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SimData.sh
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SimData.sh
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#!/bin/sh
#20200624
mkdir /users/mturchin/data/mturchin/InterPath/Analyses/Rnd2AdditiveMdls/Simulations
mkdir /users/mturchin/data/mturchin/InterPath/Analyses/Rnd2AdditiveMdls/Simulations/MAPITR
#zcat /users/mturchin/data/ukbiobank_jun17/subsets/British/British.Ran10000/Imputation/mturchin20/ukb_chrAll_v3.British.Ran10000.QCed.reqDrop.QCed.dropRltvs.PCAdrop.sort.ImptHRC.dose.100geno.raw.edit.gz | R -q -e "set.seed(68361); Data1 <- read.table(file('stdin'), header=T); Columns <- sample(1:ncol(Data1), 10000); Rows <- sample(1:nrow(Data1), 2000); Data2 <- Data1[Rows,Columns]; write.table(Data2, file=\"\", quote=FALSE, row.names=FALSE, col.names=TRUE);" | grep -v ^\> | gzip > /users/mturchin/data/mturchin/InterPath/Analyses/Rnd2AdditiveMdls/Simulations/MAPITR/ukb_chrAll_v3.British.Ran10000.QCed.reqDrop.QCed.dropRltvs.PCAdrop.sort.ImptHRC.dose.100geno.raw.edit.Simulation.cutdwn.gz
##scp -p mturchin@desktop4.ccv.brown.edu:/users/mturchin/data/ukbiobank_jun17/subsets/British/British.Ran10000/Imputation/mturchin20/ukb_chrAll_v3.British.Ran10000.QCed.reqDrop.QCed.dropRltvs.PCAdrop.sort.ImptHRC.dose.100geno.raw.edit.gz /Users/mturchin20/Documents/Work/LabMisc/RamachandranLab/MAPITR/SimData/.
#scp -p mturchin@desktop4.ccv.brown.edu:/users/mturchin/data/ukbiobank_jun17/subsets/British/British.Ran10000/Imputation/mturchin20/ukb_chrAll_v3.British.Ran10000.QCed.reqDrop.QCed.dropRltvs.PCAdrop.sort.ImptHRC.dose.100geno.raw.edit.gz /home/mturchin20/TempStuff/MAPITR/SimData/.
#gunzip /Users/mturchin20/Documents/Work/LabMisc/RamachandranLab/MAPITR/SimData/ukb_chrAll_v3.British.Ran10000.QCed.reqDrop.QCed.dropRltvs.PCAdrop.sort.ImptHRC.dose.100geno.raw.edit.gz
#cat /Users/mturchin20/Documents/Work/LabMisc/RamachandranLab/MAPITR/SimData/ukb_chrAll_v3.British.Ran10000.QCed.reqDrop.QCed.dropRltvs.PCAdrop.sort.ImptHRC.dose.100geno.raw.edit | R -q -e "set.seed(68361); Data1 <- read.table(file('stdin'), header=T); Columns <- sample(1:ncol(Data1), 10000); Rows <- sample(1:nrow(Data1), 2000); Data2 <- Data1[Rows,Columns]; write.table(Data2, file=\"\", quote=FALSE, row.names=FALSE, col.names=TRUE);" | grep -v ^\> | gzip > /Users/mturchin20/Documents/Work/LabMisc/RamachandranLab/MAPITR/SimData/ukb_chrAll_v3.British.Ran10000.QCed.reqDrop.QCed.dropRltvs.PCAdrop.sort.ImptHRC.dose.100geno.raw.edit.Simulation.cutdwn.gz
cat /Users/mturchin20/Documents/Work/LabMisc/RamachandranLab/MAPITR/SimData/ukb_chrAll_v3.British.Ran10000.QCed.reqDrop.QCed.dropRltvs.PCAdrop.sort.ImptHRC.dose.100geno.raw.edit | head -n 5000 | perl -lane 'print join("\t", @F[0..99999]);' | R -q -e "set.seed(68361); Data1 <- read.table(file('stdin'), header=T); Columns <- sample(1:ncol(Data1), 10000); Rows <- sample(1:nrow(Data1), 2000); Data2 <- Data1[Rows,Columns]; write.table(Data2, file=\"\", quote=FALSE, row.names=FALSE, col.names=TRUE);" | grep -v ^\> | gzip > /Users/mturchin20/Documents/Work/LabMisc/RamachandranLab/MAPITR/SimData/ukb_chrAll_v3.British.Ran10000.QCed.reqDrop.QCed.dropRltvs.PCAdrop.sort.ImptHRC.dose.100geno.raw.edit.Simulation.cutdwn.gz
#zcat /users/mturchin/data/ukbiobank_jun17/subsets/British/British.Ran10000/Imputation/mturchin20/ukb_chrAll_v3.British.Ran10000.QCed.reqDrop.QCed.dropRltvs.PCAdrop.sort.ImptHRC.dose.100geno.raw.edit.gz | R -q -e "set.seed(248573); Data1 <- read.table(file('stdin'), header=T); Columns <- sample(1:ncol(Data1), 20000); Rows <- sample(1:nrow(Data1), 4000); Data2 <- Data1[Rows,Columns]; write.table(Data2, file=\"\", quote=FALSE, row.names=FALSE, col.names=TRUE);" | grep -v ^\> | gzip > /users/mturchin/LabMisc/RamachandranLab/MAPITR/SimData/ukb_chrAll_v3.British.Ran10000.QCed.reqDrop.QCed.dropRltvs.PCAdrop.sort.ImptHRC.dose.100geno.raw.edit.Simulation.cutdwn.vs2.gz
zcat /users/mturchin/data/ukbiobank_jun17/subsets/British/British.Ran10000/Imputation/mturchin20/ukb_chrAll_v3.British.Ran10000.QCed.reqDrop.QCed.dropRltvs.PCAdrop.sort.ImptHRC.dose.100geno.raw.edit.gz | head -n 5000 | perl -lane 'print join("\t", @F[0..99999]);' | R -q -e "set.seed(248573); Data1 <- read.table(file('stdin'), header=T); Columns <- sample(1:ncol(Data1), 20000); Rows <- sample(1:nrow(Data1), 4000); Data2 <- Data1[Rows,Columns]; write.table(Data2, file=\"\", quote=FALSE, row.names=FALSE, col.names=TRUE);" | grep -v ^\> | gzip > /users/mturchin/LabMisc/RamachandranLab/MAPITR/SimData/ukb_chrAll_v3.British.Ran10000.QCed.reqDrop.QCed.dropRltvs.PCAdrop.sort.ImptHRC.dose.100geno.raw.edit.Simulation.cutdwn.vs3.gz
##R -q -e "set.seed(973459); Data1 <- read.table(\"/users/mturchin/data/mturchin/InterPath/Analyses/Rnd2AdditiveMdls/Simulations/MAPITR/ukb_chrAll_v3.British.Ran10000.QCed.reqDrop.QCed.dropRltvs.PCAdrop.sort.ImptHRC.dose.100geno.raw.edit.Simulation.cutdwn.gz\", header=T); \
##R -q -e "set.seed(973459); Data1 <- read.table(\"/home/mturchin20/TempStuff/MAPITR/SimData/ukb_chrAll_v3.British.Ran10000.QCed.reqDrop.QCed.dropRltvs.PCAdrop.sort.ImptHRC.dose.100geno.raw.edit.Simulation.cutdwn.gz\", header=T); \
R -q -e "set.seed(973459); Data1 <- read.table(\"/Users/mturchin20/Documents/Work/LabMisc/RamachandranLab/MAPITR/SimData/ukb_chrAll_v3.British.Ran10000.QCed.reqDrop.QCed.dropRltvs.PCAdrop.sort.ImptHRC.dose.100geno.raw.edit.Simulation.cutdwn.gz\", header=T); \
Data1.mean <- apply(Data1, 2, mean); Data1.sd <- apply(Data1, 2, sd); Data1 <- t((t(Data1)-Data1.mean)/Data1.sd); \
Y <- runif(nrow(Data1)); \
Pathways <- c(); \
Pathways.Full <- sample(1:ncol(Data1), 6000); \
Count1 <- 1; for (i in 1:30) { \
Pathways <- rbind(Pathways, Pathways.Full[Count1:(Count1+29)]); \
Count1 <- Count1 + 29; \
}; \
SNPs.Pathways <- c(); \
SNPs.Genome <- c(); \
Genome.SNPs <- 1:ncol(Data1); \
for (j in 1:4) { \
SNPs.Pathways <- rbind(SNPs.Pathways, sample(Pathways[1,], 10)); \
SNPs.Genome <- rbind(SNPs.Genome, sample(Genome.SNPs[! Genome.SNPs %in% Pathways[1,]], 10)); \
}; \
Y.new <- Y; \
for (k in 1:4) { \
for (l in 1:10) { \
Y.new <- Y.new + .5 * (Data1[,SNPs.Pathways[k,l]] * Data1[,SNPs.Genome[k,l]]); \
}; \
}; \
#write.table(Y, file=\"/Users/mturchin20/Documents/Work/LabMisc/RamachandranLab/MAPITR/SimData/SimData.Pheno.txt\", quote=FALSE, row.names=FALSE, col.names=FALSE); \
#write.table(Pathways, file=\"/Users/mturchin20/Documents/Work/LabMisc/RamachandranLab/MAPITR/SimData/SimData.Pathways.txt\", quote=FALSE, row.names=FALSE, col.names=FALSE); \
