Simulation study
Pedro L. Baldoni
08 April, 2025
Last updated: 2025-04-08
Checks: 7 0
Knit directory: DTU-code/analysis/
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Unstaged changes:
Modified: misc/mouse.Rmd/Figure-CaseStudy.pdf
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 | 76ed87c | Pedro Baldoni | 2025-03-18 | Adding analysis reports |
Introduction
Setup
knitr::opts_chunk$set(dev = "png",
dpi = 300,
dev.args = list(type = "cairo-png"),
root.dir = '.',
autodep = TRUE)
options(knitr.kable.NA = "-")library(data.table)
library(magrittr)
library(ggplot2)
library(patchwork)
library(plyr)
library(kableExtra)
devtools::load_all('../code/pkg')path.misc <- file.path('../misc/simulation-paper.Rmd')
dir.create(path.misc,recursive = TRUE,showWarnings = FALSE)
# Gene-level stats
path.fdr <-
list.files('../output/simulation/summary','fdr.gene.tsv.gz',recursive = TRUE,full.names = TRUE)
path.metrics <-
list.files('../output/simulation/summary','metrics.gene.tsv.gz',recursive = TRUE,full.names = TRUE)
path.quantile <-
list.files('../output/simulation/summary','quantile.gene.tsv.gz',recursive = TRUE,full.names = TRUE)
path.pvalue <-
list.files('../output/simulation/summary','pvalue.gene.tsv.gz',recursive = TRUE,full.names = TRUE)
path.roc <-
list.files('../output/simulation/summary','roc.gene.tsv.gz',recursive = TRUE,full.names = TRUE)
# Transcript-level stats
path.fdr.tx <-
list.files('../output/simulation/summary','fdr.transcript.tsv.gz',recursive = TRUE,full.names = TRUE)
path.metrics.tx <-
list.files('../output/simulation/summary','metrics.transcript.tsv.gz',recursive = TRUE,full.names = TRUE)
path.quantile.tx <-
list.files('../output/simulation/summary','quantile.transcript.tsv.gz',recursive = TRUE,full.names = TRUE)
path.pvalue.tx <-
list.files('../output/simulation/summary','pvalue.transcript.tsv.gz',recursive = TRUE,full.names = TRUE)
path.roc.tx <-
list.files('../output/simulation/summary','roc.transcript.tsv.gz',recursive = TRUE,full.names = TRUE)
# Time stats
path.time <-
list.files('../output/simulation/summary','^time.tsv.gz',recursive = TRUE,full.names = TRUE)bs <- 8
cnames <- c('Genome','Length','FC','Reads','Scenario','LibsPerGroup','Quantifier','Method','Feature')Data loading
relabel <- function(x,feature = NULL){
y <- copy(x)
if(!is.null(feature)) y$Feature <- feature
y$LibsPerGroup %<>% factor(levels = paste0(c(3, 5, 10), 'libsPerGroup'),labels = paste0(c(3,5,10),' samples per group')) %<>% droplevels()
y$Length %<>% factor(levels = paste0('readlen-', seq(50, 150, 25)),labels = paste0(seq(50, 150, 25), 'bp')) %<>% droplevels()
y$Scenario %<>% factor(levels = c('balanced','unbalanced'),labels = c('Equal library sizes','Unequal library sizes'))
if (!is.null(x$Sample)) {
browser()
y$Sample %<>% factor(levels = c(paste0("groupA_rep",1:10,"_R1"),paste0("groupB_rep",1:10,"_R1")),labels = c(paste0("A",1:10),paste0("B",1:10))) %<>% droplevels()
}
return(y)
}
# Loading datasets
dt.fdr <- do.call(rbind,lapply(path.fdr,fread)) %>% relabel(feature = 'Gene')
dt.metrics <- do.call(rbind,lapply(path.metrics,fread)) %>% relabel(feature = 'Gene')
dt.quantile <- do.call(rbind,lapply(path.quantile,fread)) %>% relabel(feature = 'Gene')
dt.pvalue <- do.call(rbind,lapply(path.pvalue,fread)) %>% relabel(feature = 'Gene')
