R/originalCode/create_fimo_predictors_ref.R
In alice-szeliga/keles-pipeline: A Model to Predict Regulatory and Expression Modulating eQTLs
setwd("/p/keles/CAGI2015/volumeA/Data/After_Nov5/")
trainingD <- read.table("4-eQTL-causal_SNPs_sample_v2.txt", header = T)
dim(trainingD)
#[1] 3044 25
length(unique(trainingD$eQTL_gene))
#[1] 1052
scoreList <- vector("list", 13)
names(scoreList) <- c("fb_minPval", "fb_maxScore", "fb_numOcc", "fb_sumScore",
"en_minPval", "en_maxScore", "en_numOcc", "en_sumScore",
"ja_minPval", "ja_maxScore", "ja_numOcc", "ja_sumScore", "db_score")
# Code for exploratory analysis and extracting scores
#########################################
# #
# Exploratory analysis with FactorBook #
# PWM scores #
# #
#########################################
setwd("/p/keles/CAGI2015/volumeB/Data/")
trainingD <- read.table("4-eQTL-causal_SNPs_sample_v2.txt", header = T)
#setwd("/Users/keles/Research_Projects/CAGI2015/Fimo/")
setwd("/p/keles/CAGI2015/volumeB/Fimo")
load("Training_ref_thres0.001_FactorBook_minPval.RData")
load("Training_ref_thres0.001_FactorBook_maxScore.RData")
load("Training_ref_thres0.001_FactorBook_sumScore.RData")
load("Training_ref_thres0.001_FactorBook_numOcc.RData")
fimo.minPval <- out.1
fimo.maxScore <- out.2
fimo.numOcc <- out.3
fimo.sumScore <- out.4
fimoS <- fimo.minPval
oIndex <- as.numeric(apply(as.matrix(rownames(fimoS)), 1, function(x){strsplit(x, "_SNP_")[[1]][2]}))
fimoS.ordered <- matrix(1, nrow(trainingD), ncol(fimoS))
colnames(fimoS.ordered) <- colnames(fimoS)
fimoS.ordered[oIndex, ] <- fimoS
scoreList[["fb_minPval"]] <- fimoS.ordered
fimoS <- fimo.maxScore
oIndex <- as.numeric(apply(as.matrix(rownames(fimoS)), 1, function(x){strsplit(x, "_SNP_")[[1]][2]}))
fimoS.ordered <- matrix(0, nrow(trainingD), ncol(fimoS))
colnames(fimoS.ordered) <- colnames(fimoS)
fimoS.ordered[oIndex, ] <- fimoS
scoreList[["fb_maxScore"]] <- fimoS.ordered
fimoS <- fimo.numOcc
oIndex <- as.numeric(apply(as.matrix(rownames(fimoS)), 1, function(x){strsplit(x, "_SNP_")[[1]][2]}))
fimoS.ordered <- matrix(0, nrow(trainingD), ncol(fimoS))
colnames(fimoS.ordered) <- colnames(fimoS)
fimoS.ordered[oIndex, ] <- fimoS
scoreList[["fb_numOcc"]] <- fimoS.ordered
fimoS <- fimo.sumScore
oIndex <- as.numeric(apply(as.matrix(rownames(fimoS)), 1, function(x){strsplit(x, "_SNP_")[[1]][2]}))
fimoS.ordered <- matrix(0, nrow(trainingD), ncol(fimoS))
colnames(fimoS.ordered) <- colnames(fimoS)
fimoS.ordered[oIndex, ] <- fimoS
scoreList[["fb_sumScore"]] <- fimoS.ordered
# Simple correlations
fimoS.ordered <- scoreList[["fb_minPval"]]
fimoS.ordered.binary <- apply(fimoS.ordered, 2, function(x){res <- x; res[x>=0.05]<-0; res[x <0.05] <-1; return(res)}) #PWM match crude
corRes.binary <- apply(fimoS.ordered.binary, 2, function(x){ifelse(length(unique(x)) > 1, wilcox.test(trainingD$C.A.log2FC~x)$p.value,1)})
