simulation
Pedro Baldoni
06 January, 2023
Last updated: 2023-01-06
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Knit directory: wf-TranscriptDE/analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | a8c51af | Pedro Baldoni | 2023-01-05 | Updating simulation-paper report |
| html | a8c51af | Pedro Baldoni | 2023-01-05 | Updating simulation-paper report |
| Rmd | d34d4e6 | Pedro Baldoni | 2022-11-24 | Adding simulation-paper-report |
| html | d34d4e6 | Pedro Baldoni | 2022-11-24 | Adding simulation-paper-report |
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(ggplot2)
library(thematic)
library(plyr)
library(magrittr)
library(limma)
library(edgeR)
library(BiocParallel)
library(devtools)
library(purrr)
library(readr)
library(ggpubr)
library(kableExtra)
load_all('../code/pkg/')
BPPARAM <- MulticoreParam(workers = 16,progressbar = TRUE)
register(BPPARAM)cleanPlot <- function(x,fig){
if (x == max(seq_along(fig))) {
y <- fig[[x]]
} else{
y <- fig[[x]] + theme(axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank())
}
if (x > 1) {
y <- y + theme(strip.background.x = element_blank(),
strip.text.x = element_blank())
}
return(y)
}
subsetDT <- function(x,scenario,panel = NULL,tx.per.gene = NULL, plot = TRUE){
if(isTRUE(plot)){
if(panel %in% c('A','B')){
out <- x[Genome == scenario['genome'] &
FC == ifelse(panel == 'A','fc2','fc1') &
Length == scenario['length'] &
Reads == scenario['read'] &
Quantifier == scenario['quantifier'] &
Scenario == scenario['scenario'],]
} else{
out <- x[Genome == scenario['genome'] &
FC == 'fc1' &
Length == scenario['length'] &
Reads == scenario['read'] &
Quantifier == scenario['quantifier'] &
Scenario == scenario['scenario'] &
TxPerGene == tx.per.gene ,]
}
} else{
out <- x[Genome == scenario['genome'] &
FC == 'fc2' &
Quantifier == scenario['quantifier'] &
TxPerGene == scenario['txpergene'],]
}
return(out)
}Analysis
Data wrangling
path.misc <- file.path('../misc',knitr::current_input())
dir.create(path.misc,recursive = TRUE,showWarnings = FALSE)
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.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)# Loading datasets
dt.fdr <- do.call(rbind,lapply(path.fdr,fread))
dt.metrics <- do.call(rbind,lapply(path.metrics,fread))
dt.time <- do.call(rbind,lapply(path.time,fread))
dt.quantile <- do.call(rbind,lapply(path.quantile,fread))
dt.pvalue <- do.call(rbind,lapply(path.pvalue,fread))
dt.overdispersion <- do.call(rbind,lapply(path.overdispersion,fread))# Changing labels
dt.fdr$TxPerGene %<>%
mapvalues(from = paste0(c(2, 3, 4, 5, 9999), 'TxPerGene'),
to = c(paste0("#Tx/Gene = ", c(2, 3, 4, 5)), 'All Transcripts'))
dt.fdr$LibsPerGroup %<>%
mapvalues(from = paste0(c(3, 5), 'libsPerGroup'),
to = paste0('#Lib/Group = ', c(3, 5)))
dt.fdr$Quantifier %<>% mapvalues(from = 'salmon', to = 'Salmon')
dt.fdr$Length %<>% mapvalues(from = paste0('readlen-', seq(50, 150, 25)),
to = paste0(seq(50, 150, 25), 'bp'))
dt.metrics$TxPerGene %<>%
mapvalues(from = paste0(c(2, 3, 4, 5, 9999), 'TxPerGene'),
to = c(paste0("#Tx/Gene = ", c(2, 3, 4, 5)), 'All Transcripts'))
dt.metrics$LibsPerGroup %<>%
