Last updated: 2020-03-17

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Rmd fca07d8 kevinlkx 2020-03-17 wflow_publish(“analysis/cor_effectsizes_jointLCLs.Rmd”)
  • Compute correlations of QTL effect sizes between m6A and other molecular traits and make scatterplots
  • Ascertain lead m6A-QTLs, use Yang’s genotype file
  • Source: m6AQTL_workflowr/analysis/joint_analysis_effectsizes_scatterplots_cor.Rmd
library(ggplot2)
suppressMessages(library(ggpmisc))

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")

Ascertain lead m6A-QTLs (qvalue < 0.1)

without PCs

dir_m6AQTL_results <- paste0("/project2/xinhe/m6A/m6A_seq/m6A_QTL/results/hg19/m6A_QTLs/", m6A_version)

dir_combined_data <- paste0(dir_m6AQTL_results, "/jointLCLs_analysis/", m6A_phenotype_name, "/m6AQTLs_leadSNPs_noPCs_APA", type_apaQTL, "_qvalue_0.1/")
m6AQTLs_info <- readRDS(paste0(dir_combined_data, "/m6AQTLs_info_leadSNPs.rds"))
effectSize <- readRDS(paste0(dir_combined_data, "/beta_jointLCLs_m6AQTLs_leadSNPs.rds"))
effectSize <- cbind(m6AQTLs_info[,c("gene_snp_pair","genename_snp_pair")], effectSize)
colnames(effectSize) <- c("gene_snp_pair","genename_snp_pair","m6A","4su30m","4su60m","Expression","Expression.Geuvadis","Ribosome","Translation Efficiency","Protein","Decay","APA")

effectSize <- effectSize[,!colnames(effectSize) %in%c("4su30m","4su60m","Expression.Geuvadis")]

effectSize_melt <- reshape2::melt(effectSize[,-c(1:2)], id.vars = "m6A")

ggplot(effectSize_melt,aes(x = m6A, y = value))+geom_point( size = 1)+ylab("QTL effect sizes")+xlab("m6A effect size")+stat_smooth(aes(x = m6A, y = value),method = 'lm')+ facet_wrap(facets = "variable", nrow = 2 ) +
  geom_hline(yintercept = 0, lty= 2,colour="#990000")+
  theme_bw() + 
  theme(panel.grid = element_blank(),axis.line = element_line(colour = "black",size = I(0.5)),axis.title = element_text(family = "arial", face = "bold", size = 15),axis.text = element_text(family = "arial", face = "bold", size = 14,colour = "black"), plot.title = element_text(family = "arial", face = "bold", size = 14, hjust = 0.5), legend.position = "none", strip.text = element_text(face = "bold", size = 14), panel.grid.minor = element_blank() )+
  stat_poly_eq(aes(label = paste(..rr.label..)), label.x.npc = 0.33, label.y.npc = 0.9, formula = y~x, parse = TRUE, size = 4.6,coef.digits = 2) +
  xlim(-5,5) + ylim(-5,5) + ## remove outliers, abs(effect size) > 5
  coord_cartesian(xlim = c(-2.2,2.2), ylim = c(-2.2,2.2)) ## zoom in range: c(-2.2,2.2)

with PCs

dir_combined_data <- paste0(dir_m6AQTL_results, "/jointLCLs_analysis/", m6A_phenotype_name, "/m6AQTLs_leadSNPs_PCs_APA", type_apaQTL, "_qvalue_0.1/")
m6AQTLs_info <- readRDS(paste0(dir_combined_data, "/m6AQTLs_info_leadSNPs.rds"))
effectSize <- readRDS(paste0(dir_combined_data, "/beta_jointLCLs_m6AQTLs_leadSNPs.rds"))
effectSize <- cbind(m6AQTLs_info[,c("gene_snp_pair","genename_snp_pair")], effectSize)
colnames(effectSize) <- c("gene_snp_pair","genename_snp_pair","m6A","4su30m","4su60m","Expression","Expression.Geuvadis","Ribosome","Translation Efficiency","Protein","Decay","APA")

effectSize <- effectSize[,!colnames(effectSize) %in%c("4su30m","4su60m","Expression.Geuvadis")]

effectSize_melt <- reshape2::melt(effectSize[,-c(1:2)], id.vars = "m6A")

ggplot(effectSize_melt,aes(x = m6A, y = value))+geom_point( size = 1)+
  ylab("QTL effect sizes")+xlab("m6A effect size")+
  stat_smooth(aes(x = m6A, y = value),method = 'lm')+ facet_wrap(facets = "variable", nrow = 2 ) +
  geom_hline(yintercept = 0, lty= 2,colour="#990000")+
  theme_bw() + 
  theme(panel.grid = element_blank(),axis.line = element_line(colour = "black",size = I(0.5)),axis.title = element_text(family = "arial", face = "bold", size = 15),axis.text = element_text(family = "arial", face = "bold", size = 14,colour = "black"), plot.title = element_text(family = "arial", face = "bold", size = 14, hjust = 0.5), legend.position = "none", strip.text = element_text(face = "bold", size = 14), panel.grid.minor = element_blank() )+
  stat_poly_eq(aes(label = paste(..rr.label..)), label.x.npc = 0.33, label.y.npc = 0.9, formula = y~x, parse = TRUE, size = 4.6,coef.digits = 2)+
  xlim(-5,5) + ylim(-5,5) + ## remove outliers, abs(effect size) > 5
  coord_cartesian(xlim = c(-2.2,2.2), ylim = c(-2.2,2.2)) ## zoom in range: c(-2.2,2.2)