dt.roc <- do.call(rbind,lapply(path.roc,fread)) %>% relabel(feature = 'Gene')
dt.fdr.tx <- do.call(rbind,lapply(path.fdr.tx,fread)) %>% relabel(feature = 'Transcript')
dt.metrics.tx <- do.call(rbind,lapply(path.metrics.tx,fread)) %>% relabel(feature = 'Transcript')
dt.quantile.tx <- do.call(rbind,lapply(path.quantile.tx,fread)) %>% relabel(feature = 'Transcript')
dt.pvalue.tx <- do.call(rbind,lapply(path.pvalue.tx,fread)) %>% relabel(feature = 'Transcript')
dt.roc.tx <- do.call(rbind,lapply(path.roc.tx,fread)) %>% relabel(feature = 'Transcript')
dt.time <- do.call(rbind,lapply(path.time,fread)) %>% relabel()meth <- c('edgeR-scaled-F',
'edgeR-scaled-Simes',
'limma-scaled-F',
'limma-scaled-Simes',
'DEXSeq-raw',
'satuRn-raw',
'DRIMSeq-raw')
meth.lab <- c('edgeR',
'edgeR-Simes',
'limma',
'limma-Simes',
'DEXSeq',
'satuRn',
'DRIMSeq')Power
dt.power <- rbind(dt.metrics[FC == 'fc2' & Method %in% meth,],
dt.metrics.tx[FC == 'fc2' & Method %in% meth,],
fill = TRUE)
dt.power$Method %<>% mapvalues(from = meth,to = meth.lab)
dt.power[, FDR := roundPretty(ifelse((FP+TP) == 0,0,100*FP/(FP+TP)),1)]
x.melt <- melt(dt.power,
id.vars = cnames,
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,library,scenario,feature,legend = FALSE, base_size = bs,maxy = 4000,...){
tb.bar <- df.bar[LibsPerGroup == library & Scenario == scenario & Feature == feature,]
tb.txt <- df.txt[LibsPerGroup == library & Scenario == scenario & Feature == feature,][FDR != 'NA',]
gap <- 0.05*max(dt.power$TP + dt.power$FP)
ggplot(tb.bar,aes(x = Method,y = Value,fill = Type)) +
geom_col() +
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('salmon','lightblue')) +
labs(x = NULL,...) +
coord_cartesian(ylim = c(0,maxy)) +
theme_bw(base_size = base_size,base_family = 'sans') +
theme(panel.grid = element_blank(),
axis.text.x = element_text(angle = 90,hjust = 1, vjust = 0.5),
axis.line = element_line(colour = 'black'),
axis.text = element_text(colour = 'black',size = base_size),
axis.title = 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(0.85,0.85),
legend.direction = 'vertical',
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,
library = '3 samples per group',
scenario = 'Unequal library sizes',
feature = 'Gene',
y = 'Genes with FDR < 0.05')
fig.power.b <- plot.power(df.bar = x.melt,df.txt = dt.power,
library = '5 samples per group',
scenario = 'Unequal library sizes',
feature = 'Gene',
y = 'Genes with FDR < 0.05')
fig.power.c <- plot.power(df.bar = x.melt,df.txt = dt.power,
library = '10 samples per group',
scenario = 'Unequal library sizes',
feature = 'Gene',
y = 'Genes with FDR < 0.05')
fig.power.tx.d <- plot.power(df.bar = x.melt,df.txt = dt.power,
library = '3 samples per group',
scenario = 'Unequal library sizes',
feature = 'Transcript',
y = 'Transcripts with FDR < 0.05',maxy = 9000)
fig.power.tx.e <- plot.power(df.bar = x.melt,df.txt = dt.power,
library = '5 samples per group',
scenario = 'Unequal library sizes',
feature = 'Transcript',
y = 'Transcripts with FDR < 0.05',maxy = 9000)
fig.power.tx.f <- plot.power(df.bar = x.melt,df.txt = dt.power,
library = '10 samples per group',
scenario = 'Unequal library sizes',
feature = 'Transcript',
y = 'Transcripts with FDR < 0.05',maxy = 9000)
fig.power <- wrap_plots(A = fig.power.a,
B = fig.power.b,
C = fig.power.c,
D = fig.power.tx.d,
E = fig.power.tx.e,
`F` = fig.power.tx.f,
