Last updated: 2020-03-17
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| Rmd | 6dfae66 | kevinlkx | 2020-03-17 | wflow_publish(“analysis/pi1_QTLsharing_jointLCLs.Rmd”) |
m6AQTL_workflowr/analysis/pi1_QTLsharing_jointLCLs_m6AQTL_logOR.adjusted_leadSNPs_random_combinedtest_noTE.Rmd# options(scipen = 999)
library(ggplot2)
library(qvalue)
library(metap)
bootstrap_pi1_ci <- function(pvalues, N = 1000, ci_percent = 0.8, pi0.method="bootstrap"){
lambda <- seq(0.05, 0.9, 0.05)
pi1_bootstrap <- function(pvalues, pi0.method){
p_samples <- sample(pvalues, replace = T)
pi1_samples <- (1 - pi0est(p_samples, lambda = lambda, pi0.method = pi0.method)$pi0)
return(pi1_samples)
}
pvalues <- pvalues[!is.na(pvalues)]
pi1_hat <- (1 - pi0est(pvalues, lambda = lambda, pi0.method = pi0.method)$pi0)
if(N > 0){
pi1_samples <- replicate(N, pi1_bootstrap(pvalues, pi0.method))
ci <- quantile(pi1_samples, c(0.5 - ci_percent/2, 0.5 + ci_percent/2))
}else{
ci <- rep(pi1_hat,2)
}
result <- c(ci[1], pi1_hat, ci[2])
names(result) <- c("ci_min", "pi1", "ci_max")
return(result)
}
## Fisher's method of combining pvalues from independent tests
combine_p_Fisher <- function(pvalues){
pvalues <- na.omit(pvalues)
if(length(pvalues) == 0){
pvalue_fisher <- NA
}else if(length(pvalues) == 1){
pvalue_fisher <- na.omit(pvalues)
}else{
pvalue_fisher <- metap::sumlog(pvalues)$p # using the Fisher's method in metap package
# actual calculation below:
# chisq_stat <- -2*sum(log(pvalues))
# k <- length(pvalues)
# pvalue_fisher <- pchisq(chisq_stat, df = 2*k, lower.tail = FALSE)
}
return(pvalue_fisher)
}
combine_sumsq_zscores <- function(pvalues){
k <- length(pvalues)
zscore_abs <- p2z(pvalues)
pvalue_combined <- pchisq(sum(zscore_abs^2), df = k, lower.tail = FALSE)
return(pvalue_combined)
}
combine_pvalue_Fisher <- function(pvalue_phenotypes){
p_EDA <- apply(pvalue_phenotypes[,c("Expression", "Decay", "APA")], 1, combine_p_Fisher )
p_all <- apply(pvalue_phenotypes[,c("Expression", "Ribosome", "Protein", "Decay", "APA")], 1, combine_p_Fisher )
pvalue_phenotypes_combined <- data.frame(pvalue_phenotypes,
E.D.A = p_EDA,
all = p_all)
return(pvalue_phenotypes_combined)
}
cbbPalette <- c( "#E69F00", "#D55E00", "#56B4E9", "#F0E442", "#0072B2", "#000000","#CC79A7","#009E73")m6A_version <- "jointPeak_threshold5_MeRIP_HISAT2Map"
m6A_phenotype_name <- "m6APeak_logOR_GC.IP.adjusted_qqnorm"
type_apaQTL <- "dist" # choose the closest SNP-transcript pairs for APA-QTLs
cat("m6A version:", m6A_version, "\n")
cat("m6A phenotype:", m6A_phenotype_name, "\n")
dir_m6AQTL_results <- paste0("/project2/xinhe/m6A/m6A_seq/m6A_QTL/results/hg19/m6A_QTLs/", m6A_version)
dir_output_full <- paste0(dir_m6AQTL_results, "/jointLCLs_analysis/", m6A_phenotype_name, "/m6AQTLs_full_PCs_APA", type_apaQTL)
m6AQTLs_info_full.df <- readRDS(paste0(dir_output_full,"/m6AQTLs_info_full.rds"))[,c("SNP", "PEAK", "genename_snp_pair")]
pvalue_joint_m6AQTLs_full.df <- readRDS(paste0(dir_output_full,"/pvalue_jointLCLs_m6AQTLs_full.rds"))
colnames(pvalue_joint_m6AQTLs_full.df) <- gsub("Txn", "", colnames(pvalue_joint_m6AQTLs_full.df))
dir_output <- "/project2/xinhe/m6A/data_shared/jointLCLs/pi1_m6AQTLs_leadSNPs_noTE"
dir.create(dir_output, showWarnings = F, recursive = T)phenotype_list <- c("m6A", "Expression", "Ribosome", "Protein", "Decay", "APA")
pvalue_joint_m6AQTLs_full.df <- pvalue_joint_m6AQTLs_full.df[,phenotype_list]pvalue_joint_m6AQTLs_info_full.df <- cbind(m6AQTLs_info_full.df[,c("SNP", "PEAK")], pvalue_joint_m6AQTLs_full.df)
pvalue_joint_m6AQTLs_info_leadSNPs.df <- pvalue_joint_m6AQTLs_info_full.df[order(pvalue_joint_m6AQTLs_info_full.df$m6A), ]
pvalue_joint_m6AQTLs_info_leadSNPs.df <- pvalue_joint_m6AQTLs_info_leadSNPs.df[!duplicated(pvalue_joint_m6AQTLs_info_leadSNPs.df$PEAK), ]
pvalue_joint_m6AQTLs_leadSNPs.df <- pvalue_joint_m6AQTLs_info_leadSNPs.df[,phenotype_list]
dim(pvalue_joint_m6AQTLs_leadSNPs.df)pvalue_m6AQTLs_leadSNPs_combined <- combine_pvalue_Fisher(pvalue_joint_m6AQTLs_leadSNPs.df)set.seed(5)
pvalue_joint_m6AQTLs_full.df <- na.omit(pvalue_joint_m6AQTLs_full.df)
pvalue_joint_random.df <- pvalue_joint_m6AQTLs_full.df[sample(1:nrow(pvalue_joint_m6AQTLs_full.df), 1e5),]
pvalue_random_combined <- combine_pvalue_Fisher(pvalue_joint_random.df)set.seed(100)
N_bootstrap <- 1000
pvalue_phenotypes <- pvalue_m6AQTLs_leadSNPs_combined
phenotype_list <- setdiff(colnames(pvalue_phenotypes), "m6A")
print(phenotype_list)
thresh_nlogP_list <- c(3, 4, 5, 6)
pi1_phenotypes.df <- expand.grid(thresh_nlogP = thresh_nlogP_list,
phenotypes = phenotype_list,
ci10 = NA, pi1 = NA, ci90 = NA)
for(i in 1:length(thresh_nlogP_list)){
thresh_nlogP <- thresh_nlogP_list[i]
cat("-log10(m6A pvalue) >", thresh_nlogP, "\n")
idx_select <- which(pvalue_phenotypes$m6A < 10^(-thresh_nlogP))
cat(length(idx_select), "gene-SNP pairs included \n")
for(phenotype in phenotype_list){
cat(phenotype, "...")
