Simulation results from the supplement I (small-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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Unstaged changes:
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
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the repository in which changes were
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 529139f | Pedro Baldoni | 2024-10-09 | NARGAB Revision from October 2024 |
| html | 0f05c72 | Pedro Baldoni | 2024-06-24 | Build site. |
| Rmd | 5b41ed5 | Pedro Baldoni | 2024-06-24 | Printing out tables |
| html | 70ab63f | Pedro Baldoni | 2024-04-05 | Publishing workflowr |
| Rmd | bebf946 | Pedro Baldoni | 2024-04-05 | Adding simulation Rmds |
Introduction
This Rmarkdown generates Supplementary Figures S4–S9 and Supplementary Table S1 from our Gibbs DTE paper. The results are based on the simulation designed to emulate small-scale experiments with low biological variation and sample size, a typical scenario of RNA-seq studies with genetically modified organisms or cell lines.
Setup
Below we set options for this Rmarkdown report and load the necessary libraries.
knitr::opts_chunk$set(dev = "png",
dpi = 300,
dev.args = list(type = "cairo-png"),
root.dir = '.',
autodep = TRUE)
options(knitr.kable.NA = "-")
bs <- 8library(data.table)
library(magrittr)
library(ggplot2)
library(patchwork)
library(plyr)
library(kableExtra)
devtools::load_all('../code/pkg')Next we set the paths to our simulation results.
path.misc <- file.path('../misc/simulation-supp.Rmd')
dir.create(path.misc,recursive = TRUE,showWarnings = FALSE)
path.example <- "../output/simulation/data/mm39/readlen-100/fc2/paired-end/9999TxPerGene/unbalanced/5libsPerGroup"
path.fdr <-
list.files('../output/simulation/summary','fdr.tsv.gz',recursive = TRUE,full.names = TRUE)
path.metrics <-
list.files('../output/simulation/summary','metrics.tsv.gz',recursive = TRUE,full.names = TRUE)
path.time <-
list.files('../output/simulation/summary','^time.tsv.gz',recursive = TRUE,full.names = TRUE)
path.quanttime <-
list.files('../output/simulation/summary','quanttime.tsv.gz',recursive = TRUE,full.names = TRUE)
path.quantile <-
list.files('../output/simulation/summary','quantile.tsv.gz',recursive = TRUE,full.names = TRUE)
path.pvalue <-
list.files('../output/simulation/summary','pvalue.tsv.gz',recursive = TRUE,full.names = TRUE)
path.overdispersion <-
list.files('../output/simulation/summary','overdispersion.tsv.gz',recursive = TRUE,full.names = TRUE)
path.roc <-
list.files('../output/simulation/summary','roc.tsv.gz',recursive = TRUE,full.names = TRUE)Data loading
Below we load the actual results and relabel some of the factor variables.
relabel <- function(x){
y <- copy(x)
y$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$LibsPerGroup %<>% factor(levels = paste0(c(3, 5, 10), 'libsPerGroup'),labels = paste0('#Lib/Group = ', c(3, 5, 10))) %<>% droplevels()
y$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$Sample %<>% factor(levels = c(paste0("groupA_rep",1:10,"_R1"),paste0("groupB_rep",1:10,"_R1")),labels = c(paste0("A",1:10),paste0("B",1:10))) %<>% droplevels()
}
return(y)
}
# Loading datasets
dt.fdr <- do.call(rbind,lapply(path.fdr,fread)) %>% relabel()
dt.metrics <- do.call(rbind,lapply(path.metrics,fread)) %>% relabel()
dt.time <- do.call(rbind,lapply(path.time,fread)) %>% relabel()
dt.quanttime <- do.call(rbind,lapply(path.quanttime,fread)) %>% relabel()
dt.quantile <- do.call(rbind,lapply(path.quantile,fread)) %>% relabel()
dt.pvalue <- do.call(rbind,lapply(path.pvalue,fread)) %>% relabel()
dt.overdispersion <- do.call(rbind,lapply(path.overdispersion,fread)) %>% relabel()
dt.roc <- do.call(rbind,lapply(path.roc,fread)) %>% relabel()Power
Below I compute the barplots of Supplementary Figures S4–S8.