#order(corRes.binary, decreasing = FALSE)
cat("minPVal\n")
summary(-log10(corRes.binary))
cat("maxScore\n")
fimoS.ordered <- scoreList[["fb_maxScore"]]
corRes <- apply(fimoS.ordered, 2, function(x){cor(x, trainingD$C.A.log2FC)})
#order(abs(corRes), decreasing = TRUE)
summary(abs(corRes))
cat("sumScore\n")
fimoS.ordered <- scoreList[["fb_sumScore"]]
corRes <- apply(fimoS.ordered, 2, function(x){cor(x, trainingD$C.A.log2FC)})
#order(abs(corRes), decreasing = TRUE)
#sort(abs(corRes), decreasing = TRUE)[1:10]
summary(abs(corRes))
cat("numOcc\n")
fimoS.ordered <- scoreList[["fb_numOcc"]]
corRes <- apply(fimoS.ordered, 2, function(x){cor(x, trainingD$C.A.log2FC)})
#order(abs(corRes), decreasing = TRUE)
#sort(abs(corRes), decreasing = TRUE)[1:10]
summary(abs(corRes))
#####################################
# #
# Exploratory analysis with Deepbind#
# scores #
# #
#####################################
setwd("/p/keles/CAGI2015/volumeA/Data/After_Nov5")
trainingD <- read.table("4-eQTL-causal_SNPs_sample_v2.txt", header = T)
setwd("/p/keles/CAGI2015/volumeB/DeepBind/deepbind")
db <- read.table("ref_SS.v1_deepbind.txt", header = TRUE)
scoreList[["db_score"]] <- db
## Plots
setwd("/Users/keles/Research_Projects/CAGI2015/Data/After_Nov5")
trainingD <- read.table("4-eQTL-causal_SNPs_sample_v2.txt", header = T)
setwd("/Users/keles/Research_Projects/CAGI2015/DeepBind/")
db <- read.table("Training_ref_3044_deepbind_927.txt", header = TRUE)
corRes <- apply(db, 2, function(x){cor(x, trainingD$C.A.log2FC)})
index <- order(abs(corRes), decreasing = TRUE)[1:50]
require(ggplot2)
require(reshape2)
db.sub <- db[, index]
colnames(db.sub) <- paste("db_", 1:50, sep = "")
dd <- cbind(trainingD, db.sub)
ddd <- dd
ddd$grp <- paste(ddd$Regulatory_Hit, ddd$emVar_Hit, sep = "")
ggplot(ddd, aes(x = db_2, y =C.A.log2FC)) + geom_smooth(method='lm',formula=y~x) + geom_point(aes(colour=grp)) + facet_wrap( ~ grp, nrow = 1)
#########################################
# #
# Exploratory analysis with ENCODE #
# PWM scores #
# #
#########################################
setwd("/p/keles/CAGI2015/volumeA/Data/After_Nov5")
trainingD <- read.table("4-eQTL-causal_SNPs_sample_v2.txt", header = T)
#setwd("/Users/keles/Research_Projects/CAGI2015/Fimo/")
setwd("/p/keles/CAGI2015/volumeB/Fimo")
load("Training_ref_thres0.001_ENCODE_minPval.RData")
load("Training_ref_thres0.001_ENCODE_maxScore.RData")
load("Training_ref_thres0.001_ENCODE_sumScore.RData")
load("Training_ref_thres0.001_ENCODE_numOcc.RData")
fimo.minPval <- out.1
fimo.maxScore <- out.2
fimo.numOcc <- out.3
fimo.sumScore <- out.4
fimoS <- fimo.minPval
oIndex <- as.numeric(apply(as.matrix(rownames(fimoS)), 1, function(x){strsplit(x, "_SNP_")[[1]][2]}))
fimoS.ordered <- matrix(1, nrow(trainingD), ncol(fimoS))