#write.table(SNPs.Pathways, file=\"/Users/mturchin20/Documents/Work/LabMisc/RamachandranLab/MAPITR/SimData/SimData.SNPs_Pathways.txt\", quote=FALSE, row.names=FALSE, col.names=FALSE); \
#write.table(SNPs.Genome, file=\"/Users/mturchin20/Documents/Work/LabMisc/RamachandranLab/MAPITR/SimData/SimData.SNPs_Genome.txt\", quote=FALSE, row.names=FALSE, col.names=FALSE); \
"
#write.table(Y, file=\"/home/mturchin20/TempStuff/MAPITR/SimData/SimData.Pheno.txt\", quote=FALSE, row.names=FALSE, col.names=FALSE); \
#write.table(Pathways, file=\"/home/mturchin20/TempStuff/MAPITR/SimData/SimData.Pathways.txt\", quote=FALSE, row.names=FALSE, col.names=FALSE); \
#write.table(SNPs.Pathways, file=\"/home/mturchin20/TempStuff/MAPITR/SimData/SimData.SNPs_Pathways.txt\", quote=FALSE, row.names=FALSE, col.names=FALSE); \
#write.table(SNPs.Genome, file=\"/home/mturchin20/TempStuff/MAPITR/SimData/SimData.SNPs_Genome.txt\", quote=FALSE, row.names=FALSE, col.names=FALSE); \
#R -q -e "
##set.seed(973459); Data1 <- read.table("/Users/mturchin20/Documents/Work/LabMisc/RamachandranLab/MAPITR/SimData/ukb_chrAll_v3.British.Ran10000.QCed.reqDrop.QCed.dropRltvs.PCAdrop.sort.ImptHRC.dose.100geno.raw.edit.Simulation.cutdwn.gz", header=T);
##R -q -e "
##set.seed(973459); Data1 <- read.table("/home/mturchin20/TempStuff/MAPITR/SimData/ukb_chrAll_v3.British.Ran10000.QCed.reqDrop.QCed.dropRltvs.PCAdrop.sort.ImptHRC.dose.100geno.raw.edit.gz", header=T);
##Data1 <- Data1[sample(1:nrow(Data1), 2000), sample(1:ncol(Data1), 10000)];
##set.seed(973459); Data1 <- read.table("/home/mturchin20/TempStuff/MAPITR/SimData/ukb_chrAll_v3.British.Ran10000.QCed.reqDrop.QCed.dropRltvs.PCAdrop.sort.ImptHRC.dose.100geno.raw.edit.Simulation.cutdwn.gz", header=T);
#set.seed(973459); Data1 <- read.table("/users/mturchin/LabMisc/RamachandranLab/MAPITR/SimData/ukb_chrAll_v3.British.Ran10000.QCed.reqDrop.QCed.dropRltvs.PCAdrop.sort.ImptHRC.dose.100geno.raw.edit.Simulation.cutdwn.gz", header=T);
set.seed(973459); Data1 <- read.table("/users/mturchin/LabMisc/RamachandranLab/MAPITR/SimData/ukb_chrAll_v3.British.Ran10000.QCed.reqDrop.QCed.dropRltvs.PCAdrop.sort.ImptHRC.dose.100geno.raw.edit.Simulation.cutdwn.vs3.gz", header=T);
Data1.mean <- apply(Data1, 2, mean); Data1.sd <- apply(Data1, 2, sd); Data1 <- t((t(Data1)-Data1.mean)/Data1.sd);
PVE <- .8
rho <- .2
Pathways.Num <- 2
#Pathways.Num.Selected <- 2
Pathways.Num.Selected <- 1
Pathways.SNPs <- 100
Pathways.SNPs.Interaction <- 1000
Genome.Additive.Prop <- .5
#Additive SNPs
Data1.Additive <- Data1[,sample(1:ncol(Data1), ncol(Data1)*Genome.Additive.Prop)];
Data1.Additive.Betas <- rnorm(ncol(Data1.Additive),0,1);
Y.Additive <- Data1.Additive %*% Data1.Additive.Betas;
Data1.Additive.Betas <- Data1.Additive.Betas * c(sqrt((PVE * rho) / var(Y.Additive))); #Normalize in respect to PVE
Y.Additive <- Data1.Additive %*% Data1.Additive.Betas;
#Pathways & Genome Partners Make
Pathways <- c();
Pathways.Full <- sample(1:ncol(Data1), Pathways.Num * Pathways.SNPs);
Count1 <- 1; for (i in 1:Pathways.Num) {
Pathways <- rbind(Pathways, Pathways.Full[Count1:(Count1+Pathways.SNPs-1)]);
Count1 <- Count1 + Pathways.SNPs - 1;
};
##SNPs.Pathways <- c();
Genome.AntiPathway.SNPs <- c();
Genome.Total.SNPs <- 1:ncol(Data1);
for (j in 1:Pathways.Num.Selected) {
Genome.AntiPathway.SNPs <- rbind(Genome.AntiPathway.SNPs, sample(Genome.Total.SNPs[! Genome.Total.SNPs %in% Pathways[j,]], Pathways.SNPs.Interaction));
};
#for (i in 1:5) { print(table(Pathways[i,] %in% Genome.AntiPathway.SNPs[i,])); }; #Data check
#Pathway Epistasis
Y.Epistasis <- 0;
Data1.Epistasis <- list(); Data1.Epistasis.Pathway.SNPs.Check <- list(); Data1.Epistasis.Genome.SNPs.Check <- list();
Data1.Epistasis.Alphas <- c();
for (k in 1:Pathways.Num.Selected) {
Data1.Epistasis.temp1 <- c(); Data1.Epistasis.Pathway.SNPs.Check.temp1 <- c(); Data1.Epistasis.Genome.SNPs.Check.temp1 <- c();
for (l in 1:Pathways.SNPs) {
print(c(k,l));
Data1.Epistasis.Pathway.SNPs <- c();
Data1.Epistasis.Genome.SNPs <- c();
for (m in 1:Pathways.SNPs.Interaction) {
Data1.Epistasis.Pathway.SNPs <- cbind(Data1.Epistasis.Pathway.SNPs, Data1[,Pathways[k,l]]);
Data1.Epistasis.Genome.SNPs <- cbind(Data1.Epistasis.Genome.SNPs, Data1[,Genome.AntiPathway.SNPs[k,m]]);
};
Epistasis1 <- Data1.Epistasis.Pathway.SNPs * Data1.Epistasis.Genome.SNPs;
Data1.Epistasis.temp1 <- cbind(Data1.Epistasis.temp1, Epistasis1); Data1.Epistasis.Pathway.SNPs.Check.temp1 <- cbind(Data1.Epistasis.Pathway.SNPs.Check.temp1, Data1.Epistasis.Pathway.SNPs); Data1.Epistasis.Genome.SNPs.Check.temp1 <- cbind(Data1.Epistasis.Genome.SNPs.Check.temp1, Data1.Epistasis.Genome.SNPs);
};
Data1.Epistasis.Alphas.temp1 <- rnorm(Pathways.SNPs*Pathways.SNPs.Interaction,0,1);
Y.Epistasis <- Y.Epistasis + (Data1.Epistasis.temp1 %*% Data1.Epistasis.Alphas.temp1);
Data1.Epistasis[[k]] <- Data1.Epistasis.temp1; Data1.Epistasis.Pathway.SNPs.Check[[k]] <- Data1.Epistasis.Pathway.SNPs.Check.temp1; Data1.Epistasis.Genome.SNPs.Check[[k]] <- Data1.Epistasis.Genome.SNPs.Check.temp1;
Data1.Epistasis.Alphas <- cbind(Data1.Epistasis.Alphas, Data1.Epistasis.Alphas.temp1);
};
Epistasis.PVE.Rescale <- sqrt((PVE * (1-rho)) / var(Y.Epistasis));
#for (i in 1:length(Data1.Epistasis)) { print(dim(Data1.Epistasis[[i]])); }; print(dim(Data1.Epistasis.Alphas)); #Data check
#Data1.Epistasis.Pathway.SNPs.Check[[1]][1:5,198:207]; Data1.Epistasis.Pathway.SNPs.Check[[1]][1:5,398:407]; Data1.Epistasis.Pathway.SNPs.Check[[1]][1:5,598:607]; Data1.Epistasis.Pathway.SNPs.Check[[1]][1:5,798:807]; #Data check
#Data1.Epistasis.Pathway.SNPs.Check[[3]][1:5,198:207]; Data1.Epistasis.Pathway.SNPs.Check[[3]][1:5,398:407]; Data1.Epistasis.Pathway.SNPs.Check[[3]][1:5,598:607]; Data1.Epistasis.Pathway.SNPs.Check[[3]][1:5,798:807]; #Data check
#Data1.Epistasis.Genome.SNPs.Check[[1]][1:5,c(1:5,200:205)]; Data1.Epistasis.Genome.SNPs.Check[[1]][1:5,c(6:10,406:410)]; Data1.Epistasis.Genome.SNPs.Check[[1]][1:5,c(11:15,611:615)]; Data1.Epistasis.Genome.SNPs.Check[[1]][1:5,c(16:20,816:820)]; #Data check
#Data1.Epistasis.Genome.SNPs.Check[[4]][1:5,c(1:5,200:205)]; Data1.Epistasis.Genome.SNPs.Check[[4]][1:5,c(6:10,406:410)]; Data1.Epistasis.Genome.SNPs.Check[[4]][1:5,c(11:15,611:615)]; Data1.Epistasis.Genome.SNPs.Check[[4]][1:5,c(16:20,816:820)]; #Data check
Y.Epistasis <- 0;
for (k in 1:Pathways.Num.Selected) {
Y.Epistasis <- Y.Epistasis + ((Data1.Epistasis[[k]] %*% Data1.Epistasis.Alphas[,k]) * c(Epistasis.PVE.Rescale));
};
#Error
PVE.Error <- (1 - PVE) * (var(Y.Additive + Y.Epistasis) / PVE);
Y.Error <- rnorm(nrow(Data1),0,sqrt(PVE.Error));
#Final Phenotype
Y.Final <- Y.Additive + Y.Epistasis + Y.Error
#Pathway Formatting
