 'horizontal')
fig.roc
Time comparison
Finally, the code chunk below generates Supplementary Figure S12.
dt.dte <- merge(x = dt.time[grepl("edgeR-SC$|edgeR.legacy-SC$|sleuth|Swish",Method) &
grepl("Salmon",Quantifier) &
Scenario == 'unbalanced' &
FC == 'fc2',c('LibsPerGroup','Method','Quantifier','Time')],
y = dt.roc[grepl("edgeR-SC$|edgeR.legacy-SC$|sleuth|Swish",Method) &
grepl("Salmon",Quantifier) &
Scenario == 'unbalanced' &
FC == 'fc2' &
nFDR == 0.05,c('LibsPerGroup','Method','Quantifier','oTPR','oFDR')],
by = c('LibsPerGroup','Method','Quantifier'),
all.x = TRUE)
dt.dte[,Time := 60*Time]
dt.dte$LibsPerGroup %<>% factor(levels = paste0('#Lib/Group = ',c(3,5,10,100)),
labels = c('Three','Five','Ten','Hundred'))
dt.dte$Method %<>% factor(levels = c('edgeR.legacy-SC','edgeR-SC','sleuth-LRT','sleuth-Wald','Swish'),
labels = c('edgeR-v3','edgeR-v4','sleuth-LRT','sleuth-Wald','Swish'))
dt.dte$Quantifier %<>% factor(levels = c('Salmon-Boot','Salmon-Gibbs'),
labels = c('Bootstrap','Gibbs'))
fig.time <- ggplot(data = dt.dte,aes(x = Method,y = Time)) +
facet_wrap(facets = 'Quantifier',nrow = 1) +
geom_col() +
coord_cartesian(ylim = c(0,270)) +
scale_y_continuous(breaks = seq(0,300,60)) +
theme_bw(base_size = bs,base_family = 'sans') +
theme(panel.grid = element_blank(),
axis.text.x = element_text(angle = 90),
axis.text = element_text(colour = 'black',size = bs),
strip.text = element_text(size = bs)) +
labs(x = NULL,y = "Time (sec)")
fig.time
Output files
In the code chunk below I save the figures in PNG format for the supplement.
dsg.area <- c(area(1,1),area(1,2),
area(2,1,2,2),
area(3,1,3,2),
area(4,1,4,2))
fig.metrics <- wrap_plots(A = wrap_elements(fig.bcv.raw),
B = wrap_elements(fig.bcv.scaled),
C = wrap_elements(fig.power),
D = wrap_elements(fig.power.1pct),
E = wrap_elements(fig.roc),
design = dsg.area,
heights = c(2.75,1.625,1.625,3)/9) +
plot_annotation(tag_levels = 'a',
theme = theme(plot.tag = element_text(size = bs)))
fig.metrics
ggsave(plot = fig.metrics,filename = file.path(path.misc,'SuppFigure-LargeN.png'),
device = 'png',width = 7,height = 9,units = 'in',dpi = 300)
ggsave(plot = fig.time,filename = file.path(path.misc,'SuppFigure-LargeN-Time.png'),
device = 'png',width = 6,height = 3,units = 'in',dpi = 300)
sessionInfo()R version 4.3.3 (2024-02-29)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Red Hat Enterprise Linux 9.4 (Plow)
Matrix products: default
BLAS: /stornext/System/data/software/rhel/9/base/tools/R/4.3.3/lib64/R/lib/libRblas.so
LAPACK: /stornext/System/data/software/rhel/9/base/tools/R/4.3.3/lib64/R/lib/libRlapack.so; LAPACK version 3.11.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
time zone: Australia/Melbourne
tzcode source: system (glibc)
attached base packages:
[1] stats graphics grDevices datasets utils methods base
other attached packages:
[1] pkg_1.0 kableExtra_1.4.0 png_0.1-8 edgeR_4.0.16
[5] limma_3.58.1 plyr_1.8.9 patchwork_1.3.0 ggplot2_3.5.1