mapvalues(from = paste0(c(3, 5), 'libsPerGroup'),
to = paste0('#Lib/Group = ', c(3, 5)))
dt.metrics$Quantifier %<>% mapvalues(from = 'salmon', to = 'Salmon')
dt.metrics$Length %<>% mapvalues(from = paste0('readlen-', seq(50, 150, 25)),
to = paste0(seq(50, 150, 25), 'bp'))
dt.time$TxPerGene %<>%
mapvalues(from = paste0(c(2, 3, 4, 5, 9999), 'TxPerGene'),
to = c(paste0("#Tx/Gene = ", c(2, 3, 4, 5)), 'All Transcripts'))
dt.time$LibsPerGroup %<>%
mapvalues(from = paste0(c(3, 5), 'libsPerGroup'),
to = paste0('#Lib/Group = ', c(3, 5)))
dt.time$Quantifier %<>% mapvalues(from = 'salmon', to = 'Salmon')
dt.time$Length %<>% mapvalues(from = paste0('readlen-', seq(50, 150, 25)),
to = paste0(seq(50, 150, 25), 'bp'))
dt.quantile$TxPerGene %<>%
mapvalues(from = paste0(c(2, 3, 4, 5, 9999), 'TxPerGene'),
to = c(paste0("#Tx/Gene = ", c(2, 3, 4, 5)), 'All Transcripts'))
dt.quantile$LibsPerGroup %<>%
mapvalues(from = paste0(c(3, 5), 'libsPerGroup'),
to = paste0('#Lib/Group = ', c(3, 5)))
dt.quantile$Quantifier %<>% mapvalues(from = 'salmon', to = 'Salmon')
dt.quantile$Length %<>% mapvalues(from = paste0('readlen-', seq(50, 150, 25)),
to = paste0(seq(50, 150, 25), 'bp'))
dt.pvalue$TxPerGene %<>%
mapvalues(from = paste0(c(2, 3, 4, 5, 9999), 'TxPerGene'),
to = c(paste0("#Tx/Gene = ", c(2, 3, 4, 5)), 'All Transcripts'))
dt.pvalue$LibsPerGroup %<>%
mapvalues(from = paste0(c(3, 5), 'libsPerGroup'),
to = paste0('#Lib/Group = ', c(3, 5)))
dt.pvalue$Quantifier %<>% mapvalues(from = 'salmon', to = 'Salmon')
dt.pvalue$Length %<>% mapvalues(from = paste0('readlen-', seq(50, 150, 25)),
to = paste0(seq(50, 150, 25), 'bp'))
dt.overdispersion$TxPerGene %<>%
mapvalues(from = paste0(c(2, 3, 4, 5, 9999), 'TxPerGene'),
to = c(paste0("#Tx/Gene = ", c(2, 3, 4, 5)), 'All Transcripts'))
dt.overdispersion$LibsPerGroup %<>%
mapvalues(from = paste0(c(3, 5), 'libsPerGroup'),
to = paste0('#Lib/Group = ', c(3, 5)))
dt.overdispersion$Quantifier %<>% mapvalues(from = 'salmon', to = 'Salmon')
dt.overdispersion$Length %<>% mapvalues(from = paste0('readlen-', seq(50, 150, 25)),
to = paste0(seq(50, 150, 25), 'bp'))dt.scenario <- expand.grid('genome' = 'mm39',
'length' = c('50bp','75bp','100bp','125bp','150bp'),
'read' = c('single-end','paired-end'),
'quantifier' = c('Salmon','kallisto'),
'scenario' = c('balanced','unbalanced'),
stringsAsFactors = FALSE)
dt.scenario <- as.data.table(dt.scenario)Power plot & False discovery rate
scenario.balanced <- as.character(dt.scenario[length == '100bp' &
read == 'paired-end' &
quantifier == 'Salmon' &
scenario == 'balanced',])
scenario.unbalanced <- as.character(dt.scenario[length == '100bp' &
read == 'paired-end' &
quantifier == 'Salmon' &
scenario == 'unbalanced',])
names(scenario.balanced) <- colnames(dt.scenario)
names(scenario.unbalanced) <- colnames(dt.scenario)
dt.power <- rbind(subsetDT(dt.metrics,scenario.balanced,'A'),
subsetDT(dt.metrics,scenario.unbalanced,'A'))
dt.power$LibsPerGroup %<>% mapvalues(from = paste0('#Lib/Group = ', c(3, 5)),
to = paste0(c(3,5),' samples per group'))
dt.power$Scenario %<>%
mapvalues(from = c('balanced','unbalanced'),
to = c('Equal library sizes','Unequal library sizes'))