dt.power <- dt.metrics[FC == 'fc2' &
!grepl("\\.n|RC",Method) &
Scenario == 'unbalanced',]
dt.power$LibsPerGroup %<>% factor(levels = paste0('#Lib/Group = ', c(3, 5,10)),labels = paste0(c(3,5,10),' 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('kallisto','Salmon-Boot','Salmon-Gibbs'),
labels = c('Bootstrap\nkallisto','Bootstrap\nSalmon','Gibbs\nSalmon'))
dt.power$Method %<>% mapvalues(from = c('edgeR-SC','edgeR.legacy-SC'),to = c('edgeR-v4','edgeR-v3'))
dt.power[, FDR := roundPretty(ifelse((FP+TP) == 0,NA,100*FP/(FP+TP)),1)]
plot.power <- function(df.bar,df.txt,scenario,library,legend = FALSE, base_size = bs,...){
tb.bar <- df.bar[Scenario == scenario & LibsPerGroup == library,]
tb.txt <- df.txt[Scenario == scenario & LibsPerGroup == library,][FDR != 'NA',]
gap <- 0.05*max(dt.power$TP + dt.power$FP)
ggplot(tb.bar,aes(x = Quantifier,y = Value,fill = Type)) +
facet_wrap(facets = "Method",nrow = 1) +
geom_col() +
geom_text(aes(x = Quantifier,y = (TP + FP) + gap,label = FDR),
vjust = 0,data = tb.txt,size = base_size/.pt,inherit.aes = FALSE) +
scale_fill_manual(values = c('#ff0000','#bebebe')) +
labs(x = NULL,y = paste('DE Transcripts')) +
coord_cartesian(...) +
theme_bw(base_size = base_size,base_family = 'sans') +
theme(panel.grid = element_blank(),
# axis.text.x = element_text(angle = 90),
axis.text = element_text(colour = 'black',size = base_size),
strip.text = element_text(size = base_size)) +
if (legend == TRUE) theme(legend.background = element_rect(fill = alpha('white', 0)),
legend.text = element_text(size = base_size),
legend.position = 'inside',
legend.position.inside = c(0.95,0.825),
legend.title = element_blank(),
legend.key.size = unit(0.75,"line")) else theme(legend.position = 'none')
}
make.plot.power <- function(x,scenario,library,quantifier,...){
x.melt <- melt(x,id.vars = colnames(x)[-which(colnames(x) %in% c('P.SIG','TP','FP'))],
measure.vars = c('TP','FP'),
variable.name = 'Type',
value.name = 'Value')
x.melt <- x.melt[Quantifier %in% quantifier,]
y <- copy(x[Quantifier %in% quantifier,])
x.melt$Quantifier <- droplevels(x.melt$Quantifier)
y$Quantifier <- droplevels(y$Quantifier)
x.melt$Type <-
factor(x.melt$Type,
levels = c('FP','TP'),
labels = c('False','True'))
plot.power(df.bar = x.melt,
df.txt = y,
scenario = scenario,
library = library,
legend = TRUE,
...)
}
fig.power.salmon.3 <- make.plot.power(x = dt.power,
scenario = 'Unequal library sizes',
library = '3 samples per group',
quantifier = c('Bootstrap\nSalmon',
'Gibbs\nSalmon'),
ylim = c(0,3250))
fig.power.salmon.10 <- make.plot.power(x = dt.power,
scenario = 'Unequal library sizes',
library = '10 samples per group',
quantifier = c('Bootstrap\nSalmon',
'Gibbs\nSalmon'),
ylim = c(0,5000))
fig.power.kallisto.3 <- make.plot.power(x = dt.power,
scenario = 'Unequal library sizes',
library = '3 samples per group',
quantifier = c('Bootstrap\nkallisto',
'Gibbs\nSalmon'),
ylim = c(0,3250))
fig.power.kallisto.5 <- make.plot.power(x = dt.power,
scenario = 'Unequal library sizes',
library = '5 samples per group',
quantifier = c('Bootstrap\nkallisto',
'Gibbs\nSalmon'),
ylim = c(0,3250))
fig.power.kallisto.10 <- make.plot.power(x = dt.power,
scenario = 'Unequal library sizes',
library = '10 samples per group',
quantifier = c('Bootstrap\nkallisto',
'Gibbs\nSalmon'),
ylim = c(0,5000))
fig.power.salmon.3
| Version | Author | Date |
|---|---|---|
| 70ab63f | Pedro Baldoni | 2024-04-05 |
fig.power.salmon.10
| Version | Author | Date |
|---|---|---|
| 70ab63f | Pedro Baldoni | 2024-04-05 |
fig.power.kallisto.3
| Version | Author | Date |
|---|---|---|
| 70ab63f | Pedro Baldoni | 2024-04-05 |
fig.power.kallisto.5
| Version | Author | Date |
|---|---|---|
| 70ab63f | Pedro Baldoni | 2024-04-05 |
fig.power.kallisto.10
| Version | Author | Date |
|---|---|---|
| 70ab63f | Pedro Baldoni | 2024-04-05 |
FDR
Below I compute the false discovery curves of Supplementary Figures S4–S8.