Ascertain lead m6A-QTLs (qvalue < 0.2)

without PCs

dir_combined_data <- paste0(dir_m6AQTL_results, "/jointLCLs_analysis/", m6A_phenotype_name, "/m6AQTLs_leadSNPs_noPCs_APA", type_apaQTL, "_qvalue_0.2/")
m6AQTLs_info <- readRDS(paste0(dir_combined_data, "/m6AQTLs_info_leadSNPs.rds"))
effectSize <- readRDS(paste0(dir_combined_data, "/beta_jointLCLs_m6AQTLs_leadSNPs.rds"))
effectSize <- cbind(m6AQTLs_info[,c("gene_snp_pair","genename_snp_pair")], effectSize)
colnames(effectSize) <- c("gene_snp_pair","genename_snp_pair","m6A","4su30m","4su60m","Expression","Expression.Geuvadis","Ribosome","Translation Efficiency","Protein","Decay","APA")

effectSize <- effectSize[,!colnames(effectSize) %in%c("4su30m","4su60m","Expression.Geuvadis")]

effectSize_melt <- reshape2::melt(effectSize[,-c(1:2)], id.vars = "m6A")

ggplot(effectSize_melt,aes(x = m6A, y = value))+geom_point( size = 1)+ylab("QTL effect sizes")+xlab("m6A effect size")+stat_smooth(aes(x = m6A, y = value),method = 'lm')+ facet_wrap(facets = "variable", nrow = 2 ) +
  geom_hline(yintercept = 0, lty= 2,colour="#990000")+
  theme_bw() + 
  theme(panel.grid = element_blank(),axis.line = element_line(colour = "black",size = I(0.5)),axis.title = element_text(family = "arial", face = "bold", size = 15),axis.text = element_text(family = "arial", face = "bold", size = 14,colour = "black"), plot.title = element_text(family = "arial", face = "bold", size = 14, hjust = 0.5), legend.position = "none", strip.text = element_text(face = "bold", size = 14), panel.grid.minor = element_blank() )+
  stat_poly_eq(aes(label = paste(..rr.label..)), label.x.npc = 0.33, label.y.npc = 0.9, formula = y~x, parse = TRUE, size = 4.6,coef.digits = 2) +
  xlim(-5,5) + ylim(-5,5) + ## remove outliers, abs(effect size) > 5
  coord_cartesian(xlim = c(-2.2,2.2), ylim = c(-2.2,2.2)) ## zoom in range: c(-2.2,2.2)

with PCs

dir_combined_data <- paste0(dir_m6AQTL_results, "/jointLCLs_analysis/", m6A_phenotype_name, "/m6AQTLs_leadSNPs_PCs_APA", type_apaQTL, "_qvalue_0.2/")
m6AQTLs_info <- readRDS(paste0(dir_combined_data, "/m6AQTLs_info_leadSNPs.rds"))
effectSize <- readRDS(paste0(dir_combined_data, "/beta_jointLCLs_m6AQTLs_leadSNPs.rds"))
effectSize <- cbind(m6AQTLs_info[,c("gene_snp_pair","genename_snp_pair")], effectSize)
colnames(effectSize) <- c("gene_snp_pair","genename_snp_pair","m6A","4su30m","4su60m","Expression","Expression.Geuvadis","Ribosome","Translation Efficiency","Protein","Decay","APA")

effectSize <- effectSize[,!colnames(effectSize) %in%c("4su30m","4su60m","Expression.Geuvadis")]

effectSize_melt <- reshape2::melt(effectSize[,-c(1:2)], id.vars = "m6A")

ggplot(effectSize_melt,aes(x = m6A, y = value))+geom_point( size = 1)+
  ylab("QTL effect sizes")+xlab("m6A effect size")+
  stat_smooth(aes(x = m6A, y = value),method = 'lm')+ facet_wrap(facets = "variable", nrow = 2 ) +
  geom_hline(yintercept = 0, lty= 2,colour="#990000")+
  theme_bw() + 
  theme(panel.grid = element_blank(),axis.line = element_line(colour = "black",size = I(0.5)),axis.title = element_text(family = "arial", face = "bold", size = 15),axis.text = element_text(family = "arial", face = "bold", size = 14,colour = "black"), plot.title = element_text(family = "arial", face = "bold", size = 14, hjust = 0.5), legend.position = "none", strip.text = element_text(face = "bold", size = 14), panel.grid.minor = element_blank() )+
  stat_poly_eq(aes(label = paste(..rr.label..)), label.x.npc = 0.33, label.y.npc = 0.9, formula = y~x, parse = TRUE, size = 4.6,coef.digits = 2) +
  xlim(-5,5) + ylim(-5,5) + ## remove outliers, abs(effect size) > 5
  coord_cartesian(xlim = c(-2.2,2.2), ylim = c(-2.2,2.2)) ## zoom in range: c(-2.2,2.2)

sessionInfo()