[9] magrittr_2.0.3 data.table_1.16.0 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] splines_4.3.3 later_1.3.2
[3] BiocIO_1.12.0 bitops_1.0-8
[5] filelock_1.0.3 R.oo_1.26.0
[7] tibble_3.2.1 XML_3.99-0.17
[9] lifecycle_1.0.4 rprojroot_2.0.4
[11] vroom_1.6.5 processx_3.8.4
[13] lattice_0.22-5 ensembldb_2.26.0
[15] sass_0.4.9 rmarkdown_2.28
[17] jquerylib_0.1.4 yaml_2.3.10
[19] remotes_2.5.0 httpuv_1.6.15
[21] sessioninfo_1.2.2 pkgbuild_1.4.4
[23] DBI_1.2.3 abind_1.4-8
[25] pkgload_1.4.0 zlibbioc_1.48.2
[27] GenomicRanges_1.54.1 R.utils_2.12.3
[29] purrr_1.0.2 AnnotationFilter_1.26.0
[31] BiocGenerics_0.48.1 RCurl_1.98-1.16
[33] rappdirs_0.3.3 git2r_0.33.0
[35] GenomeInfoDbData_1.2.11 wasabi_1.0.1
[37] IRanges_2.36.0 S4Vectors_0.40.2
[39] fishpond_2.8.0 svglite_2.1.3
[41] codetools_0.2-19 DelayedArray_0.28.0
[43] xml2_1.3.6 tidyselect_1.2.1
[45] farver_2.1.2 matrixStats_1.4.1
[47] stats4_4.3.3 BiocFileCache_2.10.2
[49] GenomicAlignments_1.38.2 jsonlite_1.8.9
[51] ellipsis_0.3.2 systemfonts_1.1.0
[53] tools_4.3.3 progress_1.2.3
[55] ragg_1.3.3 Rcpp_1.0.13
[57] glue_1.8.0 svMisc_1.2.3
[59] SparseArray_1.2.4 xfun_0.47
[61] MatrixGenerics_1.14.0 usethis_3.0.0
[63] GenomeInfoDb_1.38.8 dplyr_1.1.4
[65] withr_3.0.1 BiocManager_1.30.25
[67] fastmap_1.2.0 rhdf5filters_1.14.1
[69] fansi_1.0.6 callr_3.7.6
[71] digest_0.6.37 gridGraphics_0.5-1
[73] R6_2.5.1 mime_0.12
[75] textshaping_0.4.0 colorspace_2.1-1
[77] gtools_3.9.5 biomaRt_2.58.2
[79] RSQLite_2.3.7 R.methodsS3_1.8.2
[81] utf8_1.2.4 generics_0.1.3
[83] renv_1.0.9 tximeta_1.20.3
[85] rtracklayer_1.62.0 prettyunits_1.2.0
[87] httr_1.4.7 htmlwidgets_1.6.4
[89] S4Arrays_1.2.1 whisker_0.4.1
[91] pkgconfig_2.0.3 gtable_0.3.5
[93] blob_1.2.4 SingleCellExperiment_1.24.0
[95] XVector_0.42.0 htmltools_0.5.8.1
[97] profvis_0.4.0 ProtGenerics_1.34.0
[99] sleuth_0.30.1 scales_1.3.0
[101] Biobase_2.62.0 Rsubread_2.16.1
[103] knitr_1.48 rstudioapi_0.16.0
[105] tzdb_0.4.0 rjson_0.2.23
[107] curl_5.2.3 cachem_1.1.0
[109] rhdf5_2.46.1 stringr_1.5.1
[111] BiocVersion_3.18.1 parallel_4.3.3
[113] miniUI_0.1.1.1 AnnotationDbi_1.64.1
[115] restfulr_0.0.15 desc_1.4.3
[117] pillar_1.9.0 grid_4.3.3
[119] vctrs_0.6.5 urlchecker_1.0.1
[121] promises_1.3.0 dbplyr_2.5.0
[123] xtable_1.8-4 tximport_1.30.0
[125] evaluate_1.0.0 readr_2.1.5
[127] GenomicFeatures_1.54.4 cli_3.6.3
[129] locfit_1.5-9.10 compiler_4.3.3
[131] Rsamtools_2.18.0 rlang_1.1.4
[133] crayon_1.5.3 labeling_0.4.3
[135] ps_1.8.0 getPass_0.2-4
[137] fs_1.6.4 stringi_1.8.4
[139] viridisLite_0.4.2 BiocParallel_1.36.0
[141] munsell_0.5.1 Biostrings_2.70.3
[143] lazyeval_0.2.2 devtools_2.4.5
[145] Matrix_1.6-5 hms_1.1.3
[147] bit64_4.5.2 Rhdf5lib_1.24.2
[149] KEGGREST_1.42.0 statmod_1.5.0
[151] shiny_1.9.1 highr_0.11
[153] SummarizedExperiment_1.32.0 interactiveDisplayBase_1.40.0
[155] AnnotationHub_3.10.1 memoise_2.0.1
[157] thematic_0.1.6 bslib_0.8.0
[159] bit_4.5.0