colnames(fimoS.ordered) <- colnames(fimoS)
fimoS.ordered[oIndex, ] <- fimoS
scoreList[["en_minPval"]] <- fimoS.ordered
fimoS <- fimo.maxScore
oIndex <- as.numeric(apply(as.matrix(rownames(fimoS)), 1, function(x){strsplit(x, "_SNP_")[[1]][2]}))
fimoS.ordered <- matrix(0, nrow(trainingD), ncol(fimoS))
colnames(fimoS.ordered) <- colnames(fimoS)
fimoS.ordered[oIndex, ] <- fimoS
scoreList[["en_maxScore"]] <- fimoS.ordered
fimoS <- fimo.numOcc
oIndex <- as.numeric(apply(as.matrix(rownames(fimoS)), 1, function(x){strsplit(x, "_SNP_")[[1]][2]}))
fimoS.ordered <- matrix(0, nrow(trainingD), ncol(fimoS))
colnames(fimoS.ordered) <- colnames(fimoS)
fimoS.ordered[oIndex, ] <- fimoS
scoreList[["en_numOcc"]] <- fimoS.ordered
fimoS <- fimo.sumScore
oIndex <- as.numeric(apply(as.matrix(rownames(fimoS)), 1, function(x){strsplit(x, "_SNP_")[[1]][2]}))
fimoS.ordered <- matrix(0, nrow(trainingD), ncol(fimoS))
colnames(fimoS.ordered) <- colnames(fimoS)
fimoS.ordered[oIndex, ] <- fimoS
scoreList[["en_sumScore"]] <- fimoS.ordered
# Simple correlations
fimoS.ordered <- scoreList[["en_minPval"]]
fimoS.ordered.binary <- apply(fimoS.ordered, 2, function(x){res <- x; res[x>=0.05]<-0; res[x <0.05] <-1; return(res)}) #PWM match crude
corRes.binary <- apply(fimoS.ordered.binary, 2, function(x){ifelse(length(unique(x)) > 1, wilcox.test(trainingD$C.A.log2FC~x)$p.value,1)})
#order(corRes.binary, decreasing = FALSE)
cat("minPVal\n")
summary(-log10(corRes.binary))
fimoS.ordered <- scoreList[["en_maxScore"]]
corRes <- apply(fimoS.ordered, 2, function(x){cor(x, trainingD$C.A.log2FC)})
#order(abs(corRes), decreasing = TRUE)
#sort(abs(corRes), decreasing = TRUE)[1:10]
cat("maxScore\n")
summary(abs(corRes))
fimoS.ordered <- scoreList[["en_sumScore"]]
corRes <- apply(fimoS.ordered, 2, function(x){cor(x, trainingD$C.A.log2FC)})
#order(abs(corRes), decreasing = TRUE)
#sort(abs(corRes), decreasing = TRUE)[1:10]
cat("sumScore\n")
summary(abs(corRes))
fimoS.ordered <- scoreList[["en_numOcc"]]
corRes <- apply(fimoS.ordered, 2, function(x){cor(x, trainingD$C.A.log2FC)})
#order(abs(corRes), decreasing = TRUE)
#sort(abs(corRes), decreasing = TRUE)[1:10]
cat("numOcc\n")
summary(abs(corRes))
#########################################
# #
# Exploratory analysis with Jaspar #
# PWM scores #
# #
#########################################
setwd("/p/keles/CAGI2015/volumeB/Data/")
trainingD <- read.table("4-eQTL-causal_SNPs_sample_v2.txt", header = T)
#setwd("/Users/keles/Research_Projects/CAGI2015/Fimo/")
setwd("/p/keles/CAGI2015/volumeB/Fimo")
load("Training_ref_thres0.001_Jaspar_minPval.RData")
load("Training_ref_thres0.001_Jaspar_maxScore.RData")
load("Training_ref_thres0.001_Jaspar_sumScore.RData")
load("Training_ref_thres0.001_Jaspar_numOcc.RData")
fimo.minPval <- out.1
fimo.maxScore <- out.2
