Simulation study
Pedro L. Baldoni
24 June, 2024
Last updated: 2024-06-24
Checks: 7 0
Knit directory: GibbsDTE-code/analysis/
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Unstaged changes:
Modified: analysis/lung.Rmd
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
made to the R Markdown (analysis/simulation-paper.Rmd) and
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click on the hyperlinks in the table below to view the files as they
were in that past version.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| 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
Setup
knitr::opts_chunk$set(dev = "png",
dpi = 300,
dev.args = list(type = "cairo-png"),
root.dir = '.',
autodep = TRUE)
options(knitr.kable.NA = "-")library(data.table)
library(magrittr)
library(ggplot2)
library(patchwork)
library(plyr)
library(kableExtra)
devtools::load_all('../code/pkg')path.misc <- file.path('../misc/simulation.Rmd')
dir.create(path.misc,recursive = TRUE,showWarnings = FALSE)
path.example <- "../output/simulation/data/mm39/readlen-100/fc2/paired-end/9999TxPerGene/unbalanced/5libsPerGroup"
path.fdr <-
list.files('../output/simulation/summary','fdr.tsv.gz',recursive = TRUE,full.names = TRUE)
path.metrics <-
list.files('../output/simulation/summary','metrics.tsv.gz',recursive = TRUE,full.names = TRUE)
path.time <-
list.files('../output/simulation/summary','^time.tsv.gz',recursive = TRUE,full.names = TRUE)
path.quanttime <-
list.files('../output/simulation/summary','quanttime.tsv.gz',recursive = TRUE,full.names = TRUE)
path.quantile <-
list.files('../output/simulation/summary','quantile.tsv.gz',recursive = TRUE,full.names = TRUE)
path.pvalue <-
list.files('../output/simulation/summary','pvalue.tsv.gz',recursive = TRUE,full.names = TRUE)
path.overdispersion <-
list.files('../output/simulation/summary','overdispersion.tsv.gz',recursive = TRUE,full.names = TRUE)
path.roc <-
list.files('../output/simulation/summary','roc.tsv.gz',recursive = TRUE,full.names = TRUE)bs <- 8Data loading
relabel <- function(x){
y <- copy(x)
y$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$Sample %<>% factor(levels = c(paste0("groupA_rep",1:10,"_R1"),paste0("groupB_rep",1:10,"_R1")),labels = c(paste0("A",1:10),paste0("B",1:10))) %<>% droplevels()
}
return(y)
}
# Loading datasets
dt.fdr <- do.call(rbind,lapply(path.fdr,fread)) %>% relabel()
dt.metrics <- do.call(rbind,lapply(path.metrics,fread)) %>% relabel()
dt.time <- do.call(rbind,lapply(path.time,fread)) %>% relabel()
dt.quanttime <- do.call(rbind,lapply(path.quanttime,fread)) %>% relabel()
dt.quantile <- do.call(rbind,lapply(path.quantile,fread)) %>% relabel()
dt.pvalue <- do.call(rbind,lapply(path.pvalue,fread)) %>% relabel()
dt.overdispersion <- do.call(rbind,lapply(path.overdispersion,fread)) %>% relabel()
dt.roc <- do.call(rbind,lapply(path.roc,fread)) %>% relabel()Power
dt.power <- dt.metrics[FC == 'fc2' &
Quantifier != 'kallisto' &
!grepl("\\.n|RC",Method) &
Scenario == 'unbalanced',]
dt.power$LibsPerGroup %<>% factor(levels = paste0('#Lib/Group = ', c(3, 5,10)),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('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$FP)
ggplot(tb.bar,aes(x = Quantifier,y = Value,fill = Type)) +
facet_wrap(facets = "Method",nrow = 1) +
geom_col() +
geom_text(aes(x = Quantifier,y = (TP + FP) + gap,label = FDR),
vjust = 0,data = tb.txt,size = base_size/.pt,inherit.aes = FALSE) +
scale_fill_manual(values = c('#ff0000','#bebebe')) +
labs(x = NULL,y = paste('DE Transcripts')) +
scale_y_continuous(limits = c(0,3250)) +
theme_bw(base_size = base_size,base_family = 'sans') +
theme(panel.grid = element_blank(),
# axis.text.x = element_text(angle = 90),
axis.text = element_text(colour = 'black',size = base_size),
strip.text = element_text(size = base_size)) +
if (legend == TRUE) theme(legend.background = element_rect(fill = alpha('white', 0)),
legend.text = element_text(size = base_size),
legend.position = c(0.95,0.825),
legend.direction = 'vertical',
legend.title = element_blank(),
legend.key.size = unit(0.75,"line")) else theme(legend.position = 'none')
}
fig.power <- plot.power(df.bar = x.melt,
df.txt = dt.power,
scenario = 'Unequal library sizes',
library = '5 samples per group',
legend = TRUE)Warning: A numeric `legend.position` argument in `theme()` was deprecated in ggplot2
3.5.0.
