Simulation - Reduced report (results presented in the main paper)
Pedro L. Baldoni
17 February, 2023
Last updated: 2023-02-17
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Knit directory: wf-TranscriptDE/analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 623d429 | Pedro Baldoni | 2023-01-23 | Splitting figures |
| html | 623d429 | Pedro Baldoni | 2023-01-23 | Splitting figures |
| Rmd | 49c9a94 | Pedro Baldoni | 2023-01-19 | Expanding panels to multiple figures |
| html | 49c9a94 | Pedro Baldoni | 2023-01-19 | Expanding panels to multiple figures |
| Rmd | 4276bfc | Pedro Baldoni | 2023-01-06 | Organizing output of latex table |
| html | 4276bfc | Pedro Baldoni | 2023-01-06 | Organizing output of latex table |
| 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
In this report, we present the analysis of the simulations for the
catchSalmon/catchKallisto manuscript. These
simulations aim to generate typical RNA-seq data from mouse experiments.
This report focuses on the results presented in the main paper only. For
a comprehensive report of the results, please refer to the complete report.
Setup
We load necessary libraries and set up the rendering options below.
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)
library(patchwork)
library(ragg)
load_all('../code/pkg/')I use the functions below to produce the histogram plot shown in this report and to quickly subset data tables for specific scenarios.
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
Here we begin summarizing the results to generate the figures presented in the main paper.
Data wrangling
Below I set up the file paths.
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 all summarized results below. Because these datasets are
quite large, I use cache=TRUE to save time when rendering
this page.
# 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))Some data wrangling below.
# 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$Length %<>% mapvalues(from = paste0('readlen-', seq(50, 150, 25)),
to = paste0(seq(50, 150, 25), 'bp'))All the simulated scenarios are generated below.
dt.scenario <- expand.grid('genome' = 'mm39',
'length' = c('50bp','75bp','100bp','125bp','150bp'),
'read' = c('single-end','paired-end'),
'quantifier' = c('Salmon','kallisto'),
'scenario' = c('balanced','unbalanced'),
stringsAsFactors = FALSE)
dt.scenario <- as.data.table(dt.scenario)Power plot & False discovery rate
Below we generate Figure 2 of the main paper.
scenario.balanced <- as.character(dt.scenario[length == '100bp' &
read == 'paired-end' &
quantifier == 'Salmon' &
scenario == 'balanced',])
scenario.unbalanced <- as.character(dt.scenario[length == '100bp' &
read == 'paired-end' &
quantifier == 'Salmon' &
scenario == 'unbalanced',])
names(scenario.balanced) <- colnames(dt.scenario)
names(scenario.unbalanced) <- colnames(dt.scenario)
dt.power <- rbind(subsetDT(dt.metrics,scenario.balanced,'A'),
subsetDT(dt.metrics,scenario.unbalanced,'A'))
dt.power$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','True'))
plot.power <- function(df.bar,df.txt,scenario,library,legend = FALSE, base_size = 8){
tb.bar <- df.bar[Scenario == scenario & LibsPerGroup == library,]
tb.txt <- df.txt[Scenario == scenario & LibsPerGroup == library,][FDR != 'NA',]
ggplot(tb.bar,aes(x = Method,y = Value,fill = Type)) +
geom_col(colour = 'black') +
geom_text(aes(x = Method,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(0,3000)) +
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)) +
if (legend == TRUE) theme(legend.background = element_rect(fill = alpha('white', 0)),
legend.text = element_text(size = base_size),
legend.position = c(0.80,0.90),legend.title = element_blank(),
legend.key.size = unit(0.75,"line")) else theme(legend.position = 'none')
}
fig.power.a <- plot.power(df.bar = x.melt,df.txt = dt.power,scenario = 'Equal library sizes',library = '3 samples per group')
fig.power.b <- plot.power(df.bar = x.melt,df.txt = dt.power,scenario = 'Unequal library sizes',library = '3 samples per group',legend = TRUE)
fig.power.c <- plot.power(df.bar = x.melt,df.txt = dt.power,scenario = 'Equal library sizes',library = '5 samples per group')
fig.power.d <- plot.power(df.bar = x.melt,df.txt = dt.power,scenario = 'Unequal library sizes',library = '5 samples per group')
fig.power <- (fig.power.a + fig.power.b) / (fig.power.c + fig.power.d) +
plot_annotation(tag_levels = 'a') +
theme(plot.tag = element_text(size = 8))
agg_png(filename = file.path(path.misc,"figure2.png"),width = 5,height = 5,units = 'in',res = 300)
fig.power
dev.off()png
2 fig.power
Then, we generate Figure 3 below.