fimo.numOcc <- out.3
fimo.sumScore <- out.4
fimoS <- fimo.minPval
oIndex <- as.numeric(apply(as.matrix(rownames(fimoS)), 1, function(x){strsplit(x, "_SNP_")[[1]][2]}))
fimoS.ordered <- matrix(1, nrow(trainingD), ncol(fimoS))
colnames(fimoS.ordered) <- colnames(fimoS)
fimoS.ordered[oIndex, ] <- fimoS
scoreList[["ja_minPval"]] <- fimoS.ordered
fimoS <- fimo.maxScore
oIndex <- as.numeric(apply(as.matrix(rownames(fimoS)), 1, function(x){strsplit(x, "_SNP_")[[1]][2]}))
fimoS.ordered <- matrix(0, nrow(trainingD), ncol(fimoS))
colnames(fimoS.ordered) <- colnames(fimoS)
fimoS.ordered[oIndex, ] <- fimoS
scoreList[["ja_maxScore"]] <- fimoS.ordered
fimoS <- fimo.numOcc
oIndex <- as.numeric(apply(as.matrix(rownames(fimoS)), 1, function(x){strsplit(x, "_SNP_")[[1]][2]}))
fimoS.ordered <- matrix(0, nrow(trainingD), ncol(fimoS))
colnames(fimoS.ordered) <- colnames(fimoS)
fimoS.ordered[oIndex, ] <- fimoS
scoreList[["ja_numOcc"]] <- fimoS.ordered
fimoS <- fimo.sumScore
oIndex <- as.numeric(apply(as.matrix(rownames(fimoS)), 1, function(x){strsplit(x, "_SNP_")[[1]][2]}))
fimoS.ordered <- matrix(0, nrow(trainingD), ncol(fimoS))
colnames(fimoS.ordered) <- colnames(fimoS)
fimoS.ordered[oIndex, ] <- fimoS
scoreList[["ja_sumScore"]] <- fimoS.ordered
# Simple correlations
fimoS.ordered <- scoreList[["ja_minPval"]]
fimoS.ordered.binary <- apply(fimoS.ordered, 2, function(x){res <- x; res[x>=0.05]<-0; res[x <0.05] <-1; return(res)}) #PWM match crude
corRes.binary <- apply(fimoS.ordered.binary, 2, function(x){ifelse(length(unique(x)) > 1, wilcox.test(trainingD$C.A.log2FC~x)$p.value,1)})
#order(corRes.binary, decreasing = FALSE)
cat("minPVal\n")
summary(-log10(corRes.binary))
cat("maxScore\n")
fimoS.ordered <- scoreList[["ja_maxScore"]]
corRes <- apply(fimoS.ordered, 2, function(x){cor(x, trainingD$C.A.log2FC)})
#order(abs(corRes), decreasing = TRUE)
summary(abs(corRes))
cat("sumScore\n")
fimoS.ordered <- scoreList[["ja_sumScore"]]
corRes <- apply(fimoS.ordered, 2, function(x){cor(x, trainingD$C.A.log2FC)})
#order(abs(corRes), decreasing = TRUE)
#sort(abs(corRes), decreasing = TRUE)[1:10]
summary(abs(corRes))
cat("numOcc\n")
fimoS.ordered <- scoreList[["ja_numOcc"]]
corRes <- apply(fimoS.ordered, 2, function(x){cor(x, trainingD$C.A.log2FC)})
#order(abs(corRes), decreasing = TRUE)
#sort(abs(corRes), decreasing = TRUE)[1:10]
summary(abs(corRes))
#########################################
# #
# Output scores #
# #
# #
#########################################
unlist(lapply(scoreList, ncol))
fb_minPval fb_maxScore fb_numOcc fb_sumScore en_minPval en_maxScore
79 79 79 79 2065 2065
en_numOcc en_sumScore ja_minPval ja_maxScore ja_numOcc ja_sumScore
2065 2065 1082 1082 1082 1082
db_score
927
setwd("/p/keles/CAGI2015/volumeB/Predictors/")
save(scoreList, file = "scoreList_Training_ref_thres0.001_pwm.RData")