ℹ Please use the `legend.position.inside` argument of `theme()` instead.
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
generated.fig.power
| Version | Author | Date |
|---|---|---|
| 70ab63f | Pedro Baldoni | 2024-04-05 |
FDR
dt.fdr.plot <- dt.fdr[FC == 'fc2' &
Quantifier != 'kallisto' &
!grepl("\\.n|RC",Method) &
Scenario == 'unbalanced',]
colmeth <- methodsNames()$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$Quantifier %<>% factor(levels = c('Salmon-Boot','Salmon-Gibbs'),labels = c('Bootstrap','Gibbs')) %<>% factor(levels = c('Gibbs','Bootstrap'))
dt.fdr.plot$Method %<>% mapvalues(from = c('edgeR-SC','edgeR.legacy-SC'),to = c('edgeR-v4','edgeR-v3'))
dt.fdr.plot$Method %<>% factor(levels = c('Swish',
'sleuth-Wald',
'sleuth-LRT',
'edgeR-v3',
'edgeR-v4'))
plot.fdr <- function(df.line,scenario,library,legend = FALSE,base_size = bs,color = colmeth,
ylim = c(0,800),xlim = c(0,300),...){
tb.bar <- df.line[Scenario == scenario & LibsPerGroup == library,]
ggplot(tb.bar,aes(x = N,y = FDR,color = Method,linetype = Quantifier)) +
geom_line(linewidth = 0.7) +
scale_color_manual(values = color,
breaks = c('edgeR-v3',
'edgeR-v4',
'sleuth-LRT',
'sleuth-Wald',
'Swish')) +
coord_cartesian(xlim = xlim,ylim = ylim) +
labs(y = 'False discoveries',x = 'Transcripts chosen') +
theme_bw(base_size = base_size,base_family = 'sans') +
theme(panel.grid = element_blank(),
axis.text = element_text(colour = 'black',size = base_size)) +
if (legend == TRUE) theme(legend.background = element_rect(fill = alpha('white', 0)),
legend.direction = 'vertical',
legend.box = 'horizontal',
legend.position = c(0.4,0.8),
legend.text = element_text(size = base_size),
legend.title = element_blank(),
legend.key.size = unit(0.75,"line")) else theme(legend.position = 'none')
}
fig.fdr <- plot.fdr(df.line = dt.fdr.plot[N <= 2500,],
scenario = 'Unequal library sizes',
library = '5 samples per group',
ylim = c(0,310),
xlim = c(0,2500),
legend = TRUE)
fig.fdr
| Version | Author | Date |
|---|---|---|
| 70ab63f | Pedro Baldoni | 2024-04-05 |
ROC curves
dt.rcurve <- dt.roc[FC == 'fc2' &
grepl("edgeR-SC|edgeR-SC\\.n",Method) &
Scenario == 'unbalanced' &
Quantifier != 'kallisto',]
dt.rcurve$LibsPerGroup %<>% factor(levels = paste0('#Lib/Group = ', c(3, 5, 10)),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('Salmon-Boot','Salmon-Gibbs'),labels = c('Bootstrap','Gibbs'))
dt.rcurve$Method %<>%
factor(levels = c(paste0('edgeR-SC.n',seq(10,90,10)),'edgeR-SC')) %<>%
mapvalues(from = c(paste0('edgeR-SC.n',seq(10,90,10)),'edgeR-SC'),to = paste0(seq(10,100,10)))
fig.roc <- ggplot(data = dt.rcurve,aes(x = oFDR,y = oTPR,group = Method,color = Method)) +
facet_grid(rows = vars(LibsPerGroup),cols = vars(Quantifier)) +
geom_vline(xintercept = c(0.01,0.05,0.1,0.15,0.2),linetype = 'dashed',linewidth = 0.25,color = 'gray') +
geom_hline(yintercept = c(0,0.25,0.5,0.75,1),linetype = 'dashed',linewidth = 0.25,color = 'gray') +
geom_line(linewidth = 0.5) +
geom_point(aes(shape = as.factor(nFDR)),size = 3) +