design = c(area(1,1),area(1,2),area(1,3),
area(2,1),area(2,2),area(2,3))) +
plot_annotation(tag_levels = 'a')
fig.power
FDR
dt.fdr.plot <- rbind(dt.fdr[FC == 'fc2' & Method %in% meth,],
dt.fdr.tx[FC == 'fc2' & Method %in% meth,])
dt.fdr.plot$Method %<>% mapvalues(from = meth,to = meth.lab)
dt.fdr.plot$Method %<>% factor(levels = meth.lab[c(5,6,7,1,2,3,4)])
colmeth <- c('orange','darkorange','blue','darkblue','red','green3','black')
names(colmeth) <- meth.lab
colmeth <- colmeth[order(names(colmeth))]
plot.fdr <- function(df.line,scenario,library,legend = FALSE,base_size = bs,
color = colmeth,feature,ymax = NULL,xmax = NULL,x,y){
tb.bar <- df.line[Scenario == scenario & LibsPerGroup == library & Feature == feature,]
p <- ggplot(tb.bar,aes(x = N,y = FDR,color = Method)) +
geom_line(linewidth = 0.5) +
scale_color_manual(values = colmeth,
breaks = names(colmeth)) +
theme_bw(base_size = base_size,base_family = 'sans') +
theme(panel.grid = element_blank(),
axis.line = element_line(colour = 'black'),
axis.text = element_text(colour = 'black',size = base_size),
axis.title = 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 = "inside",
legend.position.inside = c(0.35,0.7),
legend.text = element_text(size = base_size),
legend.title = element_blank(),
legend.key.size = unit(0.75,"line")) else theme(legend.position = 'none')
if(feature == 'Gene') p <- p + coord_cartesian(ylim = c(0,ymax),xlim = c(0,xmax))
p + labs(x = x,y = y)
}
fig.fdr.a <- plot.fdr(df.line = dt.fdr.plot,
scenario = 'Unequal library sizes',
library = '3 samples per group',
feature = 'Gene',
ymax = 1000,
xmax = 2500,
y = 'False discoveries',
x = 'Genes chosen')
fig.fdr.b <- plot.fdr(df.line = dt.fdr.plot,
scenario = 'Unequal library sizes',
library = '5 samples per group',
feature = 'Gene',
ymax = 400,
xmax = 2500,
y = 'False discoveries',
x = 'Genes chosen')
fig.fdr.c <- plot.fdr(df.line = dt.fdr.plot,
scenario = 'Unequal library sizes',
library = '10 samples per group',
feature = 'Gene',
ymax = 200,
xmax = 2500,
y = 'False discoveries',
x = 'Genes chosen',
legend = TRUE)
fig.fdr.tx.un.d <- plot.fdr(df.line = dt.fdr.plot,
scenario = 'Unequal library sizes',
library = '3 samples per group',
feature = 'Transcript',
y = 'False discoveries',
x = 'Transcripts chosen')
fig.fdr.tx.un.e <- plot.fdr(df.line = dt.fdr.plot,
scenario = 'Unequal library sizes',
library = '5 samples per group',
feature = 'Transcript',
y = 'False discoveries',
x = 'Transcripts chosen')
fig.fdr.tx.un.f <- plot.fdr(df.line = dt.fdr.plot,
scenario = 'Unequal library sizes',
library = '10 samples per group',
feature = 'Transcript',
y = 'False discoveries',
x = 'Transcripts chosen')
fig.fdr <- wrap_plots(A = fig.fdr.a,
B = fig.fdr.b,
C = fig.fdr.c,
D = fig.fdr.tx.un.d,
E = fig.fdr.tx.un.e,
`F` = fig.fdr.tx.un.f,
design = c(area(1,1),area(1,2),area(1,3),
area(2,1),area(2,2),area(2,3))) +
plot_annotation(tag_levels = 'a')
fig.fdr
P-values
# Only edgeR, limma, and DRIMSeq output gene-level raw p-values
meth.null <- c("edgeR-scaled-F","edgeR-scaled-Simes","limma-scaled-F","limma-scaled-Simes","DRIMSeq-raw")
dt.p <-
rbind(dt.pvalue[FC == 'fc1' & Method %in% meth.null,],
dt.pvalue.tx[FC == 'fc1' & Method %in% meth,])
dt.p$Method %<>% mapvalues(from = meth,to = meth.lab)
dt.p$Method %<>% factor(levels = names(colmeth))
