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enrichment_analysis.rmd
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---
title: "Enrichment analysis"
author: "Lanyu Zhang, Tiago C. Silva, Lily Wang"
date: "`r Sys.Date()`"
output:
rmarkdown::html_document:
theme: lumen
toc: true
number_sections: true
df_print: paged
code_download: true
toc_float:
collapsed: yes
toc_depth: 3
editor_options:
chunk_output_type: inline
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, warning = FALSE,fig.width = 10,fig.height = 10)
```
# Main libraries and configuration
```{R, message = FALSE, results = 'hide'}
library(dplyr)
library(ExperimentHub)
library(GenomicRanges)
library(coMethDMR)
library(ggplot2)
# devtools::load_all("~/Dropbox (BBSR)/PanCancer/MethReg/")
```
```{R}
dir.result <- "../enrichment_analysis_results"
dir.result.region <- "../meta_analysis_region_based_results"
dir.result.cpg <- "../meta_analysis_single_cpg_results"
data.annot <- "../../coMethDMR_metaAnalysis/DATA_annotations/"
```
# Enrichment analysis
## Generate datasets
### For female
#### For regions
```{R}
### Call in datasets
female_sig <- read.csv(
file.path(
dir.result.region,
"region_based_female_meta_bacon_annot_sig_df.csv"
)
)
female_sig_pos_est <- female_sig %>% dplyr::filter(estimate > 0)
female_sig_neg_est <- female_sig %>% dplyr::filter(estimate < 0)
female_all <- read.csv(
file.path(
dir.result.region,
"region_based_female_meta_bacon_annot_df.csv"
)
)
```
```{R}
probes.cluster.gene <- readRDS(
"../cpgList_3_200_withChrX/Gene_3_200_withChrX.RDS"
)
probes.cluster.intergene <- readRDS(
"../cpgList_3_200_withChrX/InterGene_3_200_withChrX.RDS"
)
probes.cluster.all <- c(
probes.cluster.gene, probes.cluster.intergene
)
# get all cpg in meta_sig_pos input regions
idx <- names(probes.cluster.all) %in% female_sig_pos_est$inputRegion
female_sig_probes_pos_est <- probes.cluster.all[idx] %>%
unlist %>%
as.character() %>%
unique
length(female_sig_probes_pos_est)
# get all cpg in meta_sig_neg input regions
idx <- names(probes.cluster.all) %in% female_sig_neg_est$inputRegion
female_sig_probes_neg_est <- probes.cluster.all[idx] %>%
unlist %>%
as.character() %>%
unique
length(female_sig_probes_neg_est)
# get all cpg in meta_all input regions
idx <- names(probes.cluster.all) %in% female_all$inputRegion
female_all_probes <- probes.cluster.all[idx] %>%
unlist %>%
as.character() %>%
unique
length(female_all_probes)
```
#### For single cpgs
```{R}
female.single.cpg.results <- readr::read_csv(
file.path(dir.result.cpg, "single_cpg_female_meta_bacon_annot_df.csv"),
col_types = readr::cols()
)
female.single.cpg.sig.results <- readr::read_csv( file.path(dir.result.cpg, "single_cpg_female_meta_bacon_annot_sig_df.csv"),
col_types = readr::cols()
)
### Foreground
female.foreground.probes <- female.single.cpg.sig.results %>%
pull(cpg) %>% as.character
female.foreground.probes.neg.est <- female.single.cpg.sig.results %>%
filter(estimate < 0) %>% pull(cpg) %>% as.character
female.foreground.probes.pos.est <- female.single.cpg.sig.results %>%
filter(estimate > 0) %>% pull(cpg) %>% as.character
length(female.foreground.probes)
length(female.foreground.probes.neg.est)
length(female.foreground.probes.pos.est)
### Background
female.background.probes <- female.single.cpg.results %>%
pull(cpg) %>% as.character
length(female.background.probes)
```
### For male
#### For regions
```{R}
### Call in datasets
male_sig <- read.csv(
file.path(
dir.result.region,
"region_based_male_meta_bacon_annot_sig_df.csv"
)
)
male_sig_pos_est <- male_sig %>% dplyr::filter(estimate > 0)
male_sig_neg_est <- male_sig %>% dplyr::filter(estimate < 0)
male_all <- read.csv(
file.path(
dir.result.region,
"region_based_male_meta_bacon_annot_df.csv"
)
)
```
```{R}
probes.cluster.gene <- readRDS(
"../cpgList_3_200_withChrX/Gene_3_200_withChrX.RDS"
)
probes.cluster.intergene <- readRDS(
"../cpgList_3_200_withChrX/InterGene_3_200_withChrX.RDS"
)
probes.cluster.all <- c(
probes.cluster.gene, probes.cluster.intergene
)
# get all cpg in meta_sig_pos input regions
idx <- names(probes.cluster.all) %in% male_sig_pos_est$inputRegion
male_sig_probes_pos_est <- probes.cluster.all[idx] %>%
unlist %>%
as.character() %>%
unique
length(male_sig_probes_pos_est)
# get all cpg in meta_sig_neg input regions
idx <- names(probes.cluster.all) %in% male_sig_neg_est$inputRegion
male_sig_probes_neg_est <- probes.cluster.all[idx] %>%
unlist %>%
as.character() %>%
unique
length(male_sig_probes_neg_est)
# get all cpg in meta_all input regions
idx <- names(probes.cluster.all) %in% male_all$inputRegion
male_all_probes <- probes.cluster.all[idx] %>%
unlist %>%
as.character() %>%
unique
length(male_all_probes)
```
#### For single cpgs
```{R}
male.single.cpg.results <- readr::read_csv(