scale_y_continuous(limits = c(0,1)) +
scale_x_continuous(limits = c(0,0.2)) +
scale_color_viridis_d(direction = -1) +
theme_bw(base_size = bs,base_family = 'sans') +
theme(panel.grid = element_blank(),
legend.text = element_text(colour = 'black',size = bs),
axis.text = element_text(colour = 'black',size = bs),
strip.text = element_text(colour = 'black',size = bs)) +
labs(x = 'Observed FDR',y = 'Observed power',shape = 'Nominal FDR',color = 'Resamples')
fig.roc
| Version | Author | Date |
|---|---|---|
| 70ab63f | Pedro Baldoni | 2024-04-05 |
Error rate
dt.type1error <- dt.metrics[FC == 'fc1' &
Quantifier == 'Salmon-Gibbs' &
!grepl("\\.n|RC",Method) &
Scenario == 'unbalanced',]
dt.type1error$LibsPerGroup %<>% factor(levels = paste0('#Lib/Group = ', c(3, 5,10)),labels = paste0(c(3,5,10),' samples per group'))
dt.type1error$Scenario %<>% factor(levels = c('balanced','unbalanced'),labels = c('Equal library sizes','Unequal library sizes'))
dt.type1error$Quantifier %<>% factor(levels = c('Salmon-Gibbs'),labels = c('Gibbs'))
dt.type1error$Method %<>% mapvalues(from = c('edgeR-SC','edgeR.legacy-SC','sleuth-LRT','sleuth-Wald','Swish'),to = c('edgeR\nv4','edgeR\nv3','sleuth\nLRT','sleuth\nWald','Swish'))
x.melt <-
melt(dt.type1error,
id.vars = colnames(dt.type1error)[-which(colnames(dt.type1error) %in% c('P.SIG','TP','FP'))],
measure.vars = c('P.SIG'),variable.name = 'Type',value.name = 'Value')
plot.type1error <- function(df.bar,scenario,library,legend = FALSE,base_size = bs){
tb.bar <- df.bar[Scenario == scenario & LibsPerGroup == library,]
ggplot(tb.bar,aes(x = Method,y = Value)) +
geom_col(fill = "#bebebe") +
geom_hline(yintercept = 0.05,color = '#ff0000',linetype = 'dashed',linewidth = 0.5) +
labs(x = NULL,y = paste('Type 1 error rate')) +
scale_y_continuous(limits = c(0,0.06),breaks = seq(0,0.06,0.01)) +
theme_bw(base_size = base_size,base_family = 'sans') +
theme(panel.grid = element_blank(),
axis.text.x = element_text(angle = 0),
axis.text = element_text(colour = 'black',size = base_size))
}
fig.type1error <- plot.type1error(df.bar = x.melt,
scenario = 'Unequal library sizes',
library = '5 samples per group')
fig.type1error
| Version | Author | Date |
|---|---|---|
| 70ab63f | Pedro Baldoni | 2024-04-05 |
Quantification time
dt.salmontime <- dt.quanttime[Quantifier != 'kallisto',
.(TotalTime = mean(Time)/60,
ResamplingTime = mean(ResamplingTime)/60),
by = c('LibSize','Quantifier')]
dt.salmontime$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('Salmon-Boot','Salmon-Gibbs'),
labels = c('Bootstrap','Gibbs'))
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 | 25 | 1.79 | 9.45 | 11.24 |
| 50 | 3.22 | 13.41 | 16.64 | |
| 100 | 6.09 | 19.15 | 25.24 | |
| Gibbs | 25 | 1.78 | 0.91 | 2.69 |
| 50 | 3.22 | 1.62 | 4.85 | |
| 100 | 6.05 | 3.01 | 9.06 |
edgeR comparison
dt.dte <- merge(x = dt.time[grepl("edgeR-SC$|edgeR.legacy-SC$",Method) &
grepl("Salmon",Quantifier) &
Scenario == 'unbalanced' &
FC == 'fc2',c('LibsPerGroup','Method','Quantifier','Time')],