dt.fdr.plot <- dt.fdr[FC == 'fc2' &
!grepl("\\.n|RC",Method) &
Scenario == 'unbalanced',]
colmeth <- methodsNames()$color
names(colmeth) <- gsub('^edgeR-SC$','edgeR-v4',names(colmeth))
names(colmeth) <- gsub('^edgeR.legacy-SC$','edgeR-v3',names(colmeth))
dt.fdr.plot$LibsPerGroup %<>% factor(levels = paste0('#Lib/Group = ', c(3, 5,10)),labels = paste0(c(3,5,10),' samples per group'))
dt.fdr.plot$Scenario %<>% factor(levels = c('balanced','unbalanced'),labels = c('Equal library sizes','Unequal library sizes'))
dt.fdr.plot$Quantifier %<>%
factor(levels = c('Salmon-Gibbs','kallisto','Salmon-Boot'),
labels = c('Gibbs\nSalmon','Bootstrap\nkallisto','Bootstrap\nSalmon'))
dt.fdr.plot$Method %<>% mapvalues(from = c('edgeR-SC','edgeR.legacy-SC'),to = c('edgeR-v4','edgeR-v3'))
dt.fdr.plot$Method %<>% factor(levels = c('Swish',
'sleuth-Wald',
'sleuth-LRT',
'edgeR-v3',
'edgeR-v4'))
plot.fdr <- function(df.line,scenario,library,legend = FALSE,base_size = bs,color = colmeth,...){
tb.bar <- df.line[Scenario == scenario & LibsPerGroup == library,]
ggplot(tb.bar,aes(x = N,y = FDR,color = Method,linetype = Quantifier)) +
geom_line(linewidth = 0.7) +
scale_color_manual(values = color,
breaks = c('edgeR-v3',
'edgeR-v4',
'sleuth-LRT',
'sleuth-Wald',
'Swish')) +
coord_cartesian(...) +
labs(y = 'False discoveries',x = 'Transcripts chosen') +
theme_bw(base_size = base_size,base_family = 'sans') +
theme(panel.grid = element_blank(),
axis.text = element_text(colour = 'black',size = base_size)) +
if (legend == TRUE) theme(legend.background = element_rect(fill = alpha('white', 0)),
legend.direction = 'vertical',
legend.box = 'horizontal',
legend.position = c(0.2,0.9),
legend.text = element_text(size = base_size),
legend.title = element_blank(),
legend.key.size = unit(0.75,"line")) else theme(legend.position = 'none')
}
fig.fdr.salmon.3 <- plot.fdr(df.line = dt.fdr.plot[Quantifier %in% c('Bootstrap\nSalmon','Gibbs\nSalmon'),],
scenario = 'Unequal library sizes',
library = '3 samples per group',
ylim = c(0,750),
xlim = c(0,2500),
legend = TRUE)Warning: A numeric `legend.position` argument in `theme()` was deprecated in ggplot2
3.5.0.
ℹ Please use the `legend.position.inside` argument of `theme()` instead.