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$Scenario %<>%
mapvalues(from = c('balanced','unbalanced'),
to = c('Equal library sizes','Unequal library sizes'))
dt.fdr.plot <- dt.fdr.plot[TxPerGene == 'All Transcripts',]
plot.fdr <- function(df.line,scenario,library,legend = FALSE,base_size = 8){
tb.bar <- df.line[Scenario == scenario & LibsPerGroup == library,]
ggplot(tb.bar,aes(x = N,y = FDR,color = Method,group = Method)) +
geom_line(linewidth = 0.5) +
scale_color_manual(values = methodsNames()$color) +
scale_y_continuous(limits = c(0,1250)) +
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.position = c(0.3,0.8),
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.a <- plot.fdr(df.line = dt.fdr.plot,scenario = 'Equal library sizes',library = '3 samples per group')
fig.fdr.b <- plot.fdr(df.line = dt.fdr.plot,scenario = 'Unequal library sizes',library = '3 samples per group',legend = TRUE)
fig.fdr.c <- plot.fdr(df.line = dt.fdr.plot,scenario = 'Equal library sizes',library = '5 samples per group')
fig.fdr.d <- plot.fdr(df.line = dt.fdr.plot,scenario = 'Unequal library sizes',library = '5 samples per group')
fig.fdr <- (fig.fdr.a + fig.fdr.b) / (fig.fdr.c + fig.fdr.d) +
plot_annotation(tag_levels = 'a')
agg_png(filename = file.path(path.misc,"figure3.png"),width = 5,height = 5,units = 'in',res = 300)
fig.fdr
dev.off()png
2 fig.fdr
Type 1 error
Figure 4 is created below.
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 <- function(df.bar,scenario,library,legend = FALSE,base_size = 8){
tb.bar <- df.bar[Scenario == scenario & LibsPerGroup == library,]
ggplot(tb.bar,aes(x = Method,y = Value)) +
geom_col(fill = "#bebebe",col = 'black') +
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 = c(0,0.02,0.04,0.06)) +
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))
}
fig.type1error.a <- plot.type1error(df.bar = x.melt,scenario = 'Equal library sizes',library = '3 samples per group')
fig.type1error.b <- plot.type1error(df.bar = x.melt,scenario = 'Unequal library sizes',library = '3 samples per group')
fig.type1error.c <- plot.type1error(df.bar = x.melt,scenario = 'Equal library sizes',library = '5 samples per group')
fig.type1error.d <- plot.type1error(df.bar = x.melt,scenario = 'Unequal library sizes',library = '5 samples per group')
fig.type1error <- (fig.type1error.a + fig.type1error.b) / (fig.type1error.c + fig.type1error.d) +
plot_annotation(tag_levels = 'a')
agg_png(filename = file.path(path.misc,"figure4.png"),width = 5,height = 5,units = 'in',res = 300)
fig.type1error
dev.off()png
2 fig.type1error
Finally, we generate Figure 5.
dt.pvalue.plot <- subsetDT(dt.pvalue,scenario.unbalanced,'C','All Transcripts')
dt.pvalue.plot <- dt.pvalue.plot[LibsPerGroup == '#Lib/Group = 5',]
plot.hist <- function(df.hist,method,legend = FALSE,base_size = 8){
tb.bar <- df.hist[Method == method,]
ggplot(data = tb.bar,aes(x = PValue,y = Density.Avg)) +
geom_col(fill = "#bebebe",col = 'black',position = position_dodge(),width = 0.75) +
geom_hline(yintercept = 1,col = '#ff0000',linetype = 'dashed',linewidth = 0.5) +
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') +
theme_bw(base_size = base_size,base_family = 8) +
theme(panel.grid = element_blank(),
axis.text = element_text(colour = 'black',size = base_size))
}
fig.hist.a <- plot.hist(df.hist = dt.pvalue.plot,method = 'edgeR-raw')
fig.hist.b <- plot.hist(df.hist = dt.pvalue.plot,method = 'edgeR-scaled')
fig.hist.c <- plot.hist(df.hist = dt.pvalue.plot,method = 'sleuth-LRT')
fig.hist.d <- plot.hist(df.hist = dt.pvalue.plot,method = 'sleuth-Wald')
fig.hist.e <- plot.hist(df.hist = dt.pvalue.plot,method = 'Swish')
design <- c(
area(1,1),area(1,2),
area(2,1),area(2,2),
area(3,1)
)
fig.hist <- fig.hist.a + fig.hist.b + fig.hist.c + fig.hist.d + fig.hist.e +
plot_layout(design = design) +
plot_annotation(tag_levels = 'a')
agg_png(filename = file.path(path.misc,"figure5.png"),width = 5,height = 7.5,units = 'in',res = 300)
fig.hist
dev.off()png
2 fig.type1error
Read type
Below we generate Table 1 of the main paper.
