10x Fibroblast: Simulations
Christina B. Azodi
2021-11-18
Last updated: 2021-11-18
Checks: 6 1
Knit directory: KEJP_2020_splatPop/
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These are the previous versions of the repository in which changes were made to the R Markdown (analysis/10x-Fibrob_simulations.Rmd) and HTML (public/10x-Fibrob_simulations.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), 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 | 15690aa | cazodi | 2021-11-15 | update mean-variance-plots to have same y axis |
| html | 15690aa | cazodi | 2021-11-15 | update mean-variance-plots to have same y axis |
| Rmd | 116e4a3 | cazodi | 2021-10-08 | update mean-variance plots for manuscript |
| html | 116e4a3 | cazodi | 2021-10-08 | update mean-variance plots for manuscript |
| Rmd | 3f88d86 | cazodi | 2021-06-02 | conditional simulations added |
| html | 3f88d86 | cazodi | 2021-06-02 | conditional simulations added |
suppressPackageStartupMessages({
library(SingleCellExperiment)
library(scater)
library(tidyverse)
library(splatter)
library(VariantAnnotation)
library(cluster)
library(fitdistrplus)
library(RColorBrewer)
library(ggpubr)
library(cowplot)
})
source("code/plot_functions.R")
source("code/misc_functions.R")date <- Sys.Date()
set.seed(42)
n.genes <- 504
save <- TRUE
rerun <- TRUE
date.use <- "2021-11-18"
nSamples <- 6
sample.colors <- projectColors("samples")
# Chromosome 22 data
gff <- read.table("references/chr22.genes.gff3", sep="\t", header=FALSE, quote="")
vcf <- readVcf("references/chr22.filtered.vcf", "hg38")
sampleNames <- colnames(geno(vcf)$GT)
# 10x Fibroblast data and splatPopParams
sce <- readRDS("data/sce_10X-fibroblasts.rds")
params <- readRDS("output/01_sims/splatPop-params_10x-fibroblasts.rds")
params@eqtl.coreg <- 0Conditional effects
Simple example
vcf.simple <- vcf[, sample(sampleNames, nSamples)]
if(rerun) {
params.simple <- setParams(params, batchCells = 50,
eqtl.n = 0.5,
condition.prob = c(0.5, 0.5),
cde.prob = 0.25,
cde.facLoc = 0.5,
cde.facScale = 0.5)
sim.simple <- splatPopSimulate(vcf = vcf.simple, gff = gff,
params = params.simple,
sparsify = FALSE)
} else{
sim.simple <- readRDS(paste0("output/01_sims/", date.use,
"_example-conditions.rds"))
}Designing population...Simulating data for genes in GFF...Simulating gene means for population...Simulating sc counts for Group1...Done!plotSims(sim=sim.simple, variables = c("Sample", "Condition"),
colour_by = "Sample", shape_by= "Condition")Scale for 'colour' is already present. Adding another scale for 'colour',
which will replace the existing scale.Scale for 'colour' is already present. Adding another scale for 'colour',
which will replace the existing scale.
if(save){
save.name <- paste0(date, "_example-conditions")
saveRDS(sim.simple, paste0("output/01_sims/", save.name, ".rds"))
ggsave(paste0("output/00_Figures/", save.name, ".pdf"), width = 3, height = 5)
}10x-Fibroblast Conditional effects
To simplify the example, we simulate data for 6 individuals (3 “healthy” and 3 “IPF”) and compare these simulations to six reference individuals. To speed up the plotting, we also down sample the number of cells included for each sample to n=50.
sce6 <- subset(sce, , Sample != "947172" & Sample != "947174")
nSamples <- length(unique(sce6$Sample))
nCells <- round(mean(table(sce6$Sample)))
message("Average nCells in the empirical data: ", nCells)Average nCells in the empirical data: 302if(rerun){
vcf.cond <- vcf[, sample(sampleNames, nSamples)]
params.cond <- setParams(params,
similarity.scale = 2,
batchCells = nCells,
# parameters specifying effects
condition.prob = c(0.5, 0.5),
cde.prob = 0.5,
cde.facLoc = 0.2,
cde.facScale = 0.2)
sim.cond <- splatPopSimulate(vcf = vcf.cond, gff = gff,
params = params.cond, sparsify = FALSE)
} else{
sim.cond <- readRDS(paste0("output/01_sims/", date.use, "_10x-condition.rds"))
}Designing population...Simulating data for genes in GFF...Simulating gene means for population...Simulating sc counts for Group1...Done!plotComparisons(sim=sim.cond, emp=sce6, maxCells = 50, samp.col = sample.colors,
variables = c("Sample", "Condition"),
colour_by = "Sample", shape_by= "Condition",
mv.nCells = c(5, 20, 50), mv.y.max = 8.5)`geom_smooth()` using formula 'y ~ x'`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'Scale for 'colour' is already present. Adding another scale for 'colour',
which will replace the existing scale.
Scale for 'colour' is already present. Adding another scale for 'colour',
which will replace the existing scale.
Scale for 'colour' is already present. Adding another scale for 'colour',
which will replace the existing scale.
Scale for 'colour' is already present. Adding another scale for 'colour',
which will replace the existing scale.
if(save){
save.name <- paste0(date, "_10x-condition")
saveRDS(sim.cond, paste0("output/01_sims/", save.name, ".rds"))
ggsave(paste0("output/00_Figures/", save.name, ".pdf"), width = 6, height = 7)
}tSNE plot comparisons
While the PCA plots are useful for showing global relationships between cells, UMAP and tSNE dimension reduction methods have become the norm for visualizing single-cell RNA-sequencing data.
pSimC <- plotTSNEx(sim.cond, colour_by="Sample", shape_by="Condition",
maxCells = 50, samp.col = sample.colors) Scale for 'colour' is already present. Adding another scale for 'colour',
which will replace the existing scale.pEmp <- plotTSNEx(sce6, colour_by="Sample", shape_by="Condition",
maxCells = 50, samp.col = sample.colors) Scale for 'colour' is already present. Adding another scale for 'colour',
which will replace the existing scale.plot_grid(pEmp, pSimC, ncol=2, labels = "auto")
| Version | Author | Date |
|---|---|---|
| 15690aa | cazodi | 2021-11-15 |
if(save){
save.name <- paste0(date, "_10x-condition-TSNEs")
ggsave(paste0("output/00_Figures/", save.name, ".pdf"), width = 6)
}Saving 6 x 5 in image
devtools::session_info()─ Session info ──────────────────────────────────────────────────────────────
hash: place of worship, raised hand, family: woman, girl, boy
setting value
version R version 4.1.1 (2021-08-10)
os Rocky Linux 8.4 (Green Obsidian)
system x86_64, linux-gnu
ui X11
language (EN)
collate en_AU.UTF-8
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tz Australia/Melbourne
date 2021-11-18
pandoc 2.11.4 @ /usr/lib/rstudio-server/bin/pandoc/ (via rmarkdown)
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BSgenome 1.62.0 2021-10-26 [1] Bioconductor
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callr 3.7.0 2021-04-20 [1] CRAN (R 4.1.1)
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htmltools 0.5.2 2021-08-25 [1] CRAN (R 4.0.4)
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rsvd 1.0.5 2021-04-16 [1] CRAN (R 4.1.1)
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[1] /mnt/mcfiles/cazodi/R/x86_64-pc-linux-gnu-library/4.1
[2] /opt/R/4.1.1/lib/R/library
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