plot.phist <- function(df.line,scenario,library,feature,
base_size = bs,...){
tb.bar <- df.line[Scenario == scenario & LibsPerGroup == library & Feature == feature,]
plot <- ggplot(data = tb.bar,aes(x = PValue,y = Density.Avg)) +
facet_grid(cols = vars(Method)) +
geom_col(col = 'black',fill = 'grey',linewidth = 0) +
geom_hline(yintercept = 1,col = 'red',linetype = 'dashed') +
theme_bw(base_size = base_size,base_family = 'sans') +
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)) +
theme(strip.text.x = element_text(colour = 'black',size = base_size),
strip.background.x = element_blank(),
panel.grid = element_blank(),
axis.line = element_line(colour = 'black'),
axis.text = element_text(colour = 'black',size = base_size),
axis.title = element_text(colour = 'black',size = base_size)) +
labs(x = 'P-values',y = 'Density')
return(plot)
}
fig.phist <- plot.phist(df.line = dt.p,
scenario = 'Unequal library sizes',
library = '5 samples per group',
feature = 'Gene')
fig.tx.phist <- plot.phist(df.line = dt.p,
scenario = 'Unequal library sizes',
library = '5 samples per group',
feature = 'Transcript')
fig.phist.panel <- wrap_plots(A = fig.phist,
B = fig.tx.phist,
design = c(area(1,1),area(2,1))) +
plot_annotation(tag_levels = 'a')
fig.phist.panel
Computing time
dt.time.plot <- dt.time[FC == 'fc2' & Method %in% meth,]
dt.time.plot$Method %<>% mapvalues(from = meth,to = meth.lab)
dt.time.plot$Method %<>% factor(levels = names(colmeth))
plot.time <- function(df.line,scenario,library,feature,
base_size = bs,y.max = 12.6,...){
tb.bar <- df.line[Scenario == scenario & LibsPerGroup == library,]
plot <- ggplot(data = tb.bar,aes(x = Method,y = Time)) +
geom_bar(stat = 'identity',position = position_dodge(),
fill = 'lightblue',color = NA) +
theme_bw(base_size = base_size,base_family = 'sans') +
scale_y_continuous(limits = c(0,y.max)) +
theme(panel.grid = element_blank(),
axis.text.x = element_text(angle = 90),
axis.text = element_text(colour = 'black',size = base_size),
axis.title = element_text(colour = 'black',size = base_size)) +
labs(y = 'Time (min)',x = NULL)
return(plot)
}
fig.time.a <- plot.time(df.line = dt.time.plot,
scenario = 'Unequal library sizes',
library = '3 samples per group')
fig.time.b <- plot.time(df.line = dt.time.plot,
scenario = 'Unequal library sizes',
library = '5 samples per group')
fig.time.c <- plot.time(df.line = dt.time.plot,
scenario = 'Unequal library sizes',
library = '10 samples per group')
fig.time <- wrap_plots(A = fig.time.a,
B = fig.time.b,
C = fig.time.c,
design = c(area(1,1),area(1,2),area(1,3))) +
plot_annotation(tag_levels = 'a')
fig.time
Output files
ggsave(plot = fig.power,filename = file.path(path.misc,'Figure-Power.pdf'),
device = 'pdf',width = 7.5,height = 5,units = 'in')
ggsave(plot = fig.fdr,filename = file.path(path.misc,'Figure-FDR.pdf'),
device = 'pdf',width = 7.5,height = 5,units = 'in')
ggsave(plot = fig.phist.panel,filename = file.path(path.misc,'Figure-Pval.pdf'),
device = 'pdf',width = 7.5,height = 5,units = 'in')
ggsave(plot = fig.time,filename = file.path(path.misc,'Figure-Time.pdf'),
device = 'pdf',width = 7.5,height = 2.5,units = 'in')
sessionInfo()R version 4.4.1 (2024-06-14)
Platform: x86_64-pc-linux-gnu
Running under: Red Hat Enterprise Linux 9.3 (Plow)
Matrix products: default
BLAS: /stornext/System/data/software/rhel/9/base/tools/R/4.4.1/lib64/R/lib/libRblas.so