# 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\nlog-FC','Power','FDR',
'Mapping Ambiguity\nlog-FC','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,"table1.tex"))Speed
In the main paper we also comment on methods’ performance regarding computing time. The table below present such numbers.
dt.time[, .(min =60*min(Time),
mean = 60*mean(Time),
med = 60*median(Time),
max = 60*max(Time)),by = c('Quantifier','LibsPerGroup','Method')] Quantifier LibsPerGroup Method min mean med
1: kallisto #Lib/Group = 3 sleuth-LRT 122.47190 153.520267 153.639175
2: kallisto #Lib/Group = 3 sleuth-Wald 101.33460 120.665348 120.291200
3: kallisto #Lib/Group = 3 Swish 38.59855 48.828552 48.749300
4: kallisto #Lib/Group = 3 edgeR-scaled 8.11885 10.494707 10.539950
5: kallisto #Lib/Group = 3 edgeR-raw 8.39195 11.359861 11.343150
6: Salmon #Lib/Group = 3 sleuth-LRT 116.52835 149.282093 149.604325
7: Salmon #Lib/Group = 3 sleuth-Wald 97.62610 115.840474 116.410875
8: Salmon #Lib/Group = 3 Swish 37.41650 46.387361 46.050375
9: Salmon #Lib/Group = 3 edgeR-scaled 5.71395 7.365681 7.438375
10: Salmon #Lib/Group = 3 edgeR-raw 6.06345 8.133473 8.236800
11: kallisto #Lib/Group = 5 sleuth-LRT 183.69085 215.280671 213.857650
12: kallisto #Lib/Group = 5 sleuth-Wald 163.19610 181.978687 181.295200
13: kallisto #Lib/Group = 5 Swish 55.27110 69.293242 69.340225
14: kallisto #Lib/Group = 5 edgeR-scaled 12.22420 15.182799 15.288325
15: kallisto #Lib/Group = 5 edgeR-raw 12.33520 16.038983 16.092425
16: Salmon #Lib/Group = 5 sleuth-LRT 180.08925 214.219356 214.023450
17: Salmon #Lib/Group = 5 sleuth-Wald 158.58880 177.781138 178.197675
18: Salmon #Lib/Group = 5 Swish 52.00585 64.131554 64.104225
19: Salmon #Lib/Group = 5 edgeR-scaled 8.04430 10.270687 10.360175
20: Salmon #Lib/Group = 5 edgeR-raw 8.45140 11.141194 11.261925
max
1: 239.02190
2: 170.68920
3: 95.24830
4: 14.38945
5: 18.12705
6: 291.38200
7: 157.88850
8: 93.28435
9: 9.84275
10: 10.73100
11: 260.40370
12: 210.15900
13: 90.89460
14: 18.83985
15: 20.33185
16: 589.00495
17: 204.32945
18: 97.44075
19: 12.95745
20: 13.80970
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 ragg_1.2.5 patchwork_1.1.2
[4] kableExtra_1.3.4 ggpubr_0.6.0 readr_2.1.4
[7] purrr_1.0.1 devtools_2.4.5 usethis_2.1.6
[10] BiocParallel_1.32.5 edgeR_3.40.2 limma_3.54.1
[13] magrittr_2.0.3 plyr_1.8.8 thematic_0.1.2.1
[16] ggplot2_3.4.1 data.table_1.14.6
loaded via a namespace (and not attached):
[1] utf8_1.2.3 tidyselect_1.2.0
[3] RSQLite_2.2.20 AnnotationDbi_1.60.0
[5] htmlwidgets_1.6.1 grid_4.2.1
[7] munsell_0.5.0 codetools_0.2-19
[9] miniUI_0.1.1.1 withr_2.5.0
