Plotting PCA under different demographic histories
Last updated: 2020-12-22
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Knit directory: popstruct_scripts/
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These are the previous versions of the repository in which changes were made to the R Markdown (analysis/plt_PCA.Rmd) and HTML (docs/plt_PCA.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 | 99813ec | Arslan-Zaidi | 2020-12-03 | organizing workflowr project |
| html | 99813ec | Arslan-Zaidi | 2020-12-03 | organizing workflowr project |
suppressWarnings(suppressMessages({
library(ggplot2)
library(data.table)
library(dplyr)
library(reshape2)
library(rprojroot)
library(patchwork)
}
))
F = is_rstudio_project$make_fix_file()Introduction
Here, we generate PCA plots for genotypes simulated under different demographic histories (recent and perpetual) and using different sets of variants (rare and common).
PCA on common and rare variants
Read eigenvectors
com_9<-fread(
F("data/gwas/grid/genotypes/tau-9/ss500/train/genotypes/genos_grid_d36_m0.07_s500_t9_chr1_20.rmdup.train.snps.cm.200k.pca.eigenvec"),
header = TRUE)
rare_9<-fread(
F("data/gwas/grid/genotypes/tau-9/ss500/train/genotypes/genos_grid_d36_m0.07_s500_t9_chr1_20.rmdup.train.snps.re.1M.pca.eigenvec"),
header = TRUE)
com100<-fread(
F("data/gwas/grid/genotypes/tau100/ss500/train/genotypes/genos_gridt100_l1e7_ss750_m0.05_chr1_20.rmdup.train.cm.200k.eigenvec"),
header = TRUE)
rare100<-fread(
F("data/gwas/grid/genotypes/tau100/ss500/train/genotypes/genos_gridt100_l1e7_ss750_m0.05_chr1_20.rmdup.train.re.all.eigenvec"),
header = TRUE)
colnames(com_9) <- colnames(rare_9) <- colnames(com100) <- colnames(rare100) <- c("FID","IID", paste("PC", seq(1,100), sep = ""))Read ‘pop’ files, which describe which deme each individual was sampled from.
pop9 = fread(F("data/gwas/grid/genotypes/tau-9/ss500/genos_grid_d36_m0.07_s500_t9.train.pop"))
pop100 = fread(F("data/gwas/grid/genotypes/tau100/ss500/iid_train.txt"))
colnames(pop9) = colnames(pop100) = c("FID","IID","deme","longitude","latitude")Define bivariate color scheme for points
#define bivariate color scheme for pca
d<-expand.grid(lon=1:6,lat=1:6)
d$deme<-seq(1:36)-1
r1 <- with(d,(lon-min(lon))/(max(lon)-min(lon)))
r2 <- with(d,(lat-min(lat))/(max(lat)-min(lat)))
r3 <- 1-sqrt((r1^2+r2^2)/2)
cc <- rbind(r1, r2, r3, 0.5)
rownames(cc) <- c("red", "green", "blue", "alpha")
cols <- rgb(t(cc))
d$cols=cols
pop9 = merge(pop9,d,by="deme")
pop100 = merge(pop100,d,by="deme")
com_9<-merge(com_9,pop9,by=c("FID","IID"))
com100<-merge(com100,pop100,by=c("FID","IID"))
rare_9<-merge(rare_9,pop9,by=c("FID","IID"))
rare100<-merge(rare100,pop100,by=c("FID","IID"))Plot the first two PCs calculated from common and rare variants simulated under the recent and perpetual structure model.