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
generated.fig.fdr.salmon.10 <- plot.fdr(df.line = dt.fdr.plot[Quantifier %in% c('Bootstrap\nSalmon','Gibbs\nSalmon'),],
scenario = 'Unequal library sizes',
library = '10 samples per group',
ylim = c(0,300),
xlim = c(1000,3000),
legend = TRUE)
fig.fdr.kallisto.3 <- plot.fdr(df.line = dt.fdr.plot[Quantifier %in% c('Bootstrap\nkallisto','Gibbs\nSalmon'),],
scenario = 'Unequal library sizes',
library = '3 samples per group',
ylim = c(0,750),
xlim = c(0,2500),
legend = TRUE)
fig.fdr.kallisto.5 <- plot.fdr(df.line = dt.fdr.plot[Quantifier %in% c('Bootstrap\nkallisto','Gibbs\nSalmon'),],
scenario = 'Unequal library sizes',
library = '5 samples per group',
ylim = c(0,400),
xlim = c(0,2500),
legend = TRUE)
fig.fdr.kallisto.10 <- plot.fdr(df.line = dt.fdr.plot[Quantifier %in% c('Bootstrap\nkallisto','Gibbs\nSalmon'),],
scenario = 'Unequal library sizes',
library = '10 samples per group',
ylim = c(0,300),
xlim = c(1000,3000),
legend = TRUE)
fig.fdr.salmon.3
| Version | Author | Date |
|---|---|---|
| 70ab63f | Pedro Baldoni | 2024-04-05 |
fig.fdr.salmon.10
| Version | Author | Date |
|---|---|---|
| 70ab63f | Pedro Baldoni | 2024-04-05 |
fig.fdr.kallisto.3
| Version | Author | Date |
|---|---|---|
| 70ab63f | Pedro Baldoni | 2024-04-05 |
fig.fdr.kallisto.5
| Version | Author | Date |
|---|---|---|
| 70ab63f | Pedro Baldoni | 2024-04-05 |
fig.fdr.kallisto.10
| Version | Author | Date |
|---|---|---|
| 70ab63f | Pedro Baldoni | 2024-04-05 |
ROC curves
Below is the code chunk used to generate Supplementary Figure S9.
dt.rcurve <- dt.roc[FC == 'fc2' &
grepl("edgeR-SC|edgeR-SC\\.n",Method) &
Scenario == 'unbalanced',]
dt.rcurve$LibsPerGroup %<>% factor(levels = paste0('#Lib/Group = ', c(3, 5, 10)),labels = paste0(c('Three','Five', 'Ten'),' 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('kallisto','Salmon-Boot','Salmon-Gibbs'),labels = c('Bootstrap (kallisto)','Bootstrap (Salmon)','Gibbs (Salmon)'))
dt.rcurve$Method %<>%
factor(levels = c(paste0('edgeR-SC.n',seq(10,90,10)),'edgeR-SC')) %<>%
mapvalues(from = c(paste0('edgeR-SC.n',seq(10,90,10)),'edgeR-SC'),to = paste0(seq(10,100,10)))
fig.roc <- ggplot(data = dt.rcurve,aes(x = oFDR,y = oTPR,group = Method,color = Method)) +
facet_grid(rows = vars(LibsPerGroup),cols = vars(Quantifier)) +
geom_vline(xintercept = c(0.01,0.05,0.1,0.15,0.2),linetype = 'dashed',linewidth = 0.25,color = 'gray') +
geom_hline(yintercept = c(0,0.25,0.5,0.75,1),linetype = 'dashed',linewidth = 0.25,color = 'gray') +
geom_line(linewidth = 0.5) +
geom_point(aes(shape = as.factor(nFDR)),size = 3) +
scale_y_continuous(limits = c(0,1)) +
scale_x_continuous(limits = c(0,0.2)) +
scale_color_viridis_d(direction = -1) +
theme_bw(base_size = bs,base_family = 'sans') +
theme(panel.grid = element_blank(),
legend.text = element_text(colour = 'black',size = bs),
axis.text = element_text(colour = 'black',size = bs),
strip.text = element_text(colour = 'black',size = bs)) +
labs(x = 'Observed FDR',y = 'Observed power',shape = 'Nominal FDR',color = 'Resamples')
fig.roc
| Version | Author | Date |
|---|---|---|
| 70ab63f | Pedro Baldoni | 2024-04-05 |
Quantification time
Below I generate Supplementary Table S1.
