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#######vcf to varLD genotype format
import pandas as pd
import numpy as np
pd.set_option('display.max_colwidth', 9999)
#### from all the genes
### files from csv list
genelist=pd.read_csv('/project2/xuanyao/marie/E-GEUV-1/finemap/finemapped.location.csv')
## for 0 to len(genelist)
genes=genelist.iloc[:,0]
dfYRI = []
for gene in genes:
df = pd.read_table(f'{gene}.Yoruba.genotype.recode.vcf',header=252)
df = df.replace('0|0', '1').replace('0|1', '2').replace('1|0', '2').replace('1|1', '3')
df = df.iloc[:, [2,1] + list(range(10, len(df.columns)))]
df.to_csv(f'{gene}.varLDgenotype.Yoruba.csv',index=0,header=False,sep="\t")
dfEUR = []
for gene in genes:
df = pd.read_table(f'{gene}.EUR.genotype.recode.vcf',header=252)
df = df.replace('0|0', '1').replace('0|1', '2').replace('1|0', '2').replace('1|1', '3')
df = df.iloc[:, [2,1] + list(range(10, len(df.columns)))]
df.to_csv(f'{gene}.varLDgenotype.EUR.csv',index=0,header=False,sep="\t")
############csvfile=/project2/xuanyao/marie/E-GEUV-1/finemap/finemapped.location.csv
for line in `cat ${csvfile} | grep -v ^#`
do
gene=`echo ${line} |cut -d ',' -f 1`
java -jar rgenetics-1.0.jar -p VarLD /project2/xuanyao/marie/E-GEUV-1/finemap/YRI/${gene}.varLDgenotype.Yoruba.csv /project2/xuanyao/marie/E-GEUV-1/finemap/EUR/${gene}.varLDgenotype.EUR.csv -o varLD${gene}.txt
done
module load plink
plink --vcf ENSG00000267733.Yoruba.genotype.recode.vcf --r --matrix --out ENSG00000267733.Yoruba.LD.matrix
plink --vcf ENSG00000267733.Yoruba.genotype.recode.vcf --recode --out ENSG00000267733.Yoruba.ped
####### matix for trans-pop-finemapping analysis
setwd("/Users/saitoumarie/Dropbox/Chicago/RCC/eQTL.practice/LDSC/PESCA")
EURgeno<-read.table("ENSG00000160284.EUR.genotype.recode.vcf", header = FALSE, sep = "\t", skip = 252)
#head(EURgeno)
EURLD<-read.table("ENSG00000160284.EUR.LD.matrix.ld", header = FALSE, sep = " ")
#head(EURLD)
rownames(EURLD) <- EURgeno$V3
colnames(EURLD) <- EURgeno$V3
#####
YRIgeno<-read.table("ENSG00000160284.Yoruba.genotype.recode.vcf", header = FALSE, sep = "\t", skip = 252)
YRILD<-read.table("ENSG00000160284.Yoruba.LD.matrix.ld", header = FALSE, sep = " ")
rownames(YRILD) <- YRIgeno$V3
colnames(YRILD) <- YRIgeno$V3
###
EURz<-read.table("ENSG00000160284.zscore.EUR.txt", header = FALSE, sep = " ")
YRIz<-read.table("ENSG00000160284.zscore.Yoruba.txt", header = FALSE, sep = " ")
#(1) Extract the shared genes from EURLD and YRILD
#(2) Extract the shared genes of (1) from EURz and YRIz