Pathways.Edits <- apply(Pathways, 1, function(x) { return(paste(x, collapse=",")); }); Pathways.Edits <- cbind(1:length(Pathways.Edits), Pathways.Edits); Pathways.Edits <- cbind(rep("Pathway", nrow(Pathways.Edits)), Pathways.Edits); Pathways.Edits.2 <- apply(Pathways.Edits[,1:2], 1, function(x) { return(paste(x, collapse="")); }); Pathways.Edits <- cbind(Pathways.Edits.2, Pathways.Edits[,3]);
#PVE Checks
PVE.Check.Additive <- var(Y.Additive) / var(Y.Final)
PVE.Check.Epistasis <- var(Y.Epistasis) / var(Y.Final)
PVE.Check.Error <- var(Y.Error) / var(Y.Final)
PVE.Check.Pathways <- c(); for (i in 1:length(Data1.Epistasis)) { PVE.Check.Pathways <- c(PVE.Check.Pathways, var(Data1.Epistasis[[i]] %*% Data1.Epistasis.Alphas[,i] * c(Epistasis.PVE.Rescale)) / var(Y.Final)); };
print(c(PVE, rho, PVE * rho, PVE - PVE * rho)); print(c(PVE.Check.Additive, PVE.Check.Epistasis, PVE.Check.Error)); print(PVE.Check.Pathways);
#Output writing
write.table(Y.Final, file="/users/mturchin/LabMisc/RamachandranLab/MAPITR/SimData/SimData.Pheno.txt", quote=FALSE, row.names=FALSE, col.names=FALSE);
write.table(Pathways, file="/users/mturchin/LabMisc/RamachandranLab/MAPITR/SimData/SimData.Pathways.txt", quote=FALSE, row.names=FALSE, col.names=FALSE);
write.table(Pathways.Edits, file="/users/mturchin/LabMisc/RamachandranLab/MAPITR/SimData/SimData.Pathways.Edits.txt", quote=FALSE, row.names=FALSE, col.names=FALSE);
write.table(Genome.AntiPathway.SNPs, file="/users/mturchin/LabMisc/RamachandranLab/MAPITR/SimData/SimData.Genome_AntiPathway_SNPs.txt", quote=FALSE, row.names=FALSE, col.names=FALSE);
#"
##Y <- rnorm(nrow(Data1));
##write.table(Y, file="/home/mturchin20/TempStuff/MAPITR/SimData/SimData.Pheno.Orig.txt", quote=FALSE, row.names=FALSE, col.names=FALSE);
##write.table(SNPs.Pathways, file="/home/mturchin20/TempStuff/MAPITR/SimData/SimData.SNPs_Pathways.txt", quote=FALSE, row.names=FALSE, col.names=FALSE);
#write.table(Y, file="/Users/mturchin20/Documents/Work/LabMisc/RamachandranLab/MAPITR/SimData/SimData.Pheno.Orig.txt", quote=FALSE, row.names=FALSE, col.names=FALSE);
#write.table(Y.new, file="/Users/mturchin20/Documents/Work/LabMisc/RamachandranLab/MAPITR/SimData/SimData.Pheno.txt", quote=FALSE, row.names=FALSE, col.names=FALSE);
#write.table(Pathways, file="/Users/mturchin20/Documents/Work/LabMisc/RamachandranLab/MAPITR/SimData/SimData.Pathways.txt", quote=FALSE, row.names=FALSE, col.names=FALSE);
#write.table(Pathways.Edits, file="/Users/mturchin20/Documents/Work/LabMisc/RamachandranLab/MAPITR/SimData/SimData.Pathways.Edits.txt", quote=FALSE, row.names=FALSE, col.names=FALSE);
#write.table(SNPs.Pathways, file="/Users/mturchin20/Documents/Work/LabMisc/RamachandranLab/MAPITR/SimData/SimData.SNPs_Pathways.txt", quote=FALSE, row.names=FALSE, col.names=FALSE);
#write.table(SNPs.Genome, file="/Users/mturchin20/Documents/Work/LabMisc/RamachandranLab/MAPITR/SimData/SimData.SNPs_Genome.txt", quote=FALSE, row.names=FALSE, col.names=FALSE);
#write.table(Y.Final, file="/home/mturchin20/TempStuff/MAPITR/SimData/SimData.Pheno.txt", quote=FALSE, row.names=FALSE, col.names=FALSE);
#write.table(Pathways, file="/home/mturchin20/TempStuff/MAPITR/SimData/SimData.Pathways.txt", quote=FALSE, row.names=FALSE, col.names=FALSE);
#write.table(Pathways.Edits, file="/home/mturchin20/TempStuff/MAPITR/SimData/SimData.Pathways.Edits.txt", quote=FALSE, row.names=FALSE, col.names=FALSE);
#write.table(Genome.AntiPathway.SNPs, file="/home/mturchin20/TempStuff/MAPITR/SimData/SimData.Genome_AntiPathway_SNPs.txt", quote=FALSE, row.names=FALSE, col.names=FALSE);
#correction_factor = np.sqrt(self.pve*(1.0-self.rho)/np.var(self.y_pathway))
# eps = (1.0-self.pve)*np.var(self.y_additive+self.y_pathway)/self.pve
# self.y_err = np.random.normal(0,np.sqrt(eps),size=self.X.shape[0])
# self.y = self.y_additive + self.y_pathway + self.y_err + self.y_pcs
#for (n in (Pathways.Num.Selected+1):Pathways.Num) {
#};
# self.linear_pve = np.var(np.dot(self.X_additive,self.beta))/np.var(self.y)
# self.epistatic_pve = np.var(self.y_pathway)/np.var(self.y)
# self.pcs_pve = np.var(self.y_pcs)/np.var(self.y)
# print("additive pve: ",self.linear_pve)
# print("pathway pve: ",self.epistatic_pve)
# print("pc pve: ",self.pcs_pve)
# print("PVE per pathway")
# for idx,alpha_i in enumerate(self.alpha):
# print(np.var(np.dot(self.get_W(idx),alpha_i))/np.var(self.y))
# print("error pve: ",np.var(self.y_err)/np.var(self.y))
# Alpha1 <- rnorm(1,0,1);
# Data1.Epistasis.Alphas.temp1 <- c(Data1.Epistasis.Alphas.temp1, Alpha1);
# Y.Epistasis <- Y.Epistasis + Alpha1 * Epistasis1;
# Data1.Epistasis.Alphas.temp1 <- c();
# Epistasis1 <- (Data1[,Pathways[k,l]] * Data1[,Genome.AntiPathway.SNPs[k,m]]);
# Data1.Epistasis.temp1 <- cbind(Data1.Epistasis.temp1, Epistasis1);
# Epistasis1 <- Data1.Epistasis.Pathway.SNPs * Data1.Epistasis.Genome.SNPs;
# Data1.Epistasis[[k]] <- Epistasis1;
#Count2 <- 1; for (k in 1:Pathways.Num.Selected) { for (l in 1:Pathways.SNPs) { for (m in 1:Pathways.SNPs.Interaction) {
# Y.Epistasis <- Y.Epistasis + Data1.Epistasis.Alphas[Count2] * Data1.Epistasis[Count2] * Epistasis.PVE.Rescale; #Normalize in respect to PVE
# Count2 <- Count2 + 1;
#}; }; };
##R -q -e"
#library("devtools"); devtools::load_all();
#set.seed(582724);
#X <- read.table("/Users/mturchin20/Documents/Work/LabMisc/RamachandranLab/MAPITR/SimData/ukb_chrAll_v3.British.Ran10000.QCed.reqDrop.QCed.dropRltvs.PCAdrop.sort.ImptHRC.dose.100geno.raw.edit.Simulation.cutdwn.gz", header=T);
#Y <- read.table("/Users/mturchin20/Documents/Work/LabMisc/RamachandranLab/MAPITR/SimData/SimData.Pheno.txt", header=F);
#Pathways <- read.table("/Users/mturchin20/Documents/Work/LabMisc/RamachandranLab/MAPITR/SimData/SimData.Pathways.Edits.txt", header=F);
#SNPs.Pathways <- read.table("/Users/mturchin20/Documents/Work/LabMisc/RamachandranLab/MAPITR/SimData/SimData.SNPs_Pathways.txt", header=F);
#SNPs.Genome <- read.table("/Users/mturchin20/Documents/Work/LabMisc/RamachandranLab/MAPITR/SimData/SimData.SNPs_Genome.txt", header=F);
#Y2 <- cbind(Y,Y);
#Pathways.Full <- lapply(strsplit(as.character(Pathways[,2]), ","), as.numeric);
#library("Rcpp"); library("RcppArmadillo"); library("RcppParallel"); library("doParallel");
#sourceCpp("/Users/mturchin20/Documents/Work/LabMisc/RamachandranLab/MAPITR/src/MAPITR.cpp");
#X.mean <- apply(X, 2, mean); X.sd <- apply(X, 2, sd); X.adj <- t((t(X)-X.mean)/X.sd);
#summary(lm(as.matrix(Y) ~ X.adj[,1252] + X.adj[,956] + X.adj[,1252] * X.adj[,956]))
#MAPITRBase(as.matrix(Y2), as.matrix(t(X)), Pathways.Full[1:2], cores=1)
#MAPITRBaseTest(as.matrix(Y), as.matrix(t(X)), Pathways.Full[1], cores=1)