dt.power[, FDR := roundPretty(ifelse((FP+TP) == 0,NA,100*FP/(FP+TP)),1)]
dt.power <- dt.power[TxPerGene == 'All Transcripts',]
sub.byvar <-
colnames(dt.power)[-which(colnames(dt.power) %in% c('P.SIG','TP','FP'))]
gap <- 0.05*max(dt.power$TP + dt.power$FP)
x.melt <- melt(dt.power,id.vars = sub.byvar,
measure.vars = c('TP','FP'),
variable.name = 'Type',
value.name = 'Value')
x.melt$Type <-
factor(x.melt$Type,
levels = c('FP','TP'),
labels = c('False Positive','True Positive'))
plot.power <- ggplot(x.melt,aes(x = Method,y = Value,fill = Type)) +
facet_grid(rows = vars(LibsPerGroup),cols = vars(Scenario)) +
geom_col() +
geom_text(aes(x = Method,y = (TP + FP) + gap,label = FDR),
inherit.aes = FALSE,data = dt.power[FDR != 'NA',],vjust = 0) +
theme_bw() +
scale_fill_manual(values = c('#ff0000','#595959')) +
labs(x = NULL,y = paste('# DE Transcripts at FDR < 0.05')) +
scale_y_continuous(limits = c(0,3000)) +
theme(panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank(),
legend.position = c(0.15,0.90),legend.title = element_blank(),
axis.text.x = element_text(angle = 90),
legend.background = element_rect(fill=alpha('white', 0)))dt.fdr.plot <- rbind(subsetDT(dt.fdr,scenario.balanced,'A'),
subsetDT(dt.fdr,scenario.unbalanced,'A'))
dt.fdr.plot$LibsPerGroup %<>%
mapvalues(from = paste0('#Lib/Group = ', c(3, 5)),
to = paste0(c(3,5),' samples per group'))
dt.fdr.plot$Scenario %<>%
mapvalues(from = c('balanced','unbalanced'),
to = c('Equal library sizes','Unequal library sizes'))
dt.fdr.plot <- dt.fdr.plot[TxPerGene == 'All Transcripts',]
plot.fdr <- ggplot(dt.fdr.plot,aes(x = N,y = FDR,color = Method,group = Method)) +
facet_grid(rows = vars(LibsPerGroup),cols = vars(Scenario)) +
geom_line(linewidth = 1) +
theme_bw() +
scale_color_manual(values = methodsNames()$color) +
scale_y_continuous(limits = c(0,1250)) +
theme(panel.grid = element_blank(),legend.direction = 'vertical',
legend.position = c(0.15,0.825),legend.title = element_blank(),
legend.background = element_rect(fill=alpha('white', 0))) +
labs(y = 'False discoveries',x = 'Transcripts chosen')ggarrange(plot.power,plot.fdr,nrow = 1,ncol = 2,common.legend = FALSE,
labels = c('(a)','(b)'))
Simulation results. (a): Average number of true (blue) and false (red) positive DE transcripts at nominal 0.05 FDR. Observed is FDR annotated over bars. (b): Average number of false discoveries as a function of the number of chosen transcripts. Results from the simulation scenario with 100 bp paired-end read data quantified with Salmon, averaged over 20 simulations.
| Version | Author | Date |
|---|---|---|
| 5dcd60b | Pedro Baldoni | 2023-01-04 |
Type 1 error
dt.type1error <- rbind(subsetDT(dt.metrics,scenario.balanced,'B'),
subsetDT(dt.metrics,scenario.unbalanced,'B'))
dt.type1error$LibsPerGroup %<>%
mapvalues(from = paste0('#Lib/Group = ', c(3, 5)),
to = paste0(c(3,5),' samples per group'))
dt.type1error$Scenario %<>%
mapvalues(from = c('balanced','unbalanced'),
to = c('Equal library sizes','Unequal library sizes'))
dt.type1error[, FDR := roundPretty(ifelse((FP+TP) == 0,NA,100*FP/(FP+TP)),1)]