pvalues <- pvalue_phenotypes[idx_select, phenotype]
pvalues <- pvalues[!is.na(pvalues)]
pi1_bootstrap <- bootstrap_pi1_ci(pvalues, N = N_bootstrap, ci_percent = 0.8, pi0.method = "bootstrap")
pi1_phenotypes.df[pi1_phenotypes.df$thresh_nlogP == thresh_nlogP & pi1_phenotypes.df$phenotypes == phenotype,
c("ci10", "pi1", "ci90")] <- pi1_bootstrap
}
}
pi1_phenotypes.df$thresh_nlogP <- factor(pi1_phenotypes.df$thresh_nlogP, levels = thresh_nlogP_list)
pi1_phenotypes.df$phenotypes <- factor(pi1_phenotypes.df$phenotypes, levels = phenotype_list)
saveRDS(pi1_phenotypes.df, paste0(dir_output, "/pi1_phenotypes_jointLCLs_lead_m6AQTLs_PCs_combined_bootstrap.rds"))pi1_random.df <- expand.grid(thresh_nlogP = c("random"),
phenotypes = phenotype_list,
ci10 = NA, pi1 = NA, ci90 = NA)
for(phenotype in phenotype_list){
# cat(phenotype, ": ")
pvalues_random <- na.omit(pvalue_random_combined[, phenotype])
pvalues <- sample(pvalues_random, nrow(pvalue_joint_m6AQTLs_leadSNPs.df))
pi1_bootstrap <- bootstrap_pi1_ci(pvalues, N = N_bootstrap, ci_percent = 0.8, pi0.method = "bootstrap")
pi1_random.df[pi1_random.df$thresh_nlogP == "random" & pi1_random.df$phenotypes == phenotype,
c("ci10", "pi1", "ci90")] <- pi1_bootstrap
# cat(length(na.omit(pvalues)), phenotype, "samples without missing values. \t")
}
saveRDS(pi1_random.df, paste0(dir_output, "/pi1_phenotypes_jointLCLs_random_combined_bootstrap.rds"))
pi1_phenotypes_all.df <- rbind(pi1_phenotypes.df, pi1_random.df)
saveRDS(pi1_phenotypes_all.df, paste0(dir_output, "/pi1_phenotypes_jointLCLs_lead_m6AQTLs_PCs_random_combined_bootstrap.rds"))pi1_phenotypes_all.df <- readRDS(paste0(dir_output, "/pi1_phenotypes_jointLCLs_lead_m6AQTLs_PCs_random_combined_bootstrap.rds"))
pi1_phenotypes_all.df$phenotypes <- gsub("E.D.A", "Combined", pi1_phenotypes_all.df$phenotypes)
phenotype_list <- c("Expression", "Ribosome", "Protein", "Decay", "APA", "Combined")
thresh_nlogP_list <- c("random", 3, 4, 5, 6)
pi1_phenotypes.df <- pi1_phenotypes_all.df[pi1_phenotypes_all.df$phenotypes %in% phenotype_list &
pi1_phenotypes_all.df$thresh_nlogP %in% thresh_nlogP_list, ]
pi1_phenotypes.df$phenotypes <- factor(pi1_phenotypes.df$phenotypes, levels = phenotype_list)
pi1_phenotypes.df$thresh_nlogP <- factor(pi1_phenotypes.df$thresh_nlogP, levels = thresh_nlogP_list)
# Use 80% confidence intervals
p <- ggplot(pi1_phenotypes.df,
aes(x=phenotypes, y=pi1, fill=thresh_nlogP)) +
geom_bar(position=position_dodge(), stat="identity") +
geom_errorbar(aes(ymin=ci10, ymax=ci90),
width=.2, # Width of the error bars
position=position_dodge(.9)) +
labs(title = "QTL sharing (ascertained on m6A-QTLs)",
x = "\nAscertainment cutoff",
y = expression(paste("Sharing (", pi["1"], ")")),
fill = expression(paste(-log[10], " p-value"))) +
coord_cartesian(ylim=c(0,0.8)) +
scale_fill_manual(values=cbbPalette) +
theme_minimal() +
theme(legend.position="bottom")
print(p)
sessionInfo()