LAPACK: /stornext/System/data/software/rhel/9/base/tools/R/4.4.1/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: 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_1.17.0 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] splines_4.4.1 later_1.4.1
[3] BiocIO_1.14.0 bitops_1.0-9
[5] filelock_1.0.3 R.oo_1.27.0
[7] tibble_3.2.1 XML_3.99-0.18
[9] lifecycle_1.0.4 httr2_1.1.0
[11] edgeR_4.5.9 rprojroot_2.0.4
[13] processx_3.8.6 lattice_0.22-6
[15] ensembldb_2.28.1 limma_3.63.9
[17] sass_0.4.9 rmarkdown_2.29
[19] jquerylib_0.1.4 yaml_2.3.10
[21] remotes_2.5.0 httpuv_1.6.15
[23] sessioninfo_1.2.3 pkgbuild_1.4.6
[25] pbapply_1.7-2 RColorBrewer_1.1-3
[27] DBI_1.2.3 abind_1.4-8
[29] pkgload_1.4.0 zlibbioc_1.50.0
[31] GenomicRanges_1.56.2 R.utils_2.13.0
[33] purrr_1.0.4 AnnotationFilter_1.28.0
[35] BiocGenerics_0.50.0 RCurl_1.98-1.16
[37] rappdirs_0.3.3 git2r_0.35.0
[39] GenomeInfoDbData_1.2.12 DEXSeq_1.50.0
[41] wasabi_1.0.1 IRanges_2.38.1
[43] S4Vectors_0.42.1 genefilter_1.86.0
[45] fishpond_2.10.0 annotate_1.82.0
[47] svglite_2.1.3 codetools_0.2-20
[49] DelayedArray_0.30.1 xml2_1.3.7
[51] tidyselect_1.2.1 locfdr_1.1-8
[53] UCSC.utils_1.0.0 farver_2.1.2
[55] satuRn_1.12.0 matrixStats_1.5.0
[57] stats4_4.4.1 BiocFileCache_2.12.0
[59] GenomicAlignments_1.40.0 jsonlite_1.9.1
[61] ellipsis_0.3.2 survival_3.6-4
[63] systemfonts_1.2.1 tools_4.4.1
[65] progress_1.2.3 ragg_1.3.3
[67] Rcpp_1.0.14 glue_1.8.0
[69] svMisc_1.4.3 SparseArray_1.4.8
[71] DESeq2_1.44.0 xfun_0.51
[73] MatrixGenerics_1.16.0 usethis_3.1.0
[75] GenomeInfoDb_1.40.1 dplyr_1.1.4
[77] withr_3.0.2 BiocManager_1.30.25
[79] fastmap_1.2.0 boot_1.3-30
[81] rhdf5filters_1.16.0 callr_3.7.6
[83] digest_0.6.37 R6_2.6.1
[85] mime_0.12 textshaping_1.0.0
[87] colorspace_2.1-1 gtools_3.9.5
[89] biomaRt_2.60.1 RSQLite_2.3.9
[91] R.methodsS3_1.8.2 generics_0.1.3
[93] renv_1.1.2 tximeta_1.22.1
[95] rtracklayer_1.64.0 prettyunits_1.2.0
[97] httr_1.4.7 htmlwidgets_1.6.4
[99] S4Arrays_1.4.1 whisker_0.4.1
[101] pkgconfig_2.0.3 gtable_0.3.6
[103] blob_1.2.4 hwriter_1.3.2.1
[105] SingleCellExperiment_1.26.0 XVector_0.44.0
[107] htmltools_0.5.8.1 geneplotter_1.82.0
[109] profvis_0.4.0 ProtGenerics_1.36.0
[111] sleuth_0.30.1 scales_1.3.0
[113] Biobase_2.64.0 Rsubread_2.18.0
[115] png_0.1-8 knitr_1.49
[117] rstudioapi_0.17.1 reshape2_1.4.4
[119] tzdb_0.4.0 rjson_0.2.23
[121] curl_6.2.1 cachem_1.1.0
[123] rhdf5_2.48.0 stringr_1.5.1
[125] BiocVersion_3.19.1 parallel_4.4.1
[127] miniUI_0.1.1.1 AnnotationDbi_1.66.0
[129] restfulr_0.0.15 desc_1.4.3
[131] pillar_1.10.1 grid_4.4.1
[133] vctrs_0.6.5 urlchecker_1.0.1
[135] promises_1.3.2 dbplyr_2.5.0
[137] xtable_1.8-4 tximport_1.32.0
[139] evaluate_1.0.3 readr_2.1.5
[141] GenomicFeatures_1.56.0 locfit_1.5-9.12
[143] cli_3.6.4 compiler_4.4.1
[145] Rsamtools_2.20.0 rlang_1.1.5
[147] crayon_1.5.3 labeling_0.4.3
[149] ps_1.9.0 getPass_0.2-4
[151] fs_1.6.5 stringi_1.8.4
[153] viridisLite_0.4.2 BiocParallel_1.38.0
[155] txdbmaker_1.0.1 munsell_0.5.1
[157] Biostrings_2.72.1 lazyeval_0.2.2
[159] devtools_2.4.5 Matrix_1.7-0
[161] hms_1.1.3 bit64_4.6.0-1
[163] Rhdf5lib_1.26.0 KEGGREST_1.44.1
[165] statmod_1.5.0 shiny_1.10.0
[167] DRIMSeq_1.32.0 SummarizedExperiment_1.34.0
[169] AnnotationHub_3.12.0 memoise_2.0.1
[171] thematic_0.1.6 bslib_0.9.0
[173] bit_4.6.0