Last updated: 2021-08-31
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Knit directory: KEJP_2020_splatPop/
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suppressPackageStartupMessages({
library(splatter)
library(VariantAnnotation)
library(tidyverse)
library(RColorBrewer)
library(scater)
library(ggpubr)
library(cowplot)
})
source("code/plot_functions.R")
source("code/misc_functions.R")date <- Sys.Date()
save <- TRUEn.inds <- c(10, 100, 500)
n.genes <- c(10, 100, 500, 1000, 10000, 20000)
params <- newSplatPopParams()
res <- list()
x <- 0
for (i in 1:length(n.inds)){
for (j in 1:length(n.genes)){
x <- x+1
nI <- n.inds[i]
nG <- n.genes[j]
vcf <- mockVCF(n.samples = nI)
gff <- mockGFF(n.genes = nG)
start_time <- Sys.time()
sim <- splatPopSimulate(params = params, vcf = vcf,
gff = gff, sparsify = FALSE)
t <- Sys.time() - start_time
t <- as.numeric(t, units = "secs")
rm(sim)
res[[x]] <- data.frame(list(individuals=nI, genes=nG,
time=t))
}}Designing population...Simulating gene means for population...Simulating sc counts for Group1...Done!Designing population...Simulating gene means for population...Simulating sc counts for Group1...Done!Designing population...Simulating gene means for population...Simulating sc counts for Group1...Done!Designing population...Simulating gene means for population...Simulating sc counts for Group1...Done!Designing population...Simulating gene means for population...Simulating sc counts for Group1...Done!Designing population...Simulating gene means for population...Simulating sc counts for Group1...Done!Designing population...Simulating gene means for population...Simulating sc counts for Group1...Done!Designing population...Simulating gene means for population...Simulating sc counts for Group1...Done!Designing population...Simulating gene means for population...Simulating sc counts for Group1...Done!Designing population...Simulating gene means for population...Simulating sc counts for Group1...Done!Designing population...Simulating gene means for population...Simulating sc counts for Group1...Done!Designing population...Simulating gene means for population...Simulating sc counts for Group1...Done!Designing population...Simulating gene means for population...Simulating sc counts for Group1...Done!Designing population...Simulating gene means for population...Simulating sc counts for Group1...Done!Designing population...Simulating gene means for population...Simulating sc counts for Group1...Done!Designing population...Simulating gene means for population...Simulating sc counts for Group1...Done!Designing population...Simulating gene means for population...Simulating sc counts for Group1...Done!Designing population...Simulating gene means for population...Simulating sc counts for Group1...Done!timingResults <- do.call(rbind, res)
timingResults individuals genes time
1 10 10 3.092396
2 10 100 5.201914
3 10 500 18.908612
4 10 1000 35.706014
5 10 10000 348.030771
6 10 20000 702.591504
7 100 10 18.189312
8 100 100 20.507371
9 100 500 33.795551
10 100 1000 50.405751
11 100 10000 365.016927
12 100 20000 727.045039
13 500 10 85.566283
14 500 100 89.020252
15 500 500 105.179279
16 500 1000 125.512826
17 500 10000 520.239532
18 500 20000 973.510018n.inds <- c(100)
n.genes <- c(10, 100, 500, 1000, 10000, 20000)
params <- newSplatPopParams()
params <- setParams(params, list(batchCells=100, batch.size=10))
res <- list()
x <- 0
for (i in 1:length(n.inds)){
for (j in 1:length(n.genes)){
x <- x+1
nI <- n.inds[i]
nG <- n.genes[j]
vcf <- mockVCF(n.samples = nI)
gff <- mockGFF(n.genes = nG)
start_time <- Sys.time()
sim <- splatPopSimulate(params = params, vcf = vcf,
gff = gff, sparsify = FALSE)
t <- Sys.time() - start_time
t <- as.numeric(t, units = "secs")
rm(sim)
res[[x]] <- data.frame(list(individuals=nI, genes=nG,
time=t))
}}Designing population...Simulating gene means for population...Simulating sc counts for Group1...Done!Designing population...Simulating gene means for population...Simulating sc counts for Group1...Done!Designing population...Simulating gene means for population...Simulating sc counts for Group1...Done!Designing population...Simulating gene means for population...Simulating sc counts for Group1...Done!Designing population...Simulating gene means for population...Simulating sc counts for Group1...Done!Designing population...Simulating gene means for population...Simulating sc counts for Group1...Done!timingResults <- do.call(rbind, res)
timingResults individuals genes time
1 100 10 19.99916
2 100 100 20.15544
3 100 500 33.93398
4 100 1000 49.02730
5 100 10000 355.44765
6 100 20000 719.49983
devtools::session_info()─ Session info ───────────────────────────────────────────────────────────────
setting value
version R version 4.0.4 (2021-02-15)
os Red Hat Enterprise Linux 8.4 (Ootpa)
system x86_64, linux-gnu
ui X11
language (EN)
collate en_US.UTF-8
ctype en_US.UTF-8
tz Australia/Melbourne
date 2021-08-31
─ Packages ───────────────────────────────────────────────────────────────────
package * version date lib source
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BiocParallel 1.24.1 2020-11-06 [1] Bioconductor
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bit 4.0.4 2020-08-04 [1] CRAN (R 4.0.2)
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blob 1.2.2 2021-07-23 [1] CRAN (R 4.0.4)
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DelayedMatrixStats 1.12.3 2021-02-03 [1] Bioconductor
desc 1.3.0 2021-03-05 [1] CRAN (R 4.0.4)
devtools 2.4.2 2021-06-07 [1] CRAN (R 4.0.4)
digest 0.6.27 2020-10-24 [1] CRAN (R 4.0.2)
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jsonlite 1.7.2 2020-12-09 [1] CRAN (R 4.0.4)
knitr 1.33 2021-04-24 [1] CRAN (R 4.0.4)
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zlibbioc 1.36.0 2020-10-27 [1] Bioconductor
[1] /mnt/mcfiles/cazodi/R/x86_64-pc-linux-gnu-library/4.0
[2] /opt/R/4.0.4/lib/R/library