dt.type1error <- dt.type1error[TxPerGene == 'All Transcripts',]
sub.byvar <-
colnames(dt.type1error)[-which(colnames(dt.type1error) %in% c('P.SIG','TP','FP'))]
x.melt <-
melt(dt.type1error,id.vars = sub.byvar,
measure.vars = c('P.SIG'),variable.name = 'Type',value.name = 'Value')
plot.type1error <- ggplot(x.melt,aes(x = Method,y = Value)) +
facet_grid(rows = vars(LibsPerGroup),cols = vars(Scenario)) +
geom_col(fill = "#bebebe",col = '#595959') +
theme_bw() +
geom_hline(yintercept = 0.05,color = 'red',linetype = 'dashed') +
labs(x = NULL,y = paste('Proportion of p-values < 0.05')) +
scale_y_continuous(limits = c(0,0.06),breaks = c(0,0.02,0.04,0.06)) +
theme(panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.minor.y = element_blank(),
legend.position = 'top',legend.title = element_blank(),
axis.text.x = element_text(angle = 90))dt.pvalue.plot <- subsetDT(dt.pvalue,scenario.unbalanced,'C','All Transcripts')
dt.pvalue.plot <- dt.pvalue.plot[LibsPerGroup == '#Lib/Group = 5',]
plot.hist <- ggplot(data = dt.pvalue.plot,aes(x = PValue,y = Density.Avg)) +
facet_wrap('Method',nrow = 2,ncol = 3) +
geom_col(fill = "#bebebe",col = '#595959',position = position_dodge(),width = 0.75) +
geom_hline(yintercept = 1,col = 'red',linetype = 'dashed') +
theme_bw() +
theme(panel.grid = element_blank()) +
scale_x_discrete(breaks = c("(0.00-0.05]","(0.50-0.55]","(0.95-1.00]"),
labels = c(0.00,0.50,1.00)) +
labs(x = 'P-values',y = 'Density')ggarrange(plot.type1error,plot.hist,nrow = 1, ncol = 2,common.legend = FALSE,
labels = c('(a)','(b)'))
Null simulation results.(a): Average proportion of transcripts with unadjusted p-values less than 0.05. (b): Density histograms with smoothing of raw p-values for a scenario with unequal library sizes and 5 samples per group. Results from the null simulation scenario with 100 bp paired-end read data quantified with Salmon, averaged over 20 simulations.
| Version | Author | Date |
|---|---|---|
| 5dcd60b | Pedro Baldoni | 2023-01-04 |
Read type
# Overdispersion fold-change
dt.sigma2 <- dt.overdispersion[TxPerGene == 'All Transcripts' &
Quantifier == 'Salmon' &
Scenario == 'unbalanced' &
FC == 'fc2',]
dt.sigma2 <- dt.sigma2[,-c(1,3,5,6,8,10:15)]
dt.sigma2.150.PE <- dt.sigma2[Length == '150bp' & Reads == 'paired-end',][,-c(1,2)]
setnames(dt.sigma2.150.PE,old = 'Mean',new = 'Mean.150.PE')
dt.sigma2 <- merge(dt.sigma2,dt.sigma2.150.PE,by = c('LibsPerGroup'),
all.x=TRUE,sort = FALSE)
dt.sigma2[,FC := Mean - Mean.150.PE]
dt.sigma2.3 <- dt.sigma2[LibsPerGroup == '#Lib/Group = 3',]
dt.sigma2.5 <- dt.sigma2[LibsPerGroup == '#Lib/Group = 5',]
dt.sigma2.3 <- dcast(dt.sigma2.3,LibsPerGroup + Length ~ Reads,value.var = 'FC')
dt.sigma2.5 <- dcast(dt.sigma2.5,LibsPerGroup + Length ~ Reads,value.var = 'FC')
setnames(dt.sigma2.3,
old = c('paired-end','single-end'),
new = c('FC.PE','FC.SE'))
setnames(dt.sigma2.5,
old = c('paired-end','single-end'),
new = c('FC.PE','FC.SE'))
setcolorder(dt.sigma2.3,neworder = c('LibsPerGroup','Length','FC.PE','FC.SE'))
setcolorder(dt.sigma2.5,neworder = c('LibsPerGroup','Length','FC.PE','FC.SE'))
dt.sigma2.long <- rbind(dt.sigma2.3,dt.sigma2.5)
dt.sigma2.long$LibsPerGroup %<>%