[11] colorspace_2.1-0 Biobase_2.58.0
[13] filelock_1.0.2 highr_0.10
[15] knitr_1.42 rstudioapi_0.14
[17] stats4_4.2.1 SingleCellExperiment_1.20.0
[19] ggsignif_0.6.4 Rsubread_2.12.2
[21] labeling_0.4.2 MatrixGenerics_1.10.0
[23] git2r_0.31.0 tximport_1.26.1
[25] GenomeInfoDbData_1.2.9 farver_2.1.1
[27] bit64_4.0.5 rhdf5_2.42.0
[29] rprojroot_2.0.3 vctrs_0.5.2
[31] generics_0.1.3 xfun_0.37
[33] BiocFileCache_2.6.0 fishpond_2.4.1
[35] R6_2.5.1 GenomeInfoDb_1.34.9
[37] locfit_1.5-9.7 AnnotationFilter_1.22.0
[39] bitops_1.0-7 rhdf5filters_1.10.0
[41] cachem_1.0.6 DelayedArray_0.24.0
[43] showtext_0.9-5 assertthat_0.2.1
[45] promises_1.2.0.1 BiocIO_1.8.0
[47] scales_1.2.1 gtable_0.3.1
[49] processx_3.8.0 ensembldb_2.22.0
[51] workflowr_1.7.0 rlang_1.0.6
[53] systemfonts_1.0.4 splines_4.2.1
[55] rtracklayer_1.58.0 rstatix_0.7.2
[57] lazyeval_0.2.2 broom_1.0.3
[59] BiocManager_1.30.19 yaml_2.3.7
[61] abind_1.4-5 GenomicFeatures_1.50.4
[63] backports_1.4.1 httpuv_1.6.5
[65] sleuth_0.30.0 wasabi_1.0.1
[67] tools_4.2.1 ellipsis_0.3.2
[69] jquerylib_0.1.4 BiocGenerics_0.44.0
[71] sessioninfo_1.2.2 Rcpp_1.0.10
[73] progress_1.2.2 zlibbioc_1.44.0
[75] RCurl_1.98-1.10 ps_1.7.2
[77] prettyunits_1.1.1 urlchecker_1.0.1
[79] S4Vectors_0.36.1 SummarizedExperiment_1.28.0
[81] fs_1.6.1 svMisc_1.2.3
[83] whisker_0.4.1 ProtGenerics_1.30.0
[85] matrixStats_0.63.0 pkgload_1.3.2
[87] hms_1.1.2 mime_0.12
[89] evaluate_0.20 xtable_1.8-4
[91] XML_3.99-0.13 IRanges_2.32.0
[93] compiler_4.2.1 biomaRt_2.54.0
[95] tibble_3.1.8 crayon_1.5.2
[97] htmltools_0.5.4 later_1.3.0
[99] tzdb_0.3.0 tidyr_1.3.0
[101] DBI_1.1.3 dbplyr_2.3.0
[103] rappdirs_0.3.3 Matrix_1.5-3
[105] car_3.1-1 cli_3.6.0
[107] parallel_4.2.1 GenomicRanges_1.50.2
[109] pkgconfig_2.0.3 GenomicAlignments_1.34.0
[111] xml2_1.3.3 svglite_2.1.1
[113] bslib_0.4.2 webshot_0.5.4
[115] XVector_0.38.0 rvest_1.0.3
[117] stringr_1.5.0 callr_3.7.3
[119] digest_0.6.31 showtextdb_3.0
[121] Biostrings_2.66.0 rmarkdown_2.20
[123] tximeta_1.16.1 restfulr_0.0.15
[125] curl_5.0.0 shiny_1.7.4
[127] Rsamtools_2.14.0 gtools_3.9.4
[129] rjson_0.2.21 lifecycle_1.0.3
[131] jsonlite_1.8.4 Rhdf5lib_1.20.0
[133] carData_3.0-5 desc_1.4.2
[135] viridisLite_0.4.1 fansi_1.0.4
[137] pillar_1.8.1 lattice_0.20-45
[139] KEGGREST_1.38.0 fastmap_1.1.0
[141] httr_1.4.4 pkgbuild_1.4.0
[143] interactiveDisplayBase_1.36.0 glue_1.6.2
[145] remotes_2.4.2 png_0.1-8
[147] BiocVersion_3.16.0 bit_4.0.5
[149] stringi_1.7.12 sass_0.4.1
[151] profvis_0.3.7 blob_1.2.3
[153] textshaping_0.3.6 AnnotationHub_3.6.0
[155] memoise_2.0.1 dplyr_1.1.0
[157] sysfonts_0.8.8