plt_common_9<-ggplot(com_9,aes(PC1,PC2,color=as.factor(deme)))+
geom_point(alpha=0.5, size=0.1)+
theme_bw()+
scale_color_manual(values=cols)+
theme(legend.position="none",
axis.text=element_text(size=10),
panel.grid = element_blank(),
plot.margin = unit(rep(0.5,4), "pt"),
plot.background = element_blank())+
labs(title = "Perpetual structure \n (MAF > 5%)")
plt_rare_9<-ggplot(rare_9,aes(PC1,PC2,color=as.factor(deme)))+
geom_point(alpha=0.5, size=0.1)+
theme_bw()+
scale_color_manual(values=cols)+
theme(legend.position="none",
axis.text = element_text(size=10),
panel.grid=element_blank(),
plot.margin = unit(rep(0.5,4), "pt"))+
labs(title = "Perpetual structure \n (MAC = 2,3,4)")
plt_common100<-ggplot(com100,aes(PC1,PC2,color=as.factor(deme)))+
geom_point(alpha=0.5, size=0.1)+
theme_bw()+
scale_color_manual(values=cols)+
theme(legend.position="none",
axis.text=element_text(size=10),
panel.grid=element_blank(),
plot.margin = unit(rep(0.5,4), "pt"),
plot.background=element_blank())+
labs(title = "Recent structure \n (MAF > 5%)")
plt_rare100<-ggplot(rare100,aes(PC1,PC2,color=as.factor(deme)))+
geom_point(alpha=0.5, size=0.1)+
theme_bw()+
scale_color_manual(values=cols)+
theme(legend.position="none",
axis.text=element_text(size=10),
panel.grid=element_blank(),
plot.margin = unit(rep(0.5,4), "pt"),
plot.background = element_blank())+
labs(title = "Recent structure \n (MAC = 2,3,4)")
plt_combined <- (plt_common_9 + plt_common100) / (plt_rare_9 + plt_rare100)
plt_combined
| Version | Author | Date |
|---|---|---|
| 99813ec | Arslan-Zaidi | 2020-12-03 |
PCA on IBD sharing
Now let’s plot the first two PCs calculated using IBD sharing.
ibd = fread(
F("data/gwas/grid/genotypes/tau100/ss250/germline/germ.cm_ibd.eigenvec"))
pop100$iid = paste("msp_",seq(1,9000,1),sep="")
colnames(ibd) = c("FID","IID",
paste("PC",seq(1,20,1),sep=""))
# ibd2 = ibd[,c("msp","iid") := tstrsplit(IID, split = "_")]
# ibd2[,iid:=paste("tsk",as.numeric(iid)-1,sep="_")]
ibd2 = merge(pop100,
ibd,
by.x = "iid",
by.y = "IID",all.x=TRUE,sort=FALSE)
plt_ibd1 = ggplot(ibd2,aes(PC1,PC2,color=as.factor(deme)))+
geom_point(show.legend = FALSE,
alpha=0.5, size=0.1)+
scale_color_manual(values=cols)+
theme_bw()+
theme(legend.position="none",
axis.text=element_text(size=10),
plot.title = element_text(size = 11),
axis.title = element_text(size = 11),
panel.grid=element_blank())+
labs(x="PC2",y="PC3",
title="PCA on IBD-sharing")
plt_ibd2 = ggplot(ibd2,aes(PC3,PC4,color=as.factor(deme)))+
geom_point(show.legend = FALSE,
alpha=0.5, size=0.1)+
scale_color_manual(values=cols)+
theme_bw()+
theme(legend.position="none",
axis.text=element_text(size=10),
plot.title = element_text(size = 11),
axis.title = element_text(size = 11),
panel.grid=element_blank())+
labs(x="PC2",y="PC3",
title="PCA on IBD-sharing")
plt_ibd1 + plt_ibd2
| Version | Author | Date |
|---|---|---|
| 99813ec | Arslan-Zaidi | 2020-12-03 |
sessionInfo()R version 4.0.3 (2020-10-10)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.7
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] patchwork_1.0.1 rprojroot_1.3-2 reshape2_1.4.4 dplyr_1.0.2
[5] data.table_1.13.2 ggplot2_3.3.2 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rcpp_1.0.5 plyr_1.8.6 pillar_1.4.6 compiler_4.0.3
[5] later_1.1.0.1 git2r_0.27.1 tools_4.0.3 digest_0.6.27
[9] evaluate_0.14 lifecycle_0.2.0 tibble_3.0.4 gtable_0.3.0
[13] pkgconfig_2.0.3 rlang_0.4.8 rstudioapi_0.11 yaml_2.2.1
[17] xfun_0.19 withr_2.3.0 stringr_1.4.0 knitr_1.30
[21] generics_0.1.0 fs_1.5.0 vctrs_0.3.4 tidyselect_1.1.0
[25] grid_4.0.3 glue_1.4.2 R6_2.5.0 rmarkdown_2.5
[29] farver_2.0.3 purrr_0.3.4 magrittr_1.5 whisker_0.4
[33] backports_1.1.10 scales_1.1.1 promises_1.1.1 ellipsis_0.3.1
[37] htmltools_0.5.0 colorspace_1.4-1 httpuv_1.5.4 labeling_0.4.2
[41] stringi_1.5.3 munsell_0.5.0 crayon_1.3.4