#MAPITRBaseTest(as.matrix(Y), Pathways.Full[1], cores=1)
#Test2 <-
library("devtools"); devtools::load_all();
library("Rcpp"); library("RcppArmadillo"); library("RcppParallel"); library("doParallel"); library("CompQuadForm");
sourceCpp("/users/mturchin/LabMisc/RamachandranLab/MAPITR/src/MAPITR.cpp")
set.seed(582724);
X <- read.table("/users/mturchin/LabMisc/RamachandranLab/MAPITR/SimData/ukb_chrAll_v3.British.Ran10000.QCed.reqDrop.QCed.dropRltvs.PCAdrop.sort.ImptHRC.dose.100geno.raw.edit.Simulation.cutdwn.vs3.gz", header=T);
Y <- read.table("/users/mturchin/LabMisc/RamachandranLab/MAPITR/SimData/SimData.Pheno.txt", header=F);
Pathways <- read.table("/users/mturchin/LabMisc/RamachandranLab/MAPITR/SimData/SimData.Pathways.Edits.txt", header=F);
Genome.AntiPathway.SNPs <- read.table("/users/mturchin/LabMisc/RamachandranLab/MAPITR/SimData/SimData.Genome_AntiPathway_SNPs.txt", header=F);
#Y2 <- cbind(Y,Y);
Pathways.Full <- lapply(strsplit(as.character(Pathways[,2]), ","), as.numeric);
X.mean <- apply(X, 2, mean); X.sd <- apply(X, 2, sd); X.adj <- t((t(X)-X.mean)/X.sd);
##MAPITRmain <- function (Phenotype, Genotypes, Pathways, GRM_Grand = NULL, GRM_Pathway = NULL, Covariates, CenterStandardize = TRUE, RegressPhenotypes = TRUE, PrintProgress = FALSE)
#MAPITR.Results <- MAPITRmain(Y, X, Pathways);
#Y30 <- matrix(unlist(rep(Y, 30)), ncol=30)
Y30 <- c();
for (i in 1:nrow(Pathways)) { Y30 <- cbind(Y30, residuals(lm(as.matrix(Y) ~ as.matrix(X.adj[,Pathways.Full[[i]]]) - 1))); };
Results.temp2 <- MAPITRBase(Y30, t(X.adj), Pathways.Full, cores=1)
#Results.temp2 <- MAPITRBaseOrig(Y30, t(X.adj), Pathways.Full, cores=1)
#MAPITRoutput$pValues <- GetMAPITRpValues(MAPITRoutput.temp2$Est, MAPITRoutput.temp2$Eigenvalues)
Results.temp2.pValues <- GetMAPITRpValues(Results.temp2$Est, Results.temp2$Eigenvalues)
Results.temp2.pValues
#30 pathways
#4 true
#each have 200 SNPs
#10 real SNPs each interacting with 10 other SNPs in the genome
#SNPs.Pathways <- read.table("/home/mturchin20/TempStuff/MAPITR/SimData/SimData.SNPs_Pathways.txt", header=F);
#SNPs.Genome <- read.table("/home/mturchin20/TempStuff/MAPITR/SimData/SimData.SNPs_Genome.txt", header=F);
#20200813
#mkdir /users/mturchin/LabMisc/RamachandranLab/MAPITR_temp1
#scp -p /Users/mturchin20/Documents/Work/LabMisc/RamachandranLab/InterPath/Vs1/Simulations/Simulations.zip mturchin@ssh.ccv.brown.edu:/users/mturchin/LabMisc/RamachandranLab/MAPITR_temp1/.
#load("/users/mturchin/LabMisc/RamachandranLab/MAPITR_temp1/Simulations/Data/gene_snp_list.RData")
#load("/users/mturchin/LabMisc/RamachandranLab/MAPITR_temp1/Simulations/Data/gene_ids.RData")
#load("/users/mturchin/LabMisc/RamachandranLab/MAPITR_temp1/Simulations/Data/chromosome16_snps.RData")
#write.table(as.matrix(gene_snp_list), file="/users/mturchin/LabMisc/RamachandranLab/MAPITR_temp1/Simulations/Data/gene_snp_list.txt", quote=FALSE, row.names=FALSE)
#write.table(gene_ids, file="/users/mturchin/LabMisc/RamachandranLab/MAPITR_temp1/Simulations/Data/gene_ids.txt", quote=FALSE, row.names=FALSE, col.names=FALSE)
#write.table(chromosome16_snps, file="/users/mturchin/LabMisc/RamachandranLab/MAPITR_temp1/Simulations/Data/chromosome16_snps.txt", quote=FALSE, row.names=FALSE, col.names=FALSE)
mkdir /users/mturchin/LabMisc/RamachandranLab/MAPITR_temp1/Simulations/Temp1
cd /users/mturchin/LabMisc/RamachandranLab/MAPITR_temp1/Simulations/Temp1
#cp -p ../../MAPITR_temp1/Simulations/Code/InterPath.cpp ../../MAPITR_temp1/Simulations/Code/InterPath.edits1.cpp
#cp -p /users/mturchin/LabMisc/RamachandranLab/MAPITR_temp1/Simulations/Code/InterPath.cpp /users/mturchin/LabMisc/RamachandranLab/MAPITR_temp1/Simulations/Code/InterPath.edits2.cpp
#R -q -e "
set.seed(11151990); library(doParallel); library(Rcpp); library(RcppArmadillo); library(RcppParallel); library(CompQuadForm); library(Matrix); library(MASS); library(truncnorm)
load("/users/mturchin/LabMisc/RamachandranLab/MAPITR_temp1/Simulations/Data/gene_snp_list.RData")
load("/users/mturchin/LabMisc/RamachandranLab/MAPITR_temp1/Simulations/Data/gene_ids.RData")
load("/users/mturchin/LabMisc/RamachandranLab/MAPITR_temp1/Simulations/Data/chromosome16_snps.RData")
sourceCpp("/users/mturchin/LabMisc/RamachandranLab/MAPITR_temp1/Simulations/Code/InterPath.cpp")
gene_list = list()
for(i in 1:ncol(gene_snp_list)){
x = unlist(gene_snp_list[[i]])
gene_list[[i]] = x[!is.na(x)]
names(gene_list)[i] = colnames(gene_snp_list)[i]
}
X = chromosome16_snps;
colnames(X) = sapply(colnames(X),function(x) unlist(strsplit(x,split = "_"))[1])
unique.snps = apply(X,2,function(x) length(unique(x)))
X = X[,unique.snps>1]; dim(X)
#X.lines <- apply(X, 2, function(x) { return(paste(x, collapse=",")); });
Xmean=apply(X, 2, mean); Xsd=apply(X, 2, sd); X=t((t(X)-Xmean)/Xsd)
ind = nrow(X); nsnp = ncol(X)
### Make Sure that Gene List and Genotype SNPs Match Up ###
for(i in 1:length(gene_list)){
x = gene_list[[i]]
gene_list[[i]] = x[x%in%colnames(X)]
}
### Remove Duplicate SNPs ###
repeated_snps = unlist(gene_list)[duplicated(unlist(gene_list))]
for(i in 1:length(gene_list)){
x = gene_list[[i]]
gene_list[[i]] = x[which(x%in%repeated_snps==FALSE)]
}
### Remove Duplicate SNPs (based on geno) ###
X.lines <- apply(X, 2, function(x) { return(paste(x, collapse=",")); });
duplicated_snps <- names(X.lines[duplicated(X.lines)])
for(i in 1:length(gene_list)){
x = gene_list[[i]]
gene_list[[i]] = x[which(x%in%duplicated_snps==FALSE)]
}
#dim(X)
#X <- X[,!colnames(X)%in%names(X.lines[duplicated(X.lines)])]
#dim(X)
rm(X.lines)
### Remove Any Gene with One or Fewer SNPs ###
gene_list = gene_list[unlist(lapply(gene_list,function(x){length(x)}))>=2]
npthwy = length(gene_list) #Final Number of Genes/Pathways
### Define the Simulation Parameters ###
n.datasets = 1 #Total Number of Simulations
pve = 0.8; #Heritability of the trait
rho = 0.2; #Proportion of the heritability caused by additive effects {0.8, 0.5}
### Set Up Causal Pathways in Three Groups
ncausal1 = 5; ncausal2 = 50 #Pathways in each group
ncausal3 = 150-(ncausal1+ncausal2) #Total Pathways
### Create a list to save the final Results ###
pval_mat = matrix(nrow = npthwy,ncol = n.datasets); rownames(pval_mat) = names(gene_list)
G1_snps = matrix(nrow = ncausal1,ncol = n.datasets)
G2_snps = matrix(nrow = ncausal2,ncol = n.datasets)
### Run the Analysis ###
#for(j in 1:n.datasets){
#Select Causal Pathways
pthwy.ids = 1:npthwy
s1=sample(pthwy.ids, ncausal1, replace=F)
s2=sample(pthwy.ids[-s1], ncausal2, replace=F)
s3=sample(pthwy.ids[-c(s1,s2)], ncausal3, replace=F)
s1=seq(1,ncausal1,by=1)