y = dt.roc[grepl("edgeR-SC$|edgeR.legacy-SC$",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$LibsPerGroup %<>% factor(levels = paste0('#Lib/Group = ',c(3,5,10)),
labels = c('Three','Five','Ten'))
dt.dte$Method %<>% factor(levels = c('edgeR-SC','edgeR.legacy-SC'),
labels = c('edgeR-v4','edgeR-v3'))
dt.dte$Quantifier %<>% factor(levels = c('Salmon-Boot','Salmon-Gibbs'),
labels = c('Bootstrap','Gibbs'))
dt.dte.edger <- dt.dte[Method == 'edgeR-v4',]
setnames(dt.dte.edger,
old = c('Time','oTPR','oFDR'),
new = c('Time.edgeR','oTPR.edgeR','oFDR.edgeR'))
dt.dte.edger.legacy <- dt.dte[Method != 'edgeR-v4',]
setnames(dt.dte.edger.legacy,
old = c('Time','oTPR','oFDR'),
new = c('Time.edgeRLegacgy','oTPR.edgeRLegacgy','oFDR.edgeRLegacgy'))
dt.dte <- merge(dt.dte.edger.legacy[,-2],dt.dte.edger[,-2],
by = c('LibsPerGroup','Quantifier'),
all.x = TRUE,
sort = FALSE)
cnames <- c('Samples per\nGroup',
'Sampling\nMethod',
'Time',
'Power',
'FDR',
'Time',
'Power',
'FDR')
tb.dte <- kbl(x = dt.dte,
digits = 2,
format = 'latex',
escape = FALSE,
booktabs = TRUE,
col.names = linebreak(cnames,align = 'c'),
align = 'llcccccc') %>%
add_header_above(c(" " = 2, "edgeR-v3" = 3,"edgeR-v4" = 3)) %>%
collapse_rows(1, latex_hline = 'major')
kbl(x = dt.dte,
digits = 2,
format = 'pipe',
escape = FALSE,
booktabs = TRUE,
col.names = linebreak(cnames,align = 'c'),
align = 'llcccccc') %>%
add_header_above(c(" " = 2, "edgeR-v3" = 3,"edgeR-v4" = 3)) %>%
collapse_rows(1, latex_hline = 'major')| Time | Power | FDR | Time | Power | FDR | ||
|---|---|---|---|---|---|---|---|
| Three | Bootstrap | 6.79 | 0.39 | 0.04 | 3.54 | 0.42 | 0.04 |
| Gibbs | 7.81 | 0.38 | 0.03 | 4.46 | 0.42 | 0.04 | |
| Five | Bootstrap | 9.96 | 0.71 | 0.04 | 5.79 | 0.73 | 0.04 |
| Gibbs | 11.38 | 0.72 | 0.04 | 7.21 | 0.74 | 0.04 | |
| Ten | Bootstrap | 17.78 | 0.91 | 0.04 | 10.95 | 0.92 | 0.05 |
| Gibbs | 20.60 | 0.92 | 0.04 | 13.61 | 0.94 | 0.05 |
Output files
fig.metrics <- wrap_plots(A = fig.power,
B = fig.fdr,
C = fig.type1error,
design = c(area(1,1,1,2),area(2,1),area(2,2)),
heights = c(0.3,0.7),widths = c(3.5/6,1-3.5/6)) +
plot_annotation(tag_levels = 'a',
theme = theme(plot.tag = element_text(size = bs)))
ggsave(plot = fig.metrics,filename = file.path(path.misc,'Figure-Metrics.pdf'),
device = 'pdf',width = 6,height = 5,units = 'in',dpi = 300)
ggsave(plot = fig.roc,filename = file.path(path.misc,'Figure-ROC.pdf'),
device = 'pdf',width = 6,height = 5,units = 'in',dpi = 300)
save_kable(tb.salmontime,file = file.path(path.misc,'Table-QuantTime.tex'))
save_kable(tb.dte,file = file.path(path.misc,'Table-DTETime.tex'))
sessionInfo()R version 4.4.0 (2024-04-24)
Platform: x86_64-pc-linux-gnu
Running under: CentOS Linux 7 (Core)
Matrix products: default
BLAS: /stornext/System/data/apps/R/R-4.4.0/lib64/R/lib/libRblas.so
LAPACK: /stornext/System/data/apps/R/R-4.4.0/lib64/R/lib/libRlapack.so; LAPACK version 3.12.0
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: UTC
tzcode source: system (glibc)
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] pkg_1.0 kableExtra_1.4.0 plyr_1.8.9 patchwork_1.2.0