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setwd("/p/keles/CAGI2015/volumeA/Data/After_Nov5/")
trainingD <- read.table("4-eQTL-causal_SNPs_sample_v2.txt", header = T)
dim(trainingD)
#[1] 3044 25
length(unique(trainingD$eQTL_gene))
#[1] 1052
scoreList <- vector("list", 13)
names(scoreList) <- c("fb_minPval", "fb_maxScore", "fb_numOcc", "fb_sumScore",
"en_minPval", "en_maxScore", "en_numOcc", "en_sumScore",
"ja_minPval", "ja_maxScore", "ja_numOcc", "ja_sumScore", "db_score")
# Code for exploratory analysis and extracting scores
#########################################
# #
# Exploratory analysis with FactorBook #
# PWM scores #
# #
#########################################
setwd("/p/keles/CAGI2015/volumeB/Data/")
trainingD <- read.table("4-eQTL-causal_SNPs_sample_v2.txt", header = T)
#setwd("/Users/keles/Research_Projects/CAGI2015/Fimo/")
setwd("/p/keles/CAGI2015/volumeB/Fimo")
load("Training_ref_thres0.001_FactorBook_minPval.RData")
load("Training_ref_thres0.001_FactorBook_maxScore.RData")
load("Training_ref_thres0.001_FactorBook_sumScore.RData")
load("Training_ref_thres0.001_FactorBook_numOcc.RData")
fimo.minPval <- out.1
fimo.maxScore <- out.2
fimo.numOcc <- out.3
fimo.sumScore <- out.4
fimoS <- fimo.minPval
oIndex <- as.numeric(apply(as.matrix(rownames(fimoS)), 1, function(x){strsplit(x, "_SNP_")[[1]][2]}))
fimoS.ordered <- matrix(1, nrow(trainingD), ncol(fimoS))
colnames(fimoS.ordered) <- colnames(fimoS)
fimoS.ordered[oIndex, ] <- fimoS
scoreList[["fb_minPval"]] <- fimoS.ordered
fimoS <- fimo.maxScore
oIndex <- as.numeric(apply(as.matrix(rownames(fimoS)), 1, function(x){strsplit(x, "_SNP_")[[1]][2]}))
fimoS.ordered <- matrix(0, nrow(trainingD), ncol(fimoS))
colnames(fimoS.ordered) <- colnames(fimoS)
fimoS.ordered[oIndex, ] <- fimoS
scoreList[["fb_maxScore"]] <- fimoS.ordered
fimoS <- fimo.numOcc
oIndex <- as.numeric(apply(as.matrix(rownames(fimoS)), 1, function(x){strsplit(x, "_SNP_")[[1]][2]}))
fimoS.ordered <- matrix(0, nrow(trainingD), ncol(fimoS))
colnames(fimoS.ordered) <- colnames(fimoS)
fimoS.ordered[oIndex, ] <- fimoS
scoreList[["fb_numOcc"]] <- fimoS.ordered
fimoS <- fimo.sumScore
oIndex <- as.numeric(apply(as.matrix(rownames(fimoS)), 1, function(x){strsplit(x, "_SNP_")[[1]][2]}))
fimoS.ordered <- matrix(0, nrow(trainingD), ncol(fimoS))
colnames(fimoS.ordered) <- colnames(fimoS)
fimoS.ordered[oIndex, ] <- fimoS
scoreList[["fb_sumScore"]] <- fimoS.ordered
# Simple correlations
fimoS.ordered <- scoreList[["fb_minPval"]]
fimoS.ordered.binary <- apply(fimoS.ordered, 2, function(x){res <- x; res[x>=0.05]<-0; res[x <0.05] <-1; return(res)}) #PWM match crude
corRes.binary <- apply(fimoS.ordered.binary, 2, function(x){ifelse(length(unique(x)) > 1, wilcox.test(trainingD$C.A.log2FC~x)$p.value,1)})
#order(corRes.binary, decreasing = FALSE)
