Last updated: 2022-08-20
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Data from here - genoGex.Rdata has everything we need in it There are 5 ‘gex’ RDS files which are the gene expressions for the 5 different tissues, the ‘gtf’ is the gene annotation, ‘phyMap’ is the snp annotation, and ‘geno’ is the genotype matrix
Our pipeline predicts expressions from the gene expression data and genotypes of the rats from the study.
library(tidyverse)
library(data.table)load("~/Box/imlab-data/Projects/PTRS-PGRS-Rosetta/Data-From-Abe-Palmer-Lab/Rdata/genoGex.RData")First, we transpose each tissue’s gene expression file to fit the format expected by the PrediXcan pipeline scripts.
#transposing gene expression files for the 5 tissues
n = gexAc$EnsemblGeneID
gexAc_transpose <- as.data.frame(t(gexAc[,-1]))
colnames(gexAc_transpose) <- n
n = gexIl$EnsemblGeneID
gexIl_transpose <- as.data.frame(t(gexIl[,-1]))
colnames(gexIl_transpose) <- n
n = gexLh$EnsemblGeneID
gexLh_transpose <- as.data.frame(t(gexLh[,-1]))
colnames(gexLh_transpose) <- n
n = gexPl$EnsemblGeneID
gexPl_transpose <- as.data.frame(t(gexPl[,-1]))
colnames(gexPl_transpose) <- n
n = gexVo$EnsemblGeneID
gexVo_transpose <- as.data.frame(t(gexVo[,-1]))
colnames(gexVo_transpose) <- n# Running inverse normalization on each gene expression
invnorm = function(x) {
if(is.null(dim(x))) res = invnorm.vector(x) else
res=apply(x,2,invnorm.vector)
res
}
invnorm.vector = function(x) {yy = rank(x)/(length(x)+1); qnorm(yy)}
gexAc_transpose = invnorm(gexAc_transpose)
gexIl_transpose = invnorm(gexIl_transpose)
gexLh_transpose = invnorm(gexLh_transpose)
gexPl_transpose = invnorm(gexPl_transpose)
gexVo_transpose = invnorm(gexVo_transpose)Write to file.
# Writing the gene expression files to csv files to be used for PEER Factor analysis
write.table(gexAc_transpose, file = '/Users/sabrinami/Github/Rat_Genomics_Paper_Pipeline/data/gexAc.csv', sep = ",", col.names = TRUE, row.names = FALSE)
write.table(gexIl_transpose, file = '/Users/sabrinami/Github/Rat_Genomics_Paper_Pipeline/data/gexIl.csv', sep = ",", col.names = TRUE, row.names = FALSE)
write.table(gexLh_transpose, file = '/Users/sabrinami/Github/Rat_Genomics_Paper_Pipeline/data/gexLh.csv', sep = ",", col.names = TRUE, row.names = FALSE)
write.table(gexPl_transpose, file = '/Users/sabrinami/Github/Rat_Genomics_Paper_Pipeline/data/gexPl.csv', sep = ",", col.names = TRUE, row.names = FALSE)
write.table(gexVo_transpose, file = '/Users/sabrinami/Github/Rat_Genomics_Paper_Pipeline/data/gexVo.csv', sep = ",", col.names = TRUE, row.names = FALSE)The prediction model pipeline also requires a gene annotation file as input. The code below generates it from the gene annotations provided by Palmer lab in ‘gtf’. We also collect snp info.
gtf$gene_type = sub(".*?gene_biotype(.*?);.*", "\\1", gtf$Attr)
gtf$gene_name = sub(".*?gene_name(.*?);.*", "\\1", gtf$Attr)
gene_annotation = subset(gtf, select = -c(Source, Feature, Score, Strand, Attr, Frame) )
gene_annotation = gene_annotation[, c("Chr","Gene", "gene_name", "Start", "End", "gene_type" )]
colnames(gene_annotation) = c("chr", "gene_id", "gene_name", "start", "end")
rownames(gene_annotation) = gene_annotation$gene_idWe have all the information needed to generate the predictions models. We are left to reorganize it to fit the pipeline. The specifics of each step is commented at the top of each block.
# Making the snp annotation in the correct format for the pipeline
phyMap <- within(phyMap, varID <- paste(Chr, Pos, Ref, Alt, sep="_"))
rownames(phyMap) = phyMap$varID
phyMap$rsid = phyMap$varID
colnames(phyMap) = c("snp", "chr", "pos", "refAllele", "effectAllele", 'varID', "rsid")# Splitting the snp annotation file by chromosome
s <- setNames(split(phyMap, phyMap$chr), paste0("snp_annot.chr", unique(phyMap$chr)))
list2env(s, globalenv())The new genotype file combines the provided geno file
and combines information from the provided snp annotation file,
phyMap.
# writing the genotype file to a .txt file so that we can separate it by chromosome using our geneotype parse script.
rownames(geno) = rownames(phyMap)
write.table(geno, file = "/Users/sabrinami/Github/Rat_Genomics_Paper_Pipeline/data/genotype.txt", sep = "\t", col.names = TRUE, row.names = TRUE)
sessionInfo()R version 4.0.3 (2020-10-10)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur 10.16
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
loaded via a namespace (and not attached):
[1] Rcpp_1.0.8.3 rstudioapi_0.11 knitr_1.39 magrittr_1.5
[5] workflowr_1.6.2 R6_2.4.1 rlang_1.0.2 fastmap_1.1.0
[9] stringr_1.4.0 tools_4.0.3 xfun_0.31 cli_3.3.0
[13] git2r_0.27.1 jquerylib_0.1.4 htmltools_0.5.2 ellipsis_0.3.2
[17] rprojroot_1.3-2 yaml_2.2.1 digest_0.6.27 tibble_3.0.4
[21] lifecycle_0.2.0 crayon_1.3.4 later_1.1.0.1 sass_0.4.1
[25] vctrs_0.4.1 fs_1.5.0 promises_1.1.1 glue_1.6.2
[29] evaluate_0.15 rmarkdown_2.14 stringi_1.5.3 compiler_4.0.3
[33] bslib_0.3.1 pillar_1.4.6 backports_1.1.10 jsonlite_1.7.1
[37] httpuv_1.5.4 pkgconfig_2.0.3