mapvalues(from = c('#Lib/Group = 3','#Lib/Group = 5'),to = c(3,5))
dt.sigma2.long$Length %<>% factor(levels = paste0(seq(50,150,25),'bp'))
dt.sigma2.long <- dt.sigma2.long[order(LibsPerGroup,Length),]
# Power and FDR
dt.scenario.table <-
expand.grid('genome' = 'mm39',
'quantifier' = c('Salmon','kallisto'),
'txpergene' = c(paste0('#Tx/Gene = ',2:5),'All Transcripts'),
stringsAsFactors = FALSE)
dt.scenario.table <- as.data.table(dt.scenario.table)
scenario.table <-
dt.scenario.table[quantifier == 'Salmon' & txpergene == 'All Transcripts',]
scenario.table <- as.character(scenario.table)
names(scenario.table) <- colnames(dt.scenario.table)
dt.table <- subsetDT(dt.metrics,scenario = scenario.table,plot = FALSE)
dt.table <- dt.table[Method == 'edgeR-scaled' &
Scenario == 'unbalanced',]
dt.table[,Power := TP/3000]
dt.table[,FDR := ifelse((FP+TP) == 0,NA,FP/(FP+TP))]
dt.table.3 <- dt.table[LibsPerGroup == '#Lib/Group = 3',][,-c(1,3,5,6,8:12)]
dt.table.5 <- dt.table[LibsPerGroup == '#Lib/Group = 5',][,-c(1,3,5,6,8:12)]
dt.table.3 <- dcast(dt.table.3,LibsPerGroup + Length ~ Reads,value.var = c('Power','FDR'))
dt.table.5 <- dcast(dt.table.5,LibsPerGroup + Length ~ Reads,value.var = c('Power','FDR'))
setnames(dt.table.3,
old = c('Power_paired-end','Power_single-end','FDR_paired-end','FDR_single-end'),
new = c('Power.PE','Power.SE','FDR.PE','FDR.SE'))
setnames(dt.table.5,
old = c('Power_paired-end','Power_single-end','FDR_paired-end','FDR_single-end'),
new = c('Power.PE','Power.SE','FDR.PE','FDR.SE'))
setcolorder(dt.table.3,neworder = c('LibsPerGroup','Length','Power.SE','FDR.SE','Power.PE','FDR.PE'))
setcolorder(dt.table.5,neworder = c('LibsPerGroup','Length','Power.SE','FDR.SE','Power.PE','FDR.PE'))
dt.table.long <- rbind(dt.table.3,dt.table.5)
dt.table.long$LibsPerGroup %<>% mapvalues(from = c('#Lib/Group = 3','#Lib/Group = 5'),to = c(3,5))
dt.table.long$Length %<>% factor(levels = paste0(seq(50,150,25),'bp'))
dt.table.long <- dt.table.long[order(LibsPerGroup,Length),]
# Organizing tables
dt.table.sigma2 <-
merge(dt.table.long,dt.sigma2.long,
all.x = TRUE,by = c('LibsPerGroup','Length'),sort = FALSE)
setcolorder(dt.table.sigma2,
neworder = c('LibsPerGroup','Length',
'FC.SE','Power.SE','FDR.SE',
'FC.PE','Power.PE','FDR.PE'))
dt.table.sigma2[,Length := gsub('bp','',Length)]
dt.table.sigma2$LibsPerGroup %<>% mapvalues(from = c(3,5),to = c('Three','Five'))
tb <- kbl(dt.table.sigma2,digits = 3,format = 'latex',escape = FALSE,booktabs = TRUE,
align = c('c','r',rep('r',6)),
col.names = linebreak(c('Samples per\ngroup','Read Length\n(bp)',
'Mapping Ambiguity\nFold Change','Power','FDR',
'Mapping Ambiguity\nFold Change','Power','FDR'),align = "c")) %>%
add_header_above(c(" " = 2, "Single-end Read" = 3, "Paired-end Read" = 3)) %>%
collapse_rows(1, latex_hline = 'major')
save_kable(tb,file = file.path(path.misc,"simulation-paper_read.tex"))
sessionInfo()R version 4.2.1 (2022-06-23)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)
Matrix products: default
BLAS: /stornext/System/data/apps/R/R-4.2.1/lib64/R/lib/libRblas.so
LAPACK: /stornext/System/data/apps/R/R-4.2.1/lib64/R/lib/libRlapack.so