s2=seq(ncausal1+1,ncausal1+1+ncausal2,by=1)
### Simulate the Additive Effects ###
snps = unlist(gene_list[c(s1,s2,s3)])
Xmarginal = X[,snps]
beta=rnorm(dim(Xmarginal)[2])
y_marginal=c(Xmarginal%*%beta)
beta=beta*sqrt(pve*rho/var(y_marginal))
y_marginal=Xmarginal%*%beta
### Simulate Pairwise Interaction matrix ###
Xepi = c(); b = c()
causal_pthwys = gene_list[c(s1,s2,s3)]
for(i in 1:ncausal1){
snps = unlist(gene_list[s1[i]])
for(k in 1:length(snps)){
Xepi = cbind(Xepi,X[,snps[k]]*X[,unlist(gene_list[s2])])
}
}
### Simulate the Pairwise Effects ###
beta=rnorm(dim(Xepi)[2])
y_epi=c(Xepi%*%beta)
beta=beta*sqrt(pve*(1-rho)/var(y_epi))
y_epi=Xepi%*%beta
### Simulate the (Environmental) Error/Noise ###
y_err=rnorm(ind)
y_err=y_err*sqrt((1-pve)/var(y_err))
### Simulate the Total Phenotypes ###
y=y_marginal+y_epi+y_err
### Check dimensions ###
dim(X); dim(y)
######################################################################################
######################################################################################
######################################################################################
### Rewrite the Pathway List to Give Column IDs Instead of SNP Names ###
regions = list()
for(i in 1:length(gene_list)){
regions[[i]] = which(colnames(X)%in%gene_list[[i]])
names(regions)[i] = names(gene_list)[i]
}
######################################################################################
######################################################################################
######################################################################################
### Set the number of cores ###
cores = detectCores()
# save.image("20200813_temp1.RData")
# save.image("20200813_temp2.RData")
# save.image("20200813_temp3.RData")
# save.image("20200813_temp4.RData") #diff pve/rho vals
set.seed(11151990); library(doParallel); library(Rcpp); library(RcppArmadillo); library(RcppParallel); library(CompQuadForm); library(Matrix); library(MASS); library(truncnorm)
load("20200813_temp4.RData")
# sourceCpp("/users/mturchin/LabMisc/RamachandranLab/MAPITR_temp1/Simulations/Code/InterPath.edits1.cpp")
sourceCpp("/users/mturchin/LabMisc/RamachandranLab/MAPITR_temp1/Simulations/Code/InterPath.edits2.cpp")
# sourceCpp("/users/mturchin/LabMisc/RamachandranLab/MAPITR_temp1/Simulations/Code/InterPath.cpp")
regions <- regions[1:50]
# b.cols(1,j.n_elem) = trans(X.rows(j-1));
# X.t <- t(X);
Y30 <- c();
for (i in 1:length(regions)) { Y30 <- cbind(Y30, residuals(lm(as.matrix(y) ~ as.matrix(X[,regions[[i]]]) - 1))); };
### Run InterPath ###
ptm <- proc.time() #Start clock
# vc.mod = InterPath(t(X),y,regions,cores = cores)
vc.mod = InterPath(t(X),Y30,regions,cores = cores)
proc.time() - ptm #Stop clock
### Apply Davies Exact Method ###
vc.ts = vc.mod$Est
names(vc.ts) = names(regions)
pvals = c()
for(i in 1:length(vc.ts)){
lambda = sort(vc.mod$Eigenvalues[,i],decreasing = T)
Davies_Method = davies(vc.mod$Est[i], lambda = lambda, acc=1e-8)
pvals[i] = 2*min(1-Davies_Method$Qq,Davies_Method$Qq)
names(pvals)[i] = names(vc.ts[i])
}
pvals
### Find power for the first group of SNPs ###
Pthwys_1 = names(regions)[s1]
### Find power for the second group of SNPs ###
Pthwys_2 = names(regions)[s2]
######################################################################################
######################################################################################
######################################################################################
### Save Results ###
pval_mat[,j] = pvals
G1_snps[,j] = Pthwys_1
G2_snps[,j] = Pthwys_2
### Report Status ###
cat("Completed Dataset", j, "\n", sep = " ")
#}
#Save final Results
Final = list(pval_mat,G1_snps,G2_snps)
```
> proc.time() - ptm #Stop clock
user system elapsed
1662.400 41.025 1703.461
> pvals
AARS ABAT ABCA3 ABCC1 ABCC11 ABCC12
4.951986e-03 4.533479e-07 7.384488e-01 3.207512e-08 5.443513e-03 1.210351e-01
ABCC6 ACSF3 ACSM1 ACSM2A ACSM2B ACSM3
3.571622e-02 1.828907e-03 3.114093e-01 3.372007e-01 7.752575e-01 6.586091e-01
ACSM5 ADAD2 ADAMTS18 ADAT1 ADCY7 ADCY9
1.086930e-01 7.224191e-01 5.661781e-03 4.238015e-01 8.849778e-01 6.350021e-01
ADGRG1 ADGRG3 ADGRG5 AKTIP ALDOA ALG1
1.511630e-02 9.641417e-04 6.753311e-01 4.773866e-02 8.054788e-01 8.621856e-01
AMFR ANKRD11 ANKS3 ANKS4B AP1G1 APOBR
3.613904e-01 3.422696e-03 1.783861e-02 8.824898e-01 4.812328e-01 2.280406e-02
APOOP5 APRT AQP8 ARHGAP17 ARL6IP1 ARMC5
7.915088e-02 7.618808e-03 2.109255e-02 1.416500e-01 3.896289e-01 5.333194e-01
ATF7IP2 ATMIN ATP2A1 ATP2C2 ATP6V0D1 ATXN1L
8.343987e-01 9.534180e-01 8.267568e-01 4.671650e-05 5.826754e-01 9.069548e-01
ATXN2L AXIN1 BAIAP3 BANP BBS2 BCAR1
5.068331e-01 6.661920e-01 1.898870e-01 2.785836e-02 3.677133e-01 4.780869e-01
BCL7C BCO1
3.490812e-02 4.476724e-01
...
> sourceCpp("/users/mturchin/LabMisc/RamachandranLab/MAPITR_temp1/Simulations/Code/InterPath.edits2.cpp")
> regions <- regions[1:50]
>
> ### Run InterPath ###
> ptm <- proc.time() #Start clock
> vc.mod = InterPath(t(X),y,regions,cores = cores)
> proc.time() - ptm #Stop clock
user system elapsed
1661.762 40.384 1702.187
>
> ### Apply Davies Exact Method ###
> vc.ts = vc.mod$Est
> names(vc.ts) = names(regions)
>
> pvals = c()
> for(i in 1:length(vc.ts)){
+ lambda = sort(vc.mod$Eigenvalues[,i],decreasing = T)
+ Davies_Method = davies(vc.mod$Est[i], lambda = lambda, acc=1e-8)
+ pvals[i] = 2*min(1-Davies_Method$Qq,Davies_Method$Qq)
+ names(pvals)[i] = names(vc.ts[i])
+ }
> pvals
AARS ABAT ABCA3 ABCC1 ABCC11 ABCC12
4.951986e-03 4.533479e-07 7.384488e-01 3.207512e-08 5.443513e-03 1.210351e-01
ABCC6 ACSF3 ACSM1 ACSM2A ACSM2B ACSM3
3.571622e-02 1.828907e-03 3.114093e-01 3.372007e-01 7.752575e-01 6.586091e-01
ACSM5 ADAD2 ADAMTS18 ADAT1 ADCY7 ADCY9
1.086930e-01 7.224191e-01 5.661781e-03 4.238015e-01 8.849778e-01 6.350021e-01
ADGRG1 ADGRG3 ADGRG5 AKTIP ALDOA ALG1
1.511630e-02 9.641417e-04 6.753311e-01 4.773866e-02 8.054788e-01 8.621856e-01
AMFR ANKRD11 ANKS3 ANKS4B AP1G1 APOBR
3.613904e-01 3.422696e-03 1.783861e-02 8.824898e-01 4.812328e-01 2.280406e-02
APOOP5 APRT AQP8 ARHGAP17 ARL6IP1 ARMC5
7.915088e-02 7.618808e-03 2.109255e-02 1.416500e-01 3.896289e-01 5.333194e-01
ATF7IP2 ATMIN ATP2A1 ATP2C2 ATP6V0D1 ATXN1L
8.343987e-01 9.534180e-01 8.267568e-01 4.671650e-05 5.826754e-01 9.069548e-01
ATXN2L AXIN1 BAIAP3 BANP BBS2 BCAR1
5.068331e-01 6.661920e-01 1.898870e-01 2.785836e-02 3.677133e-01 4.780869e-01
BCL7C BCO1
3.490812e-02 4.476724e-01
#no covars/corrct
> # vc.mod = InterPath(t(X),matrix(Y30, ncol=length(regions), byrow=FALSE),regions,cores = cores)
> proc.time() - ptm #Stop clock