[5] ggplot2_3.5.1 magrittr_2.0.3 data.table_1.15.4 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] splines_4.4.0 later_1.3.2
[3] BiocIO_1.14.0 bitops_1.0-7
[5] filelock_1.0.3 R.oo_1.26.0
[7] tibble_3.2.1 XML_3.99-0.16.1
[9] lifecycle_1.0.4 httr2_1.0.1
[11] edgeR_4.2.0 rprojroot_2.0.4
[13] processx_3.8.4 lattice_0.22-6
[15] ensembldb_2.28.0 limma_3.60.3
[17] sass_0.4.9 rmarkdown_2.27
[19] jquerylib_0.1.4 yaml_2.3.8
[21] remotes_2.5.0 httpuv_1.6.15
[23] sessioninfo_1.2.2 pkgbuild_1.4.4
[25] DBI_1.2.3 abind_1.4-5
[27] pkgload_1.3.4 zlibbioc_1.50.0
[29] GenomicRanges_1.56.1 R.utils_2.12.3
[31] purrr_1.0.2 AnnotationFilter_1.28.0
[33] BiocGenerics_0.50.0 RCurl_1.98-1.14
[35] rappdirs_0.3.3 git2r_0.33.0
[37] GenomeInfoDbData_1.2.12 wasabi_1.0.1
[39] IRanges_2.38.0 S4Vectors_0.42.0
[41] fishpond_2.10.0 svglite_2.1.3
[43] codetools_0.2-20 DelayedArray_0.30.1
[45] xml2_1.3.6 tidyselect_1.2.1
[47] farver_2.1.2 UCSC.utils_1.0.0
[49] matrixStats_1.3.0 stats4_4.4.0
[51] BiocFileCache_2.12.0 showtext_0.9-7
[53] GenomicAlignments_1.40.0 jsonlite_1.8.8
[55] ellipsis_0.3.2 systemfonts_1.1.0
[57] tools_4.4.0 progress_1.2.3
[59] ragg_1.3.2 Rcpp_1.0.12
[61] glue_1.7.0 svMisc_1.2.3
[63] SparseArray_1.4.8 xfun_0.45
[65] MatrixGenerics_1.16.0 usethis_2.2.3
[67] GenomeInfoDb_1.40.1 dplyr_1.1.4
[69] withr_3.0.0 BiocManager_1.30.23
[71] fastmap_1.2.0 rhdf5filters_1.16.0
[73] fansi_1.0.6 callr_3.7.6
[75] digest_0.6.35 R6_2.5.1
[77] mime_0.12 textshaping_0.4.0
[79] colorspace_2.1-0 gtools_3.9.5
[81] biomaRt_2.60.0 RSQLite_2.3.7
[83] R.methodsS3_1.8.2 utf8_1.2.4
[85] generics_0.1.3 tximeta_1.22.1
[87] rtracklayer_1.62.0 prettyunits_1.2.0
[89] httr_1.4.7 htmlwidgets_1.6.4
[91] S4Arrays_1.4.1 whisker_0.4.1
[93] pkgconfig_2.0.3 gtable_0.3.5
[95] blob_1.2.4 SingleCellExperiment_1.26.0
[97] XVector_0.44.0 htmltools_0.5.8.1
[99] sysfonts_0.8.9 profvis_0.3.8
[101] ProtGenerics_1.36.0 sleuth_0.30.0
[103] scales_1.3.0 Biobase_2.64.0
[105] Rsubread_2.18.0 png_0.1-8
[107] knitr_1.47 rstudioapi_0.16.0
[109] tzdb_0.4.0 rjson_0.2.21
[111] curl_5.2.1 showtextdb_3.0
[113] cachem_1.1.0 rhdf5_2.48.0
[115] stringr_1.5.1 BiocVersion_3.19.1
[117] parallel_4.4.0 miniUI_0.1.1.1
[119] AnnotationDbi_1.66.0 restfulr_0.0.15
[121] desc_1.4.3 pillar_1.9.0
[123] grid_4.4.0 vctrs_0.6.5
[125] urlchecker_1.0.1 promises_1.3.0
[127] dbplyr_2.5.0 xtable_1.8-4
[129] tximport_1.32.0 evaluate_0.24.0
[131] readr_2.1.5 GenomicFeatures_1.56.0
[133] locfit_1.5-9.9 cli_3.6.2
[135] compiler_4.4.0 Rsamtools_2.20.0
[137] rlang_1.1.4 crayon_1.5.2
[139] labeling_0.4.3 ps_1.7.6
[141] getPass_0.2-4 fs_1.6.4
[143] stringi_1.8.4 viridisLite_0.4.2
[145] BiocParallel_1.38.0 txdbmaker_1.0.0
[147] munsell_0.5.1 Biostrings_2.72.1
[149] lazyeval_0.2.2 devtools_2.4.5
[151] Matrix_1.7-0 hms_1.1.3
[153] bit64_4.0.5 Rhdf5lib_1.26.0
[155] KEGGREST_1.44.0 statmod_1.5.0
[157] shiny_1.8.1.1 highr_0.11
[159] SummarizedExperiment_1.34.0 AnnotationHub_3.12.0
[161] memoise_2.0.1 thematic_0.1.5
[163] bslib_0.7.0 bit_4.0.5