cat("minPVal\n")
summary(-log10(corRes.binary))
cat("maxScore\n")
fimoS.ordered <- scoreList[["fb_maxScore"]]
corRes <- apply(fimoS.ordered, 2, function(x){cor(x, trainingD$C.A.log2FC)})
#order(abs(corRes), decreasing = TRUE)
summary(abs(corRes))
cat("sumScore\n")
fimoS.ordered <- scoreList[["fb_sumScore"]]
corRes <- apply(fimoS.ordered, 2, function(x){cor(x, trainingD$C.A.log2FC)})
#order(abs(corRes), decreasing = TRUE)
#sort(abs(corRes), decreasing = TRUE)[1:10]
summary(abs(corRes))
cat("numOcc\n")
fimoS.ordered <- scoreList[["fb_numOcc"]]
corRes <- apply(fimoS.ordered, 2, function(x){cor(x, trainingD$C.A.log2FC)})
#order(abs(corRes), decreasing = TRUE)
#sort(abs(corRes), decreasing = TRUE)[1:10]
summary(abs(corRes))
#####################################
# #
# Exploratory analysis with Deepbind#
# scores #
# #
#####################################
setwd("/p/keles/CAGI2015/volumeA/Data/After_Nov5")
trainingD <- read.table("4-eQTL-causal_SNPs_sample_v2.txt", header = T)
setwd("/p/keles/CAGI2015/volumeB/DeepBind/deepbind")
db <- read.table("ref_SS.v1_deepbind.txt", header = TRUE)
scoreList[["db_score"]] <- db
## Plots
setwd("/Users/keles/Research_Projects/CAGI2015/Data/After_Nov5")
trainingD <- read.table("4-eQTL-causal_SNPs_sample_v2.txt", header = T)
setwd("/Users/keles/Research_Projects/CAGI2015/DeepBind/")
db <- read.table("Training_ref_3044_deepbind_927.txt", header = TRUE)
corRes <- apply(db, 2, function(x){cor(x, trainingD$C.A.log2FC)})
index <- order(abs(corRes), decreasing = TRUE)[1:50]
require(ggplot2)
require(reshape2)
db.sub <- db[, index]
colnames(db.sub) <- paste("db_", 1:50, sep = "")
dd <- cbind(trainingD, db.sub)
ddd <- dd
ddd$grp <- paste(ddd$Regulatory_Hit, ddd$emVar_Hit, sep = "")
ggplot(ddd, aes(x = db_2, y =C.A.log2FC)) + geom_smooth(method='lm',formula=y~x) + geom_point(aes(colour=grp)) + facet_wrap( ~ grp, nrow = 1)
#########################################
# #
# Exploratory analysis with ENCODE #
# PWM scores #
# #
#########################################
setwd("/p/keles/CAGI2015/volumeA/Data/After_Nov5")
trainingD <- read.table("4-eQTL-causal_SNPs_sample_v2.txt", header = T)
#setwd("/Users/keles/Research_Projects/CAGI2015/Fimo/")
setwd("/p/keles/CAGI2015/volumeB/Fimo")
load("Training_ref_thres0.001_ENCODE_minPval.RData")
load("Training_ref_thres0.001_ENCODE_maxScore.RData")
load("Training_ref_thres0.001_ENCODE_sumScore.RData")
load("Training_ref_thres0.001_ENCODE_numOcc.RData")
fimo.minPval <- out.1
fimo.maxScore <- out.2
fimo.numOcc <- out.3
fimo.sumScore <- out.4
fimoS <- fimo.minPval
oIndex <- as.numeric(apply(as.matrix(rownames(fimoS)), 1, function(x){strsplit(x, "_SNP_")[[1]][2]}))
fimoS.ordered <- matrix(1, nrow(trainingD), ncol(fimoS))
colnames(fimoS.ordered) <- colnames(fimoS)
fimoS.ordered[oIndex, ] <- fimoS