user system elapsed
1474.966 42.586 1517.611
>
> ### Apply Davies Exact Method ###
> vc.ts = vc.mod$Est
> names(vc.ts) = names(regions)
>
> pvals = c()
> for(i in 1:length(vc.ts)){
+ lambda = sort(vc.mod$Eigenvalues[,i],decreasing = T)
+ Davies_Method = davies(vc.mod$Est[i], lambda = lambda, acc=1e-8)
+ pvals[i] = 2*min(1-Davies_Method$Qq,Davies_Method$Qq)
+ names(pvals)[i] = names(vc.ts[i])
+ }
> pvals
AARS ABAT ABCA3 ABCC1 ABCC11 ABCC12
9.075350e-03 1.332589e-07 7.269296e-01 8.315985e-09 8.904773e-04 7.395582e-02
ABCC6 ACSF3 ACSM1 ACSM2A ACSM2B ACSM3
4.724206e-02 1.221150e-03 3.537905e-01 3.758361e-01 8.866383e-01 7.561057e-01
ACSM5 ADAD2 ADAMTS18 ADAT1 ADCY7 ADCY9
1.835457e-01 8.593086e-01 4.683992e-03 2.367160e-01 7.598773e-01 4.633264e-01
ADGRG1 ADGRG3 ADGRG5 AKTIP ALDOA ALG1
8.094992e-03 7.423545e-04 5.489022e-01 4.195427e-02 6.875581e-01 9.103729e-01
AMFR ANKRD11 ANKS3 ANKS4B AP1G1 APOBR
3.975499e-01 9.669478e-04 1.572000e-02 9.477620e-01 4.750039e-01 2.166131e-02
APOOP5 APRT AQP8 ARHGAP17 ARL6IP1 ARMC5
3.922667e-02 2.086286e-02 2.057187e-02 1.623535e-01 3.846440e-01 2.715451e-01
ATF7IP2 ATMIN ATP2A1 ATP2C2 ATP6V0D1 ATXN1L
9.739844e-01 9.879812e-01 8.828891e-01 9.662260e-05 7.345508e-01 7.031548e-01
ATXN2L AXIN1 BAIAP3 BANP BBS2 BCAR1
6.785574e-01 7.652838e-01 1.987019e-01 3.239428e-02 3.443410e-01 5.144689e-01
BCL7C BCO1
4.128038e-02 3.706294e-01
#with regress out before
> pvals
AARS ABAT ABCA3 ABCC1 ABCC11 ABCC12
1.069194e-02 2.052235e-06 8.588981e-01 9.648794e-08 1.368308e-02 2.240554e-01
ABCC6 ACSF3 ACSM1 ACSM2A ACSM2B ACSM3
5.389115e-02 6.202295e-03 2.422489e-01 3.721679e-01 6.374053e-01 7.814415e-01
ACSM5 ADAD2 ADAMTS18 ADAT1 ADCY7 ADCY9
6.661380e-02 6.291496e-01 1.482319e-02 5.528941e-01 9.797160e-01 8.027934e-01
ADGRG1 ADGRG3 ADGRG5 AKTIP ALDOA ALG1
2.129916e-02 1.458324e-03 7.566949e-01 6.699238e-02 8.698268e-01 9.038601e-01
AMFR ANKRD11 ANKS3 ANKS4B AP1G1 APOBR
4.557393e-01 1.116736e-02 3.273128e-02 9.573315e-01 6.185752e-01 1.745093e-02
APOOP5 APRT AQP8 ARHGAP17 ARL6IP1 ARMC5
1.200981e-01 2.040583e-02 2.766836e-02 1.940421e-01 4.164473e-01 6.224320e-01
ATF7IP2 ATMIN ATP2A1 ATP2C2 ATP6V0D1 ATXN1L
6.005474e-01 8.230449e-01 8.874864e-01 1.022908e-04 9.108130e-01 9.659642e-01
ATXN2L AXIN1 BAIAP3 BANP BBS2 BCAR1
4.662691e-01 5.283394e-01 2.597461e-01 3.938402e-02 4.056004e-01 7.111617e-01
BCL7C BCO1
5.787721e-02 3.685350e-01
```
set.seed(686234); library(doParallel); library(Rcpp); library(RcppArmadillo); library(RcppParallel); library(CompQuadForm); library(Matrix); library(MASS); library(truncnorm)
Data1 <- read.table("/users/mturchin/LabMisc/RamachandranLab/MAPITR/SimData/ukb_chrAll_v3.British.Ran10000.QCed.reqDrop.QCed.dropRltvs.PCAdrop.sort.ImptHRC.dose.100geno.raw.edit.Simulation.cutdwn.vs3.gz", header=T);
sourceCpp("/users/mturchin/LabMisc/RamachandranLab/MAPITR_temp1/Simulations/Code/InterPath.edits2.cpp")
#MAFs <- colSums(Data1)/(2*nrow(Data1));
#Data2 <- c(); for (i in 1:ncol(Data1)) { Data2 <- cbind(Data2, rbinom(nrow(Data1), 2, MAFs[i])); };
Data3 <- apply(Data1, 2, function(x) { MAF <- sum(x)/(2*length(x)); return(rbinom(length(x), 2, MAF)); });
#Data2 <- apply(Data1, 2, function(x) { return(sample(x)); });
X = Data3;
Xmean=apply(X, 2, mean); Xsd=apply(X, 2, sd); X=t((t(X)-Xmean)/Xsd)
ind = nrow(X); nsnp = ncol(X)
### Define the Simulation Parameters ###
n.datasets = 1 #Total Number of Simulations
pve = 0.6; #Heritability of the trait
rho = 0.6; #Proportion of the heritability caused by additive effects {0.8, 0.5}
### Set Up Causal SNPs
n.pathways = 2 #Number of Pathways
ncausal1a = 50; ncausal1b = 750 #
ncausal2a = 100; ncausal2b = 1000 #
#Select Causal Pathways
snp.ids = 1:ncol(X)
s1a=sample(snp.ids, ncausal1a, replace=F)
s1b=sample(snp.ids[-s1a], ncausal1b, replace=F)
# s2a=sample(snp.ids, ncausal2a, replace=F)
# s2b=sample(snp.ids[-s2a], ncausal2b, replace=F)
regions <- list(); regions[[1]] <- s1a;
for (i in 2:5) {
regions[[i]] <- sample(snp.ids, 50, replace=F);
}
#Additive Effects
# snps = c(s1a,s1b,s2a,s2b);
snps1 = unlist(regions[c(1,2,3,4,5)])
# snps1 = c(s1a,s1b);
Xmarginal = X[,snps1]
beta=rnorm(dim(Xmarginal)[2])
y_marginal=c(Xmarginal%*%beta)
beta=beta*sqrt(pve*rho/var(y_marginal))
y_marginal=Xmarginal%*%beta
### Simulate Pairwise Interaction matrix ###
Xepi = c(); b = c()
snps2 = s1a;
for(k in 1:length(snps2)){
print(k)
Xepi = cbind(Xepi,X[,snps2[k]]*X[,s1b])
}
### Simulate the Pairwise Effects ###
beta=rnorm(dim(Xepi)[2])
y_epi=c(Xepi%*%beta)
beta=beta*sqrt(pve*(1-rho)/var(y_epi))
y_epi=Xepi%*%beta
### Simulate the (Environmental) Error/Noise ###
y_err=rnorm(ind)
y_err=y_err*sqrt((1-pve)/var(y_err))
### Simulate the Total Phenotypes ###
y=y_marginal+y_epi+y_err
### Check dimensions ###
dim(X); dim(y)
#Pathway Formatting
Pathways <- matrix(unlist(regions), nrow=5, byrow=5)
Pathways.Edits <- apply(Pathways, 1, function(x) { return(paste(x, collapse=",")); }); Pathways.Edits <- cbind(1:length(Pathways.Edits), Pathways.Edits); Pathways.Edits <- cbind(rep("Pathway", nrow(Pathways.Edits)), Pathways.Edits); Pathways.Edits.2 <- apply(Pathways.Edits[,1:2], 1, function(x) { return(paste(x, collapse="")); }); Pathways.Edits <- cbind(Pathways.Edits.2, Pathways.Edits[,3]);
cores = detectCores()
Y30 <- c();
for (i in 1:length(regions)) { Y30 <- cbind(Y30, residuals(lm(as.matrix(y) ~ as.matrix(X[,regions[[i]]]) - 1))); };
ptm <- proc.time() #Start clock
vc.mod = InterPath(t(X),Y30,regions,cores = cores)
proc.time() - ptm #Stop clock
### Apply Davies Exact Method ###
vc.ts = vc.mod$Est
names(vc.ts) = names(regions)
pvals = c()
for(i in 1:length(vc.ts)){
lambda = sort(vc.mod$Eigenvalues[,i],decreasing = T)
Davies_Method = davies(vc.mod$Est[i], lambda = lambda, acc=1e-8)
pvals[i] = 2*min(1-Davies_Method$Qq,Davies_Method$Qq)
names(pvals)[i] = names(vc.ts[i])
}
pvals
#Output writing
write.table(Data3, file="/users/mturchin/LabMisc/RamachandranLab/MAPITR/SimData/SimData2.Genotypes.txt", quote=FALSE, row.names=FALSE, col.names=TRUE);
write.table(y, file="/users/mturchin/LabMisc/RamachandranLab/MAPITR/SimData/SimData2.Phenotype.txt", quote=FALSE, row.names=FALSE, col.names=FALSE);
write.table(Pathways.Edits, file="/users/mturchin/LabMisc/RamachandranLab/MAPITR/SimData/SimData2.Pathways.txt", quote=FALSE, row.names=FALSE, col.names=FALSE);
system("gzip -f /users/mturchin/LabMisc/RamachandranLab/MAPITR/SimData/SimData2.Genotypes.txt");
#"
library(doParallel); library(Rcpp); library(RcppArmadillo); library(RcppParallel); library(CompQuadForm); library(Matrix); library(MASS); library(truncnorm)
