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mashR
## install mashR on RCC
pip install --user sos
/project2/xuanyao/marie/E-GEUV-1/mashr
module load python/3.7.0
pip install --user sos sos-notebook
/home/maries1/.local/bin/sos run fastqtl_to_mash.ipynb -h
# Run fastQTL without filterin (see page1)
# Merge the fastQTL nominal outputs
# Separate the fastQTL output into gene categories
cat Yoruba.chr{1..22}.nominals.txt > Yoruba.auto.nominals.txt
cat EUR.chr{1..22}.nominals.txt > EUR.auto.nominals.txt
csvfile=susieR.gene.both.csv
for line in `cat ${csvfile} | grep -v ^#`
do
gene=`echo ${line} |cut -d ',' -f 1`
grep -w "${gene}" Yoruba.auto.nominals.txt >> Yoruba.both.nominals.txt
grep -w "${gene}" EUR.auto.nominals.txt >> EUR.both.nominals.txt
done
csvfile=susieR.gene.diff.csv
for line in `cat ${csvfile} | grep -v ^#`
do
gene=`echo ${line} |cut -d ',' -f 1`
grep -w "${gene}" Yoruba.auto.nominals.txt >> Yoruba.diff.nominals.txt
grep -w "${gene}" EUR.auto.nominals.txt >> EUR.diff.nominals.txt
done
csvfile=susieR.gene.YRI.csv
for line in `cat ${csvfile} | grep -v ^#`
do
gene=`echo ${line} |cut -d ',' -f 1`
grep -w "${gene}" Yoruba.auto.nominals.txt >> Yoruba.YRI.nominals.txt
grep -w "${gene}" EUR.auto.nominals.txt >> EUR.YRI.nominals.txt
done
csvfile=susieR.gene.EUR.csv
for line in `cat ${csvfile} | grep -v ^#`
do
gene=`echo ${line} |cut -d ',' -f 1`
grep -w "${gene}" Yoruba.auto.nominals.txt >> Yoruba.EUR.nominals.txt
grep -w "${gene}" EUR.auto.nominals.txt >> EUR.EUR.nominals.txt
doneConvert the fastQTL outout to mashR input format
sos run fastqtl_to_mash.ipynb --gene-list genes.both.txt --data-list both.file.txt --cols 5 4
run mashR (mean)
data <- readRDS("EUR.file.mash.rds")
data.temp = mash_set_data(data$random.b,data$random.s,zero_Shat_reset = 1)
Vhat = estimate_null_correlation_simple(data.temp)
rm(data.temp)
data.random = mash_set_data(data$random.b,data$random.s,V=Vhat,zero_Shat_reset = 1)
data.strong = mash_set_data(data$strong.b,data$strong.s, V=Vhat,zero_Shat_reset = 1)
U.pca = cov_pca(data.strong,2)
U.ed = cov_ed(data.strong, U.pca)
U.c = cov_canonical(data.random)
m = mash(data.random, Ulist = c(U.ed,U.c))
print(get_pairwise_sharing(m))run mashR (SD)
get_pairwise_sharing1 = function(m, factor=0.5, lfsr_thresh=0.05, FUN= identity){
R = mashr:::get_ncond(m)
lfsr = get_lfsr(m)
S=matrix(NA,nrow = R, ncol=R)
SD=matrix(NA,nrow = R, ncol=R)
for(i in 1:R){
for(j in i:R){
sig_i=get_significant_results(m,thresh=lfsr_thresh,conditions = i)
sig_j=get_significant_results(m,thresh=lfsr_thresh,conditions = j)
a=union(sig_i,sig_j)
ratio=FUN(get_pm(m)[a,i])/FUN(get_pm(m)[a,j])##divide effect sizes
S[i,j]=mean(ratio>factor & ratio<(1/factor))
SD[i,j]=sd(ratio>factor & ratio<(1/factor))
}
}