scoreList[["en_minPval"]] <- fimoS.ordered
fimoS <- fimo.maxScore
oIndex <- as.numeric(apply(as.matrix(rownames(fimoS)), 1, function(x){strsplit(x, "_SNP_")[[1]][2]}))
fimoS.ordered <- matrix(0, nrow(trainingD), ncol(fimoS))
colnames(fimoS.ordered) <- colnames(fimoS)
fimoS.ordered[oIndex, ] <- fimoS
scoreList[["en_maxScore"]] <- fimoS.ordered
fimoS <- fimo.numOcc
oIndex <- as.numeric(apply(as.matrix(rownames(fimoS)), 1, function(x){strsplit(x, "_SNP_")[[1]][2]}))
fimoS.ordered <- matrix(0, nrow(trainingD), ncol(fimoS))
colnames(fimoS.ordered) <- colnames(fimoS)
fimoS.ordered[oIndex, ] <- fimoS
scoreList[["en_numOcc"]] <- fimoS.ordered
fimoS <- fimo.sumScore
oIndex <- as.numeric(apply(as.matrix(rownames(fimoS)), 1, function(x){strsplit(x, "_SNP_")[[1]][2]}))
fimoS.ordered <- matrix(0, nrow(trainingD), ncol(fimoS))
colnames(fimoS.ordered) <- colnames(fimoS)
fimoS.ordered[oIndex, ] <- fimoS
scoreList[["en_sumScore"]] <- fimoS.ordered
# Simple correlations
fimoS.ordered <- scoreList[["en_minPval"]]
fimoS.ordered.binary <- apply(fimoS.ordered, 2, function(x){res <- x; res[x>=0.05]<-0; res[x <0.05] <-1; return(res)}) #PWM match crude
corRes.binary <- apply(fimoS.ordered.binary, 2, function(x){ifelse(length(unique(x)) > 1, wilcox.test(trainingD$C.A.log2FC~x)$p.value,1)})
#order(corRes.binary, decreasing = FALSE)
cat("minPVal\n")
summary(-log10(corRes.binary))
fimoS.ordered <- scoreList[["en_maxScore"]]
corRes <- apply(fimoS.ordered, 2, function(x){cor(x, trainingD$C.A.log2FC)})
#order(abs(corRes), decreasing = TRUE)
#sort(abs(corRes), decreasing = TRUE)[1:10]
cat("maxScore\n")
summary(abs(corRes))
fimoS.ordered <- scoreList[["en_sumScore"]]
corRes <- apply(fimoS.ordered, 2, function(x){cor(x, trainingD$C.A.log2FC)})
#order(abs(corRes), decreasing = TRUE)
#sort(abs(corRes), decreasing = TRUE)[1:10]
cat("sumScore\n")
summary(abs(corRes))
fimoS.ordered <- scoreList[["en_numOcc"]]
corRes <- apply(fimoS.ordered, 2, function(x){cor(x, trainingD$C.A.log2FC)})
#order(abs(corRes), decreasing = TRUE)
#sort(abs(corRes), decreasing = TRUE)[1:10]
cat("numOcc\n")
summary(abs(corRes))
#########################################
# #
# Exploratory analysis with Jaspar #
# PWM scores #
# #
#########################################
setwd("/p/keles/CAGI2015/volumeB/Data/")
trainingD <- read.table("4-eQTL-causal_SNPs_sample_v2.txt", header = T)
#setwd("/Users/keles/Research_Projects/CAGI2015/Fimo/")
setwd("/p/keles/CAGI2015/volumeB/Fimo")
load("Training_ref_thres0.001_Jaspar_minPval.RData")
load("Training_ref_thres0.001_Jaspar_maxScore.RData")
load("Training_ref_thres0.001_Jaspar_sumScore.RData")
load("Training_ref_thres0.001_Jaspar_numOcc.RData")
fimo.minPval <- out.1
fimo.maxScore <- out.2
fimo.numOcc <- out.3
fimo.sumScore <- out.4
fimoS <- fimo.minPval