#sourceCpp("/users/mturchin/LabMisc/RamachandranLab/MAPITR_temp1/Simulations/Code/InterPath.edits2.cpp")
sourceCpp("/users/mturchin/LabMisc/RamachandranLab/MAPITR/src/MAPITR.cpp")
X <- read.table("/users/mturchin/LabMisc/RamachandranLab/MAPITR/temp1/SimData/SimData2.Genotypes.txt.gz", header=T);
Y <- read.table("/users/mturchin/LabMisc/RamachandranLab/MAPITR/temp1/SimData/SimData2.Phenotype.txt", header=F);
Pathways <- read.table("/users/mturchin/LabMisc/RamachandranLab/MAPITR/temp1/SimData/SimData2.Pathways.txt", header=F);
Pathways.Full <- lapply(strsplit(as.character(Pathways[,2]), ","), as.numeric);
regions <- Pathways.Full;
regions2 <- lapply(regions, function(x) { return(x-1);});
Xmean=apply(X, 2, mean); Xsd=apply(X, 2, sd); X=t((t(X)-Xmean)/Xsd)
cores = detectCores()
Y30 <- c();
for (i in 1:length(regions)) { Y30 <- cbind(Y30, residuals(lm(as.matrix(Y) ~ as.matrix(X[,regions[[i]]]) - 1))); };
ptm <- proc.time() #Start clock
# vc.mod = InterPath(t(X),Y30,regions,cores = cores)
vc.mod = MAPITRBase(t(X),Y30,regions2,cores = cores)
proc.time() - ptm #Stop clock
### Apply Davies Exact Method ###
vc.ts = vc.mod$Est
names(vc.ts) = names(regions)
pvals = c()
for(i in 1:length(vc.ts)){
lambda = sort(vc.mod$Eigenvalues[,i],decreasing = T)
Davies_Method = davies(vc.mod$Est[i], lambda = lambda, acc=1e-8)
pvals[i] = 2*min(1-Davies_Method$Qq,Davies_Method$Qq)
names(pvals)[i] = names(vc.ts[i])
}
pvals
#Data1.mean <- apply(Data1, 2, mean); Data1.sd <- apply(Data1, 2, sd); Data1 <- t((t(Data1)-Data1.mean)/Data1.sd);
#
#load("/users/mturchin/LabMisc/RamachandranLab/MAPITR_temp1/Simulations/Data/gene_snp_list.RData")
#load("/users/mturchin/LabMisc/RamachandranLab/MAPITR_temp1/Simulations/Data/gene_ids.RData")
#load("/users/mturchin/LabMisc/RamachandranLab/MAPITR_temp1/Simulations/Data/chromosome16_snps.RData")
#
#gene_list = list()
#
#for(i in 1:ncol(gene_snp_list)){
# x = unlist(gene_snp_list[[i]])
# gene_list[[i]] = x[!is.na(x)]
# names(gene_list)[i] = colnames(gene_snp_list)[i]
#}
# regions <- list(); regions[[1]] <- s1a;
#
# save.image("20200814_vs2_temp1.RData") #diff pve/rho vals
#From: https://github.com/r-spatialecology/landscapemetrics/issues/52, https://stackoverflow.com/questions/40592054/large-matrices-in-rcpparmadillo-via-the-arma-64bit-word-define, https://stackoverflow.com/questions/21944695/rcpparmadillo-and-arma-namespace, https://github.com/RcppCore/RcppArmadillo/pull/88, https://github.com/gvegayon/arma64bit, https://cran.r-project.org/web/packages/RcppArmadillo/RcppArmadillo.pdf
#library(Rcpp); library(RcppArmadillo); library(RcppParallel); library(CompQuadForm); library(Matrix); library(MASS); library(truncnorm)
library("devtools"); devtools::load_all();
X <- read.table("/users/mturchin/LabMisc/RamachandranLab/MAPITR/temp1/SimData/SimData2.Genotypes.txt.gz", header=T);
Y <- read.table("/users/mturchin/LabMisc/RamachandranLab/MAPITR/temp1/SimData/SimData2.Phenotype.txt", header=F);
Pathways <- read.table("/users/mturchin/LabMisc/RamachandranLab/MAPITR/temp1/SimData/SimData2.Pathways.txt", header=F);
Pathways.Full <- lapply(strsplit(as.character(Pathways[,2]), ","), as.numeric);
regions <- Pathways.Full;
regions2 <- lapply(regions, function(x) { return(x-1);});
Xmean=apply(X, 2, mean); Xsd=apply(X, 2, sd); X=t((t(X)-Xmean)/Xsd)
cores = parallel::detectCores()
Y30 <- c();
for (i in 1:length(regions)) { Y30 <- cbind(Y30, residuals(lm(as.matrix(Y) ~ as.matrix(X[,regions[[i]]]) - 1))); };
ptm <- proc.time() #Start clock
vc.mod = MAPITRBase(t(X),Y30,regions,cores = cores)
proc.time() - ptm #Stop clock
### Apply Davies Exact Method ###
vc.ts = vc.mod$Est
names(vc.ts) = names(regions)
pvals = c()
for(i in 1:length(vc.ts)){
lambda = sort(vc.mod$Eigenvalues[,i],decreasing = T)
Davies_Method = CompQuadForm::davies(vc.mod$Est[i], lambda = lambda, acc=1e-8)
pvals[i] = 2*min(1-Davies_Method$Qq,Davies_Method$Qq)
names(pvals)[i] = names(vc.ts[i])
}
pvals
#Data1.mean <- apply(Data1, 2, mean); Data1.sd <- apply(Data1, 2, sd); Data1 <- t((t(Data1)-Data1.mean)/Data1.sd);
#Going from main MAPITR functions
#From: https://stackoverflow.com/questions/733926/openmp-coding-warning-ignoring-pragma-omp-parallel
library("devtools"); devtools::load_all();
X <- read.table("/users/mturchin/LabMisc/RamachandranLab/MAPITR/temp1/SimData/SimData2.Genotypes.txt.gz", header=T);
Y <- read.table("/users/mturchin/LabMisc/RamachandranLab/MAPITR/temp1/SimData/SimData2.Phenotype.txt", header=F);
Pathways <- read.table("/users/mturchin/LabMisc/RamachandranLab/MAPITR/temp1/SimData/SimData2.Pathways.txt", header=F);
Pathways.Full <- lapply(strsplit(as.character(Pathways[,2]), ","), as.numeric);
regions <- Pathways.Full;
#Output1 <- MAPITR(X, Y, regions);
Output1 <- MAPITRmain(X, Y, Pathways);
Genotypes <- X
Phenotype <- Y
#Pathways <- Pathways
GRM_Grand = NULL; GRM_Pathway = NULL; Covariates = NULL; CenterStandardize = TRUE; RegressPhenotypes = TRUE; PrintProgress = FALSE
MAPITRprocessing <- list()
MAPITRoutput <- list()
MAPITRoutput$LogFile <- c()
MAPITRoutput$pValues <- NULL
MAPITRoutput$PVE <- NULL
cores = parallel::detectCores()
MAPITRoutput$LogFile <- rbind(MAPITRoutput$LogFile, paste(format(Sys.time()), " -- beginning MAPITR.", sep=""))
if (PrintProgress == TRUE) {
write(paste(format(Sys.time()), " -- beginning MAPITR.", sep=""), stderr())
}
#Data Checks
MAPITRoutput$LogFile <- DataChecks(Phenotype, Genotypes, Pathways, Covariates, MAPITRoutput$LogFile)
#Preprocessing Data
MAPITRoutput.temp1 <- PreprocessData(Phenotype, Genotypes, Pathways, Covariates, CenterStandardize, RegressPhenotypes, MAPITRoutput$LogFile)
PhenotypeMatrix <- MAPITRoutput.temp1$PhenotypeMatrix
Genotypes <- MAPITRoutput.temp1$Genotypes
Pathways.Full <- MAPITRoutput.temp1$Pathways.Full
MAPITRoutput$LogFile <- MAPITRoutput.temp1$LogFile
rm(MAPITRoutput.temp1)
print("yaya2")
# save.image("20200820_temp1.RData")
library("devtools");
devtools::load_all();
load("20200820_temp1.RData")
RunMAPITR.Base.Output.temp1 <- MAPITRBase(t(as.matrix(Genotypes)),as.matrix(PhenotypeMatrix),Pathways.Full,cores=cores)
# RunMAPITR.Base.Output.temp2 <- MAPITRBase2(t(Genotypes),PhenotypeMatrix,Pathways.Full,cores=cores)
# RunMAPITR.Base.Output.temp3 <- MAPITRBase3(t(Genotypes),PhenotypeMatrix,Pathways.Full,cores=cores)
# RunMAPITR.Base.Output.temp4 <- MAPITRBase4(t(Genotypes))
vc.ts = RunMAPITR.Base.Output.temp1$Est
names(vc.ts) = names(Pathways.Full)
pvals = c()
for(i in 1:length(vc.ts)){
lambda = sort(RunMAPITR.Base.Output.temp1$Eigenvalues[,i],decreasing = T)
Davies_Method = CompQuadForm::davies(RunMAPITR.Base.Output.temp1$Est[i], lambda = lambda, acc=1e-8)