S[lower.tri(S, diag = FALSE)] = t(S)[lower.tri(S, diag = FALSE)]
colnames(S) = row.names(S) = colnames(m$result$PosteriorMean)
SD[lower.tri(SD, diag = FALSE)] = t(SD)[lower.tri(SD, diag = FALSE)]
colnames(SD) = row.names(SD) = colnames(m$result$PosteriorMean)
return(list(S,SD))
}Prapere phenotype (gene expression) files for GCTA
genelist=read.csv("LDSC/test3.location.csv", header=T,stringsAsFactors = F)
EUR = read.csv("EUR.TPM.scaled.csv", header=T,stringsAsFactors = F)
Yoruba = read.csv("Yoruba.TPM.scaled.csv", header=T,stringsAsFactors = F)
## extract genes from the gene expression list
for(i in 1:3){
grep(genelist[i,1],Yoruba )
}
Yorubalist<-Yoruba[Yoruba[,1]%in%genelist[,1],]
EURlist <- EUR[EUR[,1]%in%genelist[,1],]
comlist <- Yorubalist$X[Yorubalist$X%in% EURlist$gene]
setwd("/Users/saitoumarie/Dropbox/Chicago/RCC/eQTL.practice/LDSC/phen")
for(i in 1:length(comlist)){
hako <- data.frame(cbind(c(names(EURlist), names(Yorubalist)), c(names(EURlist), names(Yorubalist))))
hako$EUR <- c(as.numeric(EURlist[EURlist$gene==comlist[i],]), rep(NA, length(Yorubalist)))
hako$Yoruba <- c(rep(NA, length(EURlist)), as.numeric(Yorubalist[Yorubalist$X==comlist[i],]))
hako<-subset(hako,hako$X1!="gene")
hako<-subset(hako,hako$X1!="X")
colnames(hako) <- c('ID', 'ID', 'EUR', 'Yoruba')
# write.csv(hako, comlist[i])
write.table(hako, file = paste(comlist[i], ".pre"), sep = "\t", row.names = FALSE,
col.names = FALSE,quote=FALSE)
}run GCTA
export PATH="$PATH:/home/maries1/gcta_1.93.2beta"
#./gcta64
csvfile=finemapped.location.csv
for line in `cat ${csvfile} | grep -v ^#`
do
gene=`echo ${line} | cut -d ',' -f 1`
gcta64 --bfile ${gene}.EandY --autosome --make-grm --out ${gene}
gcta64 --reml-bivar --grm ${gene} --pheno phen/${gene}.phen --out result/${gene}
done
csvfile=finemapped.location.csv
for line in `cat ${csvfile} | grep -v ^#`
do
gene=`echo ${line} | cut -d ',' -f 1`
grep 'V(G)/Vp_tr1' result/${gene}.hsq >> result/trait1.EUR.txt
grep 'V(G)/Vp_tr2' result/${gene}.hsq >> result/trait2.Yoruba.txt
grep 'rG' result/${gene}.hsq >> result/rG.txt
done
csvfile=finemapped.location.csv
for line in `cat ${csvfile} | grep -v ^#`
do
gene=`echo ${line} | cut -d ',' -f 1`
ls result/${gene}.hsq >> list.txt
done
sessionInfo()R version 4.0.0 (2020-04-24)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.6
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] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rcpp_1.0.4.6 rprojroot_1.3-2 digest_0.6.25 later_1.1.0.1
[5] R6_2.4.1 backports_1.1.8 git2r_0.27.1 magrittr_1.5
[9] evaluate_0.14 stringi_1.4.6 rlang_0.4.7 fs_1.4.1
[13] promises_1.1.1 whisker_0.4 rmarkdown_2.3 tools_4.0.0
[17] stringr_1.4.0 glue_1.4.1 httpuv_1.5.4 xfun_0.14
[21] yaml_2.2.1 compiler_4.0.0 htmltools_0.4.0 knitr_1.28