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F4_repliseq_TPM.R
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F4_repliseq_TPM.R
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#!/usr/bin/Rscript
library(tidyverse)
library(hexbin)
data <- read.table("~/Desktop/Decato-PMD-revision-analysis/repliseq_summary",header=TRUE)
# Too many bins to effectively visualize this relationship.
ggplot(data, aes(x=Methylation_Level, y=Repliseq_Signal)) +
stat_binhex() +
scale_fill_viridis_c() +
facet_wrap(~Sample, ncol=2,scales="free") +
stat_smooth(method=lm) +
theme_bw() +
theme(legend.position = "right", text = element_text(size=14), axis.text = element_text(size = 10),
axis.text.x = element_text(angle = 45, hjust = 1), strip.background = element_blank(),
strip.placement = "outside")
data <- data %>%
mutate(Bin = ifelse(Methylation_Level<0.33, "Low", ifelse(Methylation_Level<0.67, "Med","High"))) %>%
mutate(MethylationPercent = Methylation_Level*100)
data$Bin <- factor(data$Bin, levels=c("High","Med","Low"))
ggplot(data, aes(x=Sample, y=Repliseq_Signal,fill=Bin)) +
geom_boxplot() +
theme_bw() +
theme(legend.position = "right", text = element_text(size=14), axis.text = element_text(size = 10),
axis.text.x = element_text(angle = 45, hjust = 1), strip.background = element_blank(),
strip.placement = "outside")
# R^2=0.2757, p<2e-16, 1% reduction in methylation corresponds to 0.5199 loss in repliseq signal
imr90 <- data %>% filter(Sample == "Lister-ESC-2009_Human_IMR90")
summary(lm(imr90$Repliseq_Signal~imr90$MethylationPercent))
# R^2=0.3314, p<2e-16, 1% reduction in methylation corresponds to 0.3815 loss in repliseq signal
mcf7 <- data %>% filter(Sample == "Menafra-2014_Human_MCF7")
summary(lm(mcf7$Repliseq_Signal~mcf7$MethylationPercent))
# R^2=0.2612, p<2e-16, 1% reduction in methylation corresponds to 0.4263 loss in repliseq signal
gm12878 <- data %>% filter(Sample == "Schlesinger-2013_Human_GM12878")
summary(lm(gm12878$Repliseq_Signal~gm12878$MethylationPercent))
# R^2=0.3018, p<2e-16, 1% reduction in methylation corresponds to 0.4197 loss in repliseq signal
hepg2<- data %>% filter(Sample == "Ziller-2013-Human_HepG2")
summary(lm(hepg2$Repliseq_Signal~hepg2$MethylationPercent))
######
# Escapee status and Repli-seq signal
######
data$Region <- factor(data$Region, levels=c("inPMD","escapeePMD","outPMD"))
ggplot(data, aes(x=Sample, y=Repliseq_Signal, fill=Region)) +
geom_violin(draw_quantiles = c(0.5)) +
theme_bw() +
theme(legend.position = "right", text = element_text(size=14), axis.text = element_text(size = 10),
axis.text.x = element_text(angle = 45, hjust = 1), strip.background = element_blank(),
strip.placement = "outside")
imr90_escapee_inside <- data %>%
filter(Sample == "Lister-ESC-2009_Human_IMR90") %>%
filter(Region == "inPMD" | Region == "escapeePMD")
wilcox.test(imr90_escapee_inside$Repliseq_Signal~imr90_escapee_inside$Region)
# p < 2.2e-16
imr90_escapee_outside <- data %>%
filter(Sample == "Lister-ESC-2009_Human_IMR90") %>%
filter(Region == "outPMD" | Region == "escapeePMD")