pvals[i] = 2*min(1-Davies_Method$Qq,Davies_Method$Qq)
names(pvals)[i] = names(vc.ts[i])
}
pvals
#Going from main MAPITR functions
#From: https://stackoverflow.com/questions/733926/openmp-coding-warning-ignoring-pragma-omp-parallel
library("devtools"); devtools::load_all();
X <- read.table("/users/mturchin/LabMisc/RamachandranLab/MAPITR/temp1/SimData/SimData2.Genotypes.txt.gz", header=T);
Y <- read.table("/users/mturchin/LabMisc/RamachandranLab/MAPITR/temp1/SimData/SimData2.Phenotype.txt", header=F);
Pathways <- read.table("/users/mturchin/LabMisc/RamachandranLab/MAPITR/temp1/SimData/SimData2.Pathways.txt", header=F);
Pathways.Full <- lapply(strsplit(as.character(Pathways[,2]), ","), as.numeric);
regions <- Pathways.Full;
#Output1 <- MAPITR(X, Y, regions);
Output1 <- MAPITRmain(X, Y, Pathways);
Output2 <- MAPITR(X, Y, Pathways);
MAPITR_SimData_Genotypes <- X;
MAPITR_SimData_Phenotype <- Y;
MAPITR_SimData_Pathways <- Pathways;
#20200823 NOTE -- moving to use 'TestData' as 'SimData' since 'SimData' is too large, throws warnings in the devtools::check() process
#saveCompress1 <- "bzip2"
#save(MAPITR_SimData_Genotypes, file="/users/mturchin/LabMisc/RamachandranLab/MAPITR/data/MAPITR_SimData_Genotypes.rda", compresss=saveCompress1);
#save(MAPITR_SimData_Phenotype, file="/users/mturchin/LabMisc/RamachandranLab/MAPITR/data/MAPITR_SimData_Phenotype.rda");
#save(MAPITR_SimData_Pathways, file="/users/mturchin/LabMisc/RamachandranLab/MAPITR/data/MAPITR_SimData_Pathways.rda");
# Trying to create a unit test 'sized' simulated dataset
#20200831 NOTE -- below was for the first try at CRAN submission, the second try was with the leaner Data2 version
#set.seed(173463);
#Data2 <- Data1[sample(1:nrow(Data1), 750), sample(1:ncol(Data1), 1000)];
#set.seed(214756); (550)
#test1(1245653) #"Pathway1" "2.51417753771577e-06" (500 indvs, 750 SNPs, 75 pathway SNPs)
#test1(121910061) #"Pathway1" "0.00311225500146861" "Pathway2" "0.0778531718360918" (500 indvs, 750 SNPs, 100 pathway SNPs)
#test1(19061) #"Pathway1" "0.00305002076201233" (500 indvs, 750 SNPs, 100 pathway SNPs)
library("devtools"); devtools::load_all();
Data1 <- read.table("/users/mturchin/LabMisc/RamachandranLab/MAPITR/temp1/SimData/ukb_chrAll_v3.British.Ran10000.QCed.reqDrop.QCed.dropRltvs.PCAdrop.sort.ImptHRC.dose.100geno.raw.edit.Simulation.cutdwn.vs3.gz", header=T);
#test1 <- function(seed1) {
#set.seed(seed1)
set.seed(19061)
Data2 <- Data1[sample(1:nrow(Data1), 500), sample(1:ncol(Data1), 750)];
#Data3 <- apply(Data2, 2, function(x) { MAF <- sum(x)/(2*length(x)); return(rbinom(length(x), 2, MAF)); });
#Data3 <- c(); for (i in 1:ncol(Data2)) { Data3 <- cbind(Data3, rbinom(nrow(Data2), 2, runif(1,.4,.6))); };
Data3 <- c(); for (i in 1:ncol(Data2)) { Data3 <- cbind(Data3, rbinom(nrow(Data2), 2, .5)); };
X = Data3;
#X <- Data3[sample(1:nrow(Data3), 100), sample(1:ncol(Data3), 1000)];
Xmean=apply(X, 2, mean); Xsd=apply(X, 2, sd); X=t((t(X)-Xmean)/Xsd)
ind = nrow(X); nsnp = ncol(X)
### Define the Simulation Parameters ###
n.datasets = 1 #Total Number of Simulations
pve = 0.9; #Heritability of the trait
rho = 0.1; #Proportion of the heritability caused by additive effects {0.8, 0.5}
### Set Up Causal SNPs
n.pathways = 2 #Number of Pathways
ncausal1a = 100; ncausal1b = 650 #
ncausal2a = 100; ncausal2b = 1000 #
#Select Causal Pathways
snp.ids = 1:ncol(X)
s1a=sample(snp.ids, ncausal1a, replace=F)
s1b=sample(snp.ids[-s1a], ncausal1b, replace=F)
regions <- list(); regions[[1]] <- s1a;
for (i in 2:5) {
regions[[i]] <- sample(snp.ids, 100, replace=F);
}
#Additive Effects
snps1 = unlist(regions[c(1,2,3,4,5)])
Xmarginal = X[,snps1]
beta=rnorm(dim(Xmarginal)[2])
y_marginal=c(Xmarginal%*%beta)
beta=beta*sqrt(pve*rho/var(y_marginal))
y_marginal=Xmarginal%*%beta
### Simulate Pairwise Interaction matrix ###
Xepi = c(); b = c()
snps2 = s1a;
for(k in 1:length(snps2)){
print(k)
Xepi = cbind(Xepi,X[,snps2[k]]*X[,s1b])
}
### Simulate the Pairwise Effects ###
beta=rnorm(dim(Xepi)[2])
y_epi=c(Xepi%*%beta)
beta=beta*sqrt(pve*(1-rho)/var(y_epi))
y_epi=Xepi%*%beta
### Simulate the (Environmental) Error/Noise ###
y_err=rnorm(ind)
y_err=y_err*sqrt((1-pve)/var(y_err))
### Simulate the Total Phenotypes ###
y=y_marginal+y_epi+y_err
### Check dimensions ###
dim(X); dim(y)
#Pathway Formatting
Pathways <- matrix(unlist(regions), nrow=5, byrow=5)
Pathways.Edits <- apply(Pathways, 1, function(x) { return(paste(x, collapse=",")); }); Pathways.Edits <- cbind(1:length(Pathways.Edits), Pathways.Edits); Pathways.Edits <- cbind(rep("Pathway", nrow(Pathways.Edits)), Pathways.Edits); Pathways.Edits.2 <- apply(Pathways.Edits[,1:2], 1, function(x) { return(paste(x, collapse="")); }); Pathways.Edits <- cbind(Pathways.Edits.2, Pathways.Edits[,3]);
#Output1 <- MAPITRmain(X, y, Pathways.Edits);
Output1 <- MAPITR(X, y, Pathways.Edits);
Output1$Results
#return(Output1$Results);
#}
#Output writing
write.table(Data3, file="/users/mturchin/LabMisc/RamachandranLab/MAPITR/temp1/SimData/SimData4.Genotypes.txt", quote=FALSE, row.names=FALSE, col.names=TRUE);
write.table(y, file="/users/mturchin/LabMisc/RamachandranLab/MAPITR/temp1/SimData/SimData4.Phenotype.txt", quote=FALSE, row.names=FALSE, col.names=FALSE);
write.table(Pathways.Edits, file="/users/mturchin/LabMisc/RamachandranLab/MAPITR/temp1/SimData/SimData4.Pathways.txt", quote=FALSE, row.names=FALSE, col.names=FALSE);
system("gzip -f /users/mturchin/LabMisc/RamachandranLab/MAPITR/temp1/SimData/SimData4.Genotypes.txt");
#write.table(Data3, file="/users/mturchin/LabMisc/RamachandranLab/MAPITR/temp1/SimData/SimData3.Genotypes.txt", quote=FALSE, row.names=FALSE, col.names=TRUE);
#write.table(y, file="/users/mturchin/LabMisc/RamachandranLab/MAPITR/temp1/SimData/SimData3.Phenotype.txt", quote=FALSE, row.names=FALSE, col.names=FALSE);
#write.table(Pathways.Edits, file="/users/mturchin/LabMisc/RamachandranLab/MAPITR/temp1/SimData/SimData3.Pathways.txt", quote=FALSE, row.names=FALSE, col.names=FALSE);
#system("gzip -f /users/mturchin/LabMisc/RamachandranLab/MAPITR/temp1/SimData/SimData3.Genotypes.txt");
library("devtools"); devtools::load_all();
X <- read.table("/users/mturchin/LabMisc/RamachandranLab/MAPITR/temp1/SimData/SimData3.Genotypes.txt.gz", header=T);
Y <- read.table("/users/mturchin/LabMisc/RamachandranLab/MAPITR/temp1/SimData/SimData3.Phenotype.txt", header=F);
Pathways <- read.table("/users/mturchin/LabMisc/RamachandranLab/MAPITR/temp1/SimData/SimData3.Pathways.txt", header=F);
#Output1 <- MAPITRmain(X, Y, Pathways);
#Output1$Results
Output2 <- MAPITR(X, Y, Pathways);
Output2$Results