file.path(dir.result.cpg, "single_cpg_male_meta_bacon_annot_df.csv"),
col_types = readr::cols()
)
male.single.cpg.sig.results <- readr::read_csv( file.path(dir.result.cpg, "single_cpg_male_meta_bacon_annot_sig_df.csv"),
col_types = readr::cols()
)
### Foreground
male.foreground.probes <- male.single.cpg.sig.results %>%
pull(cpg) %>% as.character
male.foreground.probes.neg.est <- male.single.cpg.sig.results %>%
filter(estimate < 0) %>% pull(cpg) %>% as.character
male.foreground.probes.pos.est <- male.single.cpg.sig.results %>%
filter(estimate > 0) %>% pull(cpg) %>% as.character
length(male.foreground.probes)
length(male.foreground.probes.neg.est)
length(male.foreground.probes.pos.est)
### Background
male.background.probes <- male.single.cpg.results %>%
pull(cpg) %>% as.character
length(male.background.probes)
```
## Enrichment plots and tables for single cpgs + cpgs in dmrs
```{R}
devtools::source_gist("https://gist.github.com/Jennyzly2016/f5f39c49c8e2354261bbfa8f661eb088")
```
```{R}
combine.background.probes <- c(female_all_probes, female.background.probes) %>% unique()
combine.female.pos <- c(female_sig_probes_pos_est, female.foreground.probes.pos.est) %>% unique()
combine.female.neg <- c(female_sig_probes_neg_est, female.foreground.probes.neg.est) %>% unique()
combine.male.pos <- c(male_sig_probes_pos_est, male.foreground.probes.pos.est) %>% unique()
combine.male.neg <- c(male_sig_probes_neg_est, male.foreground.probes.neg.est) %>% unique()
```
### Genomic features
#### Generate enrichment plots
```{R}
pos.all.plot <- cpGsGenomicFeatures(
list(
"All probes (n = 464371)" = combine.background.probes,
"Significant w/ Positive estimate in female (n = 451)" = combine.female.pos,
"Significant w/ Positive estimate in male (n = 218)" = combine.male.pos
),
bar.colors = c("#999999", "#D55E00", "#0072B2")
)
pos.all.plot <- pos.all.plot$plot +
ggtitle("Meta-analysis positive probes")
pos.all.plot
ggsave(
plot = pos.all.plot,
width = 10, height = 5,
filename = file.path(
dir.result, "enrichment_meta_analysis_enrichment_positive.pdf"
)
)
neg.all.plot <- cpGsGenomicFeatures(
list(
"All probes (n = 464371)" = combine.background.probes,
"Significant w/ Negative estimate in female (n = 250)" = combine.female.neg,
"Significant w/ Negative estimate in male (n = 41)" = combine.male.neg
),
bar.colors = c("#999999", "#E69F00", "#56B4E9")
)
neg.all.plot <- neg.all.plot$plot +
ggtitle("Meta-analysis negative probes")
neg.all.plot
ggsave(
plot = neg.all.plot,
width = 10, height = 5,
filename = file.path(
dir.result, "enrichment_meta_analysis_enrichment_negative.pdf"
)
)
```
#### Generate enrichment tables
##### For CpG islands
###### females vs. background
```{R, message = FALSE, echo = FALSE}
plot.chrm.hypo <- cpGsEnrichment(
fg.probes = combine.female.neg,
bg.probes = combine.background.probes,
enrichment.type = "island",
fg.label = paste0("Significant probes w/ Negative estimate in female (n = ",
length(combine.female.neg)),
bg.label = paste0("Evaluated probes (n = ",
length(combine.background.probes))
)
# plot.chrm.hypo
plot.chrm.hyper <- cpGsEnrichment(
fg.probes = combine.female.pos,
bg.probes = combine.background.probes,
enrichment.type = "island",
fg.label = paste0("Significant probes w/ Positive estimate in female (n = ",
length(combine.female.pos)),
bg.label = paste0("Evaluated probes (n = ",
length(combine.background.probes))
)
# plot.chrm.hyper
tab.island.female <- cbind(
data.frame(
"Meta-Analysis" = c(
rep("Single CpGs + CpGs in DMRs",nrow(plot.chrm.hyper$table) + nrow(plot.chrm.hypo$table))
),
"Foreground probes" = c(
rep("Significant probes w/ Positive estimate",nrow(plot.chrm.hyper$table)),
rep("Significant probes w/ Negative estimate",nrow(plot.chrm.hypo$table))
),
"Enrichment analysis" = c(
rep("Relation_to_Island",nrow(plot.chrm.hyper$table) + nrow(plot.chrm.hypo$table))
)
),
rbind(plot.chrm.hyper$table,
plot.chrm.hypo$table)
)
```
###### males vs. background
```{R, message = FALSE, echo = FALSE}
plot.chrm.hypo <- cpGsEnrichment(
fg.probes = combine.male.neg,
bg.probes = combine.background.probes,
enrichment.type = "island",
fg.label = paste0("Significant probes w/ Negative estimate in male (n = ",
length(combine.male.neg)),
bg.label = paste0("Evaluated probes (n = ",
length(combine.background.probes))
)
# plot.chrm.hypo
plot.chrm.hyper <- cpGsEnrichment(
fg.probes = combine.male.pos,
bg.probes = combine.background.probes,
enrichment.type = "island",
fg.label = paste0("Significant probes w/ Positive estimate in male (n = ",
length(combine.male.pos)),
bg.label = paste0("Evaluated probes (n = ",
length(combine.background.probes))
)
# plot.chrm.hyper
tab.island.male <- cbind(
data.frame(
"Meta-Analysis" = c(
rep("Single CpGs + CpGs in DMRs",nrow(plot.chrm.hyper$table) + nrow(plot.chrm.hypo$table))