Misc: Multi-chemistry batches
Christina B. Azodi
2021-08-31
Last updated: 2021-08-31
Checks: 6 1
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")sample.colors <- projectColors("samples")
type.col <- c("#44bb99", "#aa3377")
save <- TRUE
date <- Sys.Date()
set.seed(42)
# Chromosome 22 data
gff <- read.table("references/chr22.genes.gff3", sep="\t", header=FALSE, quote="")
vcf <- readVcf("references/chr22.filtered.vcf", "hg38")
colnames(vcf) <- gsub("_.*", "", colnames(vcf))
sampleNames <- colnames(vcf)
vcf.6 <- vcf[, sample(sampleNames, 6)]Multi-chemistry/platform batch effects
Because splatPop is a modular and reproducible simulation framework, it is possible to simulate multiple populations of cells and merge them together to create more complex batch effects. For example, if you wanted to simulate cells sequenced using different platforms or chemistries, you could simulate the first to create a splatPop key, and then use that splatPop key as input with other splatPopParams specifying different single cell properties.
The following code demonstrates how this could be done by showing an extreme example, a simulated dataset with cells sequenced using either smartseq2 or 10x platforms. First, two separate splatPopParams objects are needed, one for each chemistry. Note that we want to make sure the pop.quant.norm parameter is set to TRUE, as we want the simulated gene means to be quantile normalized for each sample to fit the distribution of the single cell data in the respective splatPopParams object. Then we use splatPopSimulateMeans to generate the splatPop key using one of the splatPopParams objects (order does not matter), that key will be used as input for the rest of the simulations. Then, we use splatPopSimulateSC to simulate the single cells for that first splatPopParams object. Finally, for all other populations we want to simulate, we simply run splatPopSimulate (which wraps the simulate means and SC function together).
# Set up splatPopParams
paramsSS2 <- readRDS("output/01_sims/splatPop-params_iPSC-ss2_sc.rds")
params10x <- readRDS("output/01_sims/splatPop-params_Neuro-10x.rds")
paramsSS2@eqtl.coreg <- 0
params10x@eqtl.coreg <- 0
paramsSS2 <- setParams(paramsSS2, pop.quant.norm = TRUE)
params10x <- setParams(params10x, pop.quant.norm = TRUE)
sim.mean.ss2 <- splatPopSimulateMeans(vcf = vcf.6, gff = gff,
params = paramsSS2)Simulating gene means for population...sim.ss2 <- splatPopSimulateSC(params = paramsSS2, key=sim.mean.ss2$key,
sim.means=sim.mean.ss2$means, batchCells=20,
sparsify = FALSE)Simulating sc counts for Group1...Done!sim.ss2$Batch <- "smartseq2"
sim.10x <- splatPopSimulate(vcf = vcf.6, key=sim.mean.ss2$key,
params = params10x, batchCells=20,
sparsify = FALSE)Designing population...Simulating gene means for population...Simulating sc counts for Group1...Done!sim.10x$Batch <- "10x"
sim.joint <- SingleCellExperiment::cbind(sim.ss2, sim.10x)
sim.jointclass: SingleCellExperiment
dim: 504 240
metadata(4): Params Simulated_Means Params Simulated_Means
assays(6): BatchCellMeans BaseCellMeans ... TrueCounts counts
rownames(504): ENSG00000198355 ENSG00000100100 ... ENSG00000159958
ENSG00000075275
rowData names(23): Row.names chromosome ... meanQuantileNorm.y
cvQuantileNorm.y
colnames(240): NA19347:Cell1 NA19347:Cell2 ... HG00338:Cell19
HG00338:Cell20
colData names(6): Cell Batch ... Group Condition
reducedDimNames(0):
altExpNames(0):We can see these populations of cells are very different, which is expected given the substantial differences in the data generated by 10x and smartseq2 chemistries.
sim.joint <- logNormCounts(sim.joint)
sim.joint <- runPCA(sim.joint)
# Plots
pca.plot <- plotPCA(sim.joint, colour_by = "Sample", shape_by = "Batch",
theme_size=12, point_size=2)
pca.plot <- ggpar(pca.plot, palette=get_palette(sample.colors, 6),
legend.title=list(shape="Batch", color="Sample"), legend="bottom")Scale for 'colour' is already present. Adding another scale for 'colour',
which will replace the existing scale.gene.mean.plot <- as.data.frame(rowData(sim.joint)) %>%
pivot_longer(cols=c(meanSampled, meanQuantileNorm),
names_to="Batch", values_to="gene_mean") %>%
mutate(Batch = gsub("meanSampled", "smartseq2", Batch),
Batch = gsub("meanQuantileNorm", "10x", Batch)) %>%
gghistogram(x="gene_mean", fill="Batch", palette=type.col)
sim.joint$cell_sum <- colSums(counts(sim.joint))
cell.sum.plot <- as.data.frame(colData(sim.joint)) %>%
gghistogram(x="cell_sum", fill="Batch", palette=type.col, color=NA)
right <- plot_grid(gene.mean.plot,
cell.sum.plot + theme(legend.position="none"),
ncol=1, rel_heights = c(1,0.8), labels = c("b", "c"))
plot_grid(pca.plot, right, labels=c("a", ""))
Simulated cells from different chemistries. (a) PCA plots of cells colored by individual and shaped by chemistry batch (n=20 cells per individual per batch). The distribution of (b) gene mean counts and (c) cell count sums between the two chemistries.
if(save){
save.name <- paste0(date, "_mixed-chem")
saveRDS(sim.joint, paste0("output/01_sims/", save.name, ".rds"))
ggsave(paste0("output/00_Figures/", save.name, ".pdf"), width = 6, height = 4)
}
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
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gridExtra 2.3 2017-09-09 [1] CRAN (R 4.0.2)
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hms 1.1.0 2021-05-17 [1] CRAN (R 4.0.4)
htmltools 0.5.2 2021-08-25 [1] CRAN (R 4.0.4)
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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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munsell 0.5.0 2018-06-12 [1] CRAN (R 4.0.2)
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openxlsx 4.2.4 2021-06-16 [1] CRAN (R 4.0.4)
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Rcpp 1.0.7 2021-07-07 [1] CRAN (R 4.0.4)
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readr * 2.0.1 2021-08-10 [1] CRAN (R 4.0.4)
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rlang 0.4.11 2021-04-30 [1] CRAN (R 4.0.4)
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Rsamtools * 2.6.0 2020-10-27 [1] Bioconductor
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XML 3.99-0.7 2021-08-17 [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