wilcox.test(imr90_escapee_outside$Repliseq_Signal~imr90_escapee_outside$Region)
wilcox.test(imr90_escapee_outside$Repliseq_Signal~imr90_escapee_outside$Region)
# p < 2.2e-16
mcf7_escapee_inside <- data %>%
filter(Sample == "Menafra-2014_Human_MCF7") %>%
filter(Region == "inPMD" | Region == "escapeePMD")
wilcox.test(mcf7_escapee_inside$Repliseq_Signal~mcf7_escapee_inside$Region)
# p < 2.2e-16
mcf7_escapee_outside <- data %>%
filter(Sample == "Menafra-2014_Human_MCF7") %>%
filter(Region == "outPMD" | Region == "escapeePMD")
wilcox.test(mcf7_escapee_outside$Repliseq_Signal~mcf7_escapee_outside$Region)
wilcox.test(mcf7_escapee_outside$Repliseq_Signal~mcf7_escapee_outside$Region)
# p < 2.2e-16
#gm12878_escapee_inside <- data %>%
#gm12878_escapee_outside <- data %>%
#hepg2_escapee_inside <- data %>%
#hepg2_escapee_outside <- data %>%
###################################################################################
#### TPM figures
###################################################################################
tpm <- read.table("~/Desktop/Decato-PMD-revision-analysis/TPM_info", header=TRUE)
tpm$Region <- factor(tpm$Region, levels=c("inPMDs","escapees","outPMDs"))
# SA: Healthy small airway, BE: Healthy bronchial epithelium
tpm <- tpm %>%
separate(Sample, sep = "_", into = c("PMD_Source","tpm2","RNA","Barcode","Replicate"), extra = "warn") %>%
mutate(CancerHealthy = ifelse(RNA == "SA" | RNA == "BE", "Healthy","Cancer"))
ggplot(tpm, aes(x=PMD_Source, y=log(TPM),fill=CancerHealthy)) +
geom_boxplot(outlier.shape=NA) +
coord_cartesian(ylim=c(-5.5,8))+
facet_wrap(~Region, ncol=6) +
theme_bw() +
theme(legend.position = "bottom", text = element_text(size=14), axis.text = element_text(size = 10),
axis.text.x = element_text(angle = 45, hjust = 1), strip.background = element_blank(),
strip.placement = "outside")
escapees_outPMDs <- tpm %>%
filter(Region == "escapees" | Region == "outPMDs")
wilcox.test(escapees_outPMDs$TPM~escapees_outPMDs$Region, paired=FALSE) # p<0.1027, n.s.
escapees_inPMDs <- tpm %>%
filter(Region == "escapees" | Region == "inPMDs")
wilcox.test(escapees_inPMDs$TPM~escapees_inPMDs$Region) # p<2.2e-16
tpm %>%
filter(Region == "inPMDs") %>%
group_by(PMD_Source) %>%
do(w = wilcox.test(TPM~CancerHealthy, alternative = "less", data=., paired=FALSE)) %>%
summarise(PMD_Source, Wilcox = w$p.value)
# PMD_Source Wilcox
# 1650Lung 3.21e-88
# 441NSCLC 3.09e-84
# Calu1Lung 2.66e-37
# M3Lung 2.25e-81
tpm %>%
filter(Region == "escapees") %>%
group_by(PMD_Source) %>%
do(w = wilcox.test(TPM~CancerHealthy, alternative = "less", data=., paired=FALSE)) %>%
summarise(PMD_Source, Wilcox = w$p.value)
# PMD_Source Wilcox
# 1650Lung 0.176
# 441NSCLC 0.494
# Calu1Lung 0.0000863
# M3Lung 0.704
tpm %>%
filter(Region == "outPMDs") %>%
group_by(PMD_Source) %>%
do(w = wilcox.test(TPM~CancerHealthy, alternative = "less", data=., paired=FALSE)) %>%
summarise(PMD_Source, Wilcox = w$p.value)
# PMD_Source Wilcox
# 1650Lung 1.04e-70
# 441NSCLC 2.54e-19
# Calu1Lung 6.32e- 9
# M3Lung 7.46e- 1