#(3) make a new matrix by pasting EURgeno$V1, EURgeno$V2, EURgeno$V3, EURz$V5, YRIz$V5
#(4) make two new matrices by pasting EURgeno$V3, EURgeno$V2, (EURgeno$V4 and EURgeno$V5) , EURz$V5,
#(1) LD file
dat <- YRILD[!apply(is.na(YRILD), 1, all),]
YRILD2<-dat[ ,!apply(is.na(dat), 2, all) ]
dat1 <- EURLD[!apply(is.na(EURLD), 1, all),]
EURLD2<-dat1[,!apply(is.na(dat1), 2, all) ]
YRILD3 <- YRILD2[ intersect(names(YRILD2), names(EURLD2)),intersect(names(YRILD2), names(EURLD2))]
EURLD3 <- EURLD2[ intersect(names(YRILD2), names(EURLD2)),intersect(names(YRILD2), names(EURLD2))]
#(2) shared gene extraction
EURz2<-subset(EURz,EURz$V2 %in% intersect(names(YRILD2), names(EURLD2)))
YRIz2<-subset(YRIz,YRIz$V2 %in% intersect(names(YRILD2), names(EURLD2)))
#(3) PAINTOR z-score
EURgeno2<-subset(EURgeno,EURgeno$V3 %in% intersect(names(YRILD2), names(EURLD2)))
YRIgeno2<-subset(YRIgeno,YRIgeno$V3 %in% intersect(names(YRILD2), names(EURLD2)))
z.2pop<-data.frame(EURgeno2$V1, EURgeno2$V2, EURgeno2$V3, EURz2$V5, YRIz2$V5)
names(z.2pop) <- c("CHR","POS","RSID","ZSCORE.EUR","ZSCORE.YRI")
head(z.2pop)
plot(z.2pop$ZSCORE.EUR,z.2pop$ZSCORE.YRI)
hist(z.2pop$POS, breaks=20)
plot(z.2pop$POS,z.2pop$ZSCORE.EUR)
## output PAINTOR
write.table(z.2pop, file = "ENSG00000160284.Paintor.z.txt", sep = " ",quote=FALSE,
row.names = FALSE,col.names = TRUE)
write.table(EURLD3, file = "ENSG00000160284.EUR.LD", sep = " ",quote=FALSE,
row.names = FALSE,col.names = FALSE)
write.table(YRILD3, file = "ENSG00000160284.YRI.LD", sep = " ",quote=FALSE,
row.names = FALSE,col.names = FALSE)
write.table(anno, file = "ENSG00000160284.anno", sep = " ",quote=FALSE,
row.names = FALSE,col.names = TRUE)
#(4) PESCA z-score
# N= 358, 87
# SNP BP A2 A1 Z N
#(4) make two new matrices by pasting EURgeno$V3, EURgeno$V2, (EURgeno$V4 and EURgeno$V5) , EURz$V5,
# if EURz2$V5 < 0, take EURgeno2$V4. If EURz2$V5 >= 0, take EURgeno2$V5)
# A2: Effect allele (i.e. trait increasing allele) of the SNP. The sign of Z-score is with respect to this allele.
z.EUR.pesca<-data.frame(EURgeno2$V3, EURgeno2$V2, EURgeno2$V4, EURgeno2$V5, EURz2$V5)
z.EUR.pesca$V6<-c(358)
names(z.EUR.pesca) <- c("SNP","BP","A2","A1","Z","N")
#z.EUR.pesca<-data.frame(EURgeno2$V3, EURgeno2$V2, EURgeno2$V4 or EURgeno2$V5, EURz2$V5, abs(EURz2$V5))
### 8/6
z.EUR.pesca$V6 <- NA
z.EUR.pesca$V7 <- NA
for(i in 1:nrow(z.EUR.pesca)){
if (EURz2[i,5] < 0){
z.EUR.pesca$V6[i] <- EURgeno2[i,4]
z.EUR.pesca$V7[i] <- EURgeno2[i,5]
} else {
z.EUR.pesca$V6[i] <- EURgeno2[i,5]
z.EUR.pesca$V7[i] <- EURgeno2[i,4]
}
}
z.EUR.pesca2<-data.frame(z.EUR.pesca$SNP, z.EUR.pesca$BP, z.EUR.pesca$V6, z.EUR.pesca$V7, z.EUR.pesca$Z,z.EUR.pesca$N)