oIndex <- as.numeric(apply(as.matrix(rownames(fimoS)), 1, function(x){strsplit(x, "_SNP_")[[1]][2]}))
fimoS.ordered <- matrix(1, nrow(trainingD), ncol(fimoS))
colnames(fimoS.ordered) <- colnames(fimoS)
fimoS.ordered[oIndex, ] <- fimoS
scoreList[["ja_minPval"]] <- fimoS.ordered
fimoS <- fimo.maxScore
oIndex <- as.numeric(apply(as.matrix(rownames(fimoS)), 1, function(x){strsplit(x, "_SNP_")[[1]][2]}))
fimoS.ordered <- matrix(0, nrow(trainingD), ncol(fimoS))
colnames(fimoS.ordered) <- colnames(fimoS)
fimoS.ordered[oIndex, ] <- fimoS
scoreList[["ja_maxScore"]] <- fimoS.ordered
fimoS <- fimo.numOcc
oIndex <- as.numeric(apply(as.matrix(rownames(fimoS)), 1, function(x){strsplit(x, "_SNP_")[[1]][2]}))
fimoS.ordered <- matrix(0, nrow(trainingD), ncol(fimoS))
colnames(fimoS.ordered) <- colnames(fimoS)
fimoS.ordered[oIndex, ] <- fimoS
scoreList[["ja_numOcc"]] <- fimoS.ordered
fimoS <- fimo.sumScore
oIndex <- as.numeric(apply(as.matrix(rownames(fimoS)), 1, function(x){strsplit(x, "_SNP_")[[1]][2]}))
fimoS.ordered <- matrix(0, nrow(trainingD), ncol(fimoS))
colnames(fimoS.ordered) <- colnames(fimoS)
fimoS.ordered[oIndex, ] <- fimoS
scoreList[["ja_sumScore"]] <- fimoS.ordered
# Simple correlations
fimoS.ordered <- scoreList[["ja_minPval"]]
fimoS.ordered.binary <- apply(fimoS.ordered, 2, function(x){res <- x; res[x>=0.05]<-0; res[x <0.05] <-1; return(res)}) #PWM match crude
corRes.binary <- apply(fimoS.ordered.binary, 2, function(x){ifelse(length(unique(x)) > 1, wilcox.test(trainingD$C.A.log2FC~x)$p.value,1)})
#order(corRes.binary, decreasing = FALSE)
cat("minPVal\n")
summary(-log10(corRes.binary))
cat("maxScore\n")
fimoS.ordered <- scoreList[["ja_maxScore"]]
corRes <- apply(fimoS.ordered, 2, function(x){cor(x, trainingD$C.A.log2FC)})
#order(abs(corRes), decreasing = TRUE)
summary(abs(corRes))
cat("sumScore\n")
fimoS.ordered <- scoreList[["ja_sumScore"]]
corRes <- apply(fimoS.ordered, 2, function(x){cor(x, trainingD$C.A.log2FC)})
#order(abs(corRes), decreasing = TRUE)
#sort(abs(corRes), decreasing = TRUE)[1:10]
summary(abs(corRes))
cat("numOcc\n")
fimoS.ordered <- scoreList[["ja_numOcc"]]
corRes <- apply(fimoS.ordered, 2, function(x){cor(x, trainingD$C.A.log2FC)})
#order(abs(corRes), decreasing = TRUE)
#sort(abs(corRes), decreasing = TRUE)[1:10]
summary(abs(corRes))
#########################################
# #
# Output scores #
# #
# #
#########################################
unlist(lapply(scoreList, ncol))
fb_minPval fb_maxScore fb_numOcc fb_sumScore en_minPval en_maxScore
79 79 79 79 2065 2065
en_numOcc en_sumScore ja_minPval ja_maxScore ja_numOcc ja_sumScore
2065 2065 1082 1082 1082 1082
db_score
927
setwd("/p/keles/CAGI2015/volumeB/Predictors/")
save(scoreList, file = "scoreList_Training_ref_thres0.001_pwm.RData")
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