dt.salmontime <- dt.quanttime[,
.(TotalTime = mean(Time)/60,
ResamplingTime = mean(ResamplingTime)/60),
by = c('LibSize','Quantifier')]
dt.salmontime$QuantTime <- dt.salmontime$TotalTime - dt.salmontime$ResamplingTime
setcolorder(dt.salmontime,c('Quantifier','LibSize','QuantTime','ResamplingTime','TotalTime'))
dt.salmontime$LibSize %<>% factor(levels = c('2.5e+07','5e+07','1e+08'),
labels = c('25','50','100'))
dt.salmontime$Quantifier %<>% factor(levels = c('kallisto','Salmon-Boot','Salmon-Gibbs'),
labels = c('Bootstrap (kallisto)','Bootstrap (Salmon)','Gibbs (Salmon)'))
dt.salmontime <- dt.salmontime[order(Quantifier,LibSize),]
cnames <- c('Resampling\nMethod',
'Library Size\n(million read pairs)',
'Optimization',
'Resampling',
'Total')
tb.salmontime <- kbl(x = dt.salmontime,
format = 'latex',
digits = 2,
col.names = linebreak(cnames,align = 'c'),
align = 'ccccc',
escape = FALSE,
booktabs = TRUE) %>%
add_header_above(c(" " = 2, "Quantification Time (minutes)" = 3)) %>%
collapse_rows(1, latex_hline = 'major')
kbl(x = dt.salmontime,
format = 'pipe',
digits = 2,
col.names = linebreak(cnames,align = 'c'),
align = 'ccccc',
escape = FALSE,
booktabs = TRUE) %>%
add_header_above(c(" " = 2, "Quantification Time (minutes)" = 3)) %>%
collapse_rows(1, latex_hline = 'major')| Optimization | Resampling | Total | ||
|---|---|---|---|---|
| Bootstrap (kallisto) | 25 |
|
|
4.92 |
| 50 |
|
|
7.32 | |
| 100 |
|
|
11.71 | |
| Bootstrap (Salmon) | 25 | 1.79 | 9.45 | 11.24 |
| 50 | 3.22 | 13.41 | 16.64 | |
| 100 | 6.09 | 19.15 | 25.24 | |
| Gibbs (Salmon) | 25 | 1.78 | 0.91 | 2.69 |
| 50 | 3.22 | 1.62 | 4.85 | |
| 100 | 6.05 | 3.01 | 9.06 |
Output files
And finally all files are exported to be incorporated in the supplement.
fig.metrics.salmon.3 <- wrap_plots(A = fig.power.salmon.3,
B = fig.fdr.salmon.3,
design = c(area(1,1),area(2,1)),
heights = c(0.25,0.75)) +
plot_annotation(tag_levels = 'a',
theme = theme(plot.tag = element_text(size = bs)))
fig.metrics.salmon.10 <- wrap_plots(A = fig.power.salmon.10,
B = fig.fdr.salmon.10,
design = c(area(1,1),area(2,1)),
heights = c(0.25,0.75)) +
plot_annotation(tag_levels = 'a',
theme = theme(plot.tag = element_text(size = bs)))
fig.metrics.kallisto.3 <- wrap_plots(A = fig.power.kallisto.3,
B = fig.fdr.kallisto.3,
design = c(area(1,1),area(2,1)),
heights = c(0.25,0.75)) +
plot_annotation(tag_levels = 'a',
theme = theme(plot.tag = element_text(size = bs)))
fig.metrics.kallisto.5 <- wrap_plots(A = fig.power.kallisto.5,
B = fig.fdr.kallisto.5,
design = c(area(1,1),area(2,1)),
heights = c(0.25,0.75)) +
plot_annotation(tag_levels = 'a',
theme = theme(plot.tag = element_text(size = bs)))
fig.metrics.kallisto.10 <- wrap_plots(A = fig.power.kallisto.10,
B = fig.fdr.kallisto.10,
design = c(area(1,1),area(2,1)),
heights = c(0.25,0.75)) +
plot_annotation(tag_levels = 'a',
theme = theme(plot.tag = element_text(size = bs)))
ggsave(plot = fig.metrics.salmon.3,filename = file.path(path.misc,'SuppFigure-Metrics-Salmon-3.pdf'),
device = 'pdf',width = 6,height = 8,units = 'in',dpi = 300)
ggsave(plot = fig.metrics.salmon.10,filename = file.path(path.misc,'SuppFigure-Metrics-Salmon-10.pdf'),