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] stats graphics grDevices utils datasets methods base
other attached packages:
[1] pkg_1.0 kableExtra_1.3.4 ggpubr_0.5.0
[4] readr_2.1.3 purrr_0.3.5 devtools_2.4.5
[7] usethis_2.1.6 BiocParallel_1.32.3 edgeR_3.40.0
[10] limma_3.54.0 magrittr_2.0.3 plyr_1.8.8
[13] thematic_0.1.2.1 ggplot2_3.4.0 data.table_1.14.6
loaded via a namespace (and not attached):
[1] utf8_1.2.2 tidyselect_1.2.0
[3] RSQLite_2.2.19 AnnotationDbi_1.60.0
[5] htmlwidgets_1.5.4 grid_4.2.1
[7] munsell_0.5.0 codetools_0.2-18
[9] miniUI_0.1.1.1 withr_2.5.0
[11] colorspace_2.0-3 Biobase_2.58.0
[13] filelock_1.0.2 highr_0.9
[15] knitr_1.41 rstudioapi_0.14
[17] stats4_4.2.1 SingleCellExperiment_1.20.0
[19] ggsignif_0.6.4 Rsubread_2.12.0
[21] MatrixGenerics_1.10.0 labeling_0.4.2
[23] git2r_0.30.1 tximport_1.26.0
[25] GenomeInfoDbData_1.2.9 bit64_4.0.5
[27] farver_2.1.1 rhdf5_2.42.0
[29] rprojroot_2.0.3 vctrs_0.5.1
[31] generics_0.1.3 xfun_0.35
[33] BiocFileCache_2.6.0 fishpond_2.4.0
[35] R6_2.5.1 GenomeInfoDb_1.34.3
[37] locfit_1.5-9.6 AnnotationFilter_1.22.0
[39] bitops_1.0-7 rhdf5filters_1.10.0
[41] cachem_1.0.6 DelayedArray_0.24.0
[43] assertthat_0.2.1 promises_1.2.0.1
[45] BiocIO_1.8.0 scales_1.2.1
[47] gtable_0.3.1 processx_3.8.0
[49] ensembldb_2.22.0 workflowr_1.7.0
[51] rlang_1.0.6 systemfonts_1.0.4
[53] splines_4.2.1 rtracklayer_1.58.0
[55] rstatix_0.7.1 lazyeval_0.2.2
[57] broom_1.0.1 BiocManager_1.30.19
[59] yaml_2.3.6 abind_1.4-5
[61] GenomicFeatures_1.50.2 backports_1.4.1
[63] httpuv_1.6.6 sleuth_0.30.0
[65] wasabi_1.0.1 tools_4.2.1
[67] ellipsis_0.3.2 jquerylib_0.1.4
[69] BiocGenerics_0.44.0 sessioninfo_1.2.2
[71] Rcpp_1.0.9 progress_1.2.2
[73] zlibbioc_1.44.0 RCurl_1.98-1.9
[75] ps_1.7.2 prettyunits_1.1.1
[77] cowplot_1.1.1 urlchecker_1.0.1
[79] S4Vectors_0.36.0 SummarizedExperiment_1.28.0
[81] fs_1.5.2 svMisc_1.2.3
[83] whisker_0.4 ProtGenerics_1.30.0
[85] matrixStats_0.63.0 pkgload_1.3.2
[87] hms_1.1.2 mime_0.12
[89] evaluate_0.18 xtable_1.8-4
[91] XML_3.99-0.12 IRanges_2.32.0
[93] compiler_4.2.1 biomaRt_2.54.0
[95] tibble_3.1.8 crayon_1.5.2
[97] htmltools_0.5.3 later_1.3.0
[99] tzdb_0.3.0 tidyr_1.2.1
[101] DBI_1.1.3 dbplyr_2.2.1
[103] rappdirs_0.3.3 Matrix_1.5-3
[105] car_3.1-1 cli_3.4.1
[107] parallel_4.2.1 GenomicRanges_1.50.1
[109] pkgconfig_2.0.3 GenomicAlignments_1.34.0
[111] xml2_1.3.3 svglite_2.1.0
[113] bslib_0.4.1 webshot_0.5.4
[115] XVector_0.38.0 rvest_1.0.3
[117] stringr_1.4.1 callr_3.7.3
[119] digest_0.6.30 Biostrings_2.66.0
[121] rmarkdown_2.18 tximeta_1.16.0
[123] restfulr_0.0.15 curl_4.3.3
[125] shiny_1.7.3 Rsamtools_2.14.0
[127] gtools_3.9.3 rjson_0.2.21
[129] lifecycle_1.0.3 jsonlite_1.8.3
[131] Rhdf5lib_1.20.0 carData_3.0-5
[133] desc_1.4.2 viridisLite_0.4.1
[135] fansi_1.0.3 pillar_1.8.1
[137] lattice_0.20-45 KEGGREST_1.38.0
[139] fastmap_1.1.0 httr_1.4.4
[141] pkgbuild_1.3.1 interactiveDisplayBase_1.36.0
[143] glue_1.6.2 remotes_2.4.2
[145] png_0.1-7 BiocVersion_3.16.0
[147] bit_4.0.5 stringi_1.7.8
[149] sass_0.4.4 profvis_0.3.7
[151] blob_1.2.3 AnnotationHub_3.6.0
[153] memoise_2.0.1 dplyr_1.0.10