names(z.EUR.pesca2) <- c("SNP","BP","A2","A1","Z","N")
write.table(z.EUR.pesca2, file = "ENSG00000160284.PESCA.EUR.z.txt", sep = " ",quote=FALSE,
row.names = FALSE,col.names = TRUE)
write.table(z.YRI.pesca2, file = "ENSG00000160284.PESCA.YRI.z.txt", sep = " ",quote=FALSE,
row.names = FALSE,col.names = TRUE)
###
z.YRI.pesca<-data.frame(YRIgeno2$V3, YRIgeno2$V2, YRIgeno2$V4, YRIgeno2$V5, YRIz2$V5)
z.YRI.pesca$V6<-c(87)
names(z.YRI.pesca) <- c("SNP","BP","A2","A1","Z","N")
z.YRI.pesca$V6 <- NA
z.YRI.pesca$V7 <- NA
for(i in 1:nrow(z.YRI.pesca)){
if (YRIz2[i,5] < 0){
z.YRI.pesca$V6[i] <- YRIgeno2[i,4]
z.YRI.pesca$V7[i] <- YRIgeno2[i,5]
} else {
z.YRI.pesca$V6[i] <- YRIgeno2[i,5]
z.YRI.pesca$V7[i] <- YRIgeno2[i,4]
}
}
z.YRI.pesca2<-data.frame(z.YRI.pesca$SNP, z.YRI.pesca$BP, z.YRI.pesca$V6, z.YRI.pesca$V7, z.YRI.pesca$Z,z.YRI.pesca$N)
names(z.YRI.pesca2) <- c("SNP","BP","A2","A1","Z","N")
########### for
for(i in 1:nrow(z.EUR.pesca)){
if (EURz2[i,5] < 0){
EURz2$V6 <- EURgeno2[i,4]
EURz2$V7 <- EURgeno2[i,5]
} else {
EURz2$V6 <- EURgeno2[i,5]
EURz2$V7 <- EURgeno2[i,4]
}
}
############## ikkai
if (EURz2[1,5] < 0){
print(EURgeno2[1,4])
}else{
print(EURgeno2[1,5])
}
##############
matches <- list(c(2,1),c(5,2),c(6,3))
for (match in matches){
if (match[1] > match[2]){
print("Win")
} else {
print ("Lose")
}
}
i==1
answer[1,3]
z.EUR.pesca[1,3]
z.EUR.pesca[[i],3]
matches <- list(c(2,1),c(5,2),c(6,3))
for (match in matches){
if (match[1] > match[2]){
print("Win")
} else {
print ("Lose")
}
}
for(i in 1:nrow(z.EUR.pesca)){
hako <- data.frame(cbind(c(names(EURlist), names(Yorubalist)), c(names(EURlist), names(Yorubalist))))
hako$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$X1!="X")
colnames(hako) <- c('ID', 'ID', 'EUR', 'Yoruba')
}
#(5) make a dummy annotation file (1 * SNP number)
anno<-data.frame(rep(1,length(EURgeno2$V1)))
names(anno) <- c("dummy")module load boost/1.55
/home/maries1/pesca/src/pesca \
--mode fit \
--zscore1 input.EUR.z.ENSG00000160284.txt \
--zscore2 input.YRI.z.ENSG00000160284.txt \
--ld1 input.EUR.LD.ENSG00000160284.txt\
--ld2 input.YRI.LD.ENSG00000160284.txt \
--nburn 5000 \
--nsample 5000 \
--lambda 0.0001 \
--sigmasq1 105.89 \
--sigmasq2 44.75 \
--totnsnp 837 \
--max_iter 200 \
--out ENSG00000160284.PESCA.step1./PAINTOR -input input.files.ENSG00000160284.txt -in -Zhead ZSCORE.EUR,ZSCORE.YRI -LDname EUR.LD,YRI.LD -in /ENSG00000160284/ -out /ENSG00000160284/ -enumerate 3 -annotations dummyImage Title
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