device = 'pdf',width = 6,height = 8,units = 'in',dpi = 300)
ggsave(plot = fig.metrics.kallisto.3,filename = file.path(path.misc,'SuppFigure-Metrics-kallisto-3.pdf'),
device = 'pdf',width = 6,height = 8,units = 'in',dpi = 300)
ggsave(plot = fig.metrics.kallisto.5,filename = file.path(path.misc,'SuppFigure-Metrics-kallisto-5.pdf'),
device = 'pdf',width = 6,height = 8,units = 'in',dpi = 300)
ggsave(plot = fig.metrics.kallisto.10,filename = file.path(path.misc,'SuppFigure-Metrics-kallisto-10.pdf'),
device = 'pdf',width = 6,height = 8,units = 'in',dpi = 300)
ggsave(plot = fig.roc,filename = file.path(path.misc,'SuppFigure-ROC.pdf'),
device = 'pdf',width = 7,height = 5,units = 'in',dpi = 300)
save_kable(tb.salmontime,file = file.path(path.misc,'SuppTable-QuantTime.tex'))
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 plyr_1.8.9 patchwork_1.3.0
[5] ggplot2_3.5.1 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 edgeR_4.0.16
[11] rprojroot_2.0.4 processx_3.8.4
[13] lattice_0.22-5 ensembldb_2.26.0
[15] limma_3.58.1 sass_0.4.9
[17] rmarkdown_2.28 jquerylib_0.1.4
[19] yaml_2.3.10 remotes_2.5.0
[21] httpuv_1.6.15 sessioninfo_1.2.2
[23] pkgbuild_1.4.4 DBI_1.2.3
[25] abind_1.4-8 pkgload_1.4.0
[27] zlibbioc_1.48.2 GenomicRanges_1.54.1
[29] R.utils_2.12.3 purrr_1.0.2
[31] AnnotationFilter_1.26.0 BiocGenerics_0.48.1
[33] RCurl_1.98-1.16 rappdirs_0.3.3
[35] git2r_0.33.0 GenomeInfoDbData_1.2.11
[37] wasabi_1.0.1 IRanges_2.36.0
[39] S4Vectors_0.40.2 fishpond_2.8.0
[41] svglite_2.1.3 codetools_0.2-19
[43] DelayedArray_0.28.0 xml2_1.3.6
[45] tidyselect_1.2.1 farver_2.1.2
[47] matrixStats_1.4.1 stats4_4.3.3
[49] BiocFileCache_2.10.2 GenomicAlignments_1.38.2
[51] jsonlite_1.8.9 ellipsis_0.3.2
[53] systemfonts_1.1.0 tools_4.3.3
[55] progress_1.2.3 ragg_1.3.3
[57] Rcpp_1.0.13 glue_1.8.0
[59] svMisc_1.2.3 SparseArray_1.2.4
[61] xfun_0.47 MatrixGenerics_1.14.0
[63] usethis_3.0.0 GenomeInfoDb_1.38.8
[65] dplyr_1.1.4 withr_3.0.1
[67] BiocManager_1.30.25 fastmap_1.2.0
[69] rhdf5filters_1.14.1 fansi_1.0.6
[71] callr_3.7.6 digest_0.6.37
[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] png_0.1-8 knitr_1.48
[105] rstudioapi_0.16.0 tzdb_0.4.0
[107] rjson_0.2.23 curl_5.2.3
[109] cachem_1.1.0 rhdf5_2.46.1
[111] stringr_1.5.1 BiocVersion_3.18.1
[113] parallel_4.3.3 miniUI_0.1.1.1
[115] AnnotationDbi_1.64.1 restfulr_0.0.15
[117] desc_1.4.3 pillar_1.9.0
[119] grid_4.3.3 vctrs_0.6.5
[121] urlchecker_1.0.1 promises_1.3.0
[123] dbplyr_2.5.0 xtable_1.8-4
[125] tximport_1.30.0 evaluate_1.0.0
[127] readr_2.1.5 GenomicFeatures_1.54.4
[129] locfit_1.5-9.10 cli_3.6.3
[131] compiler_4.3.3 Rsamtools_2.18.0
[133] rlang_1.1.4 crayon_1.5.3
[135] labeling_0.4.3 ps_1.8.0
[137] getPass_0.2-4 fs_1.6.4
[139] stringi_1.8.4 viridisLite_0.4.2
[141] BiocParallel_1.36.0 munsell_0.5.1
[143] Biostrings_2.70.3 lazyeval_0.2.2
[145] devtools_2.4.5 Matrix_1.6-5
[147] hms_1.1.3 bit64_4.5.2
[149] Rhdf5lib_1.24.2 KEGGREST_1.42.0
[151] statmod_1.5.0 shiny_1.9.1
[153] highr_0.11 SummarizedExperiment_1.32.0
[155] interactiveDisplayBase_1.40.0 AnnotationHub_3.10.1
[157] memoise_2.0.1 thematic_0.1.6
[159] bslib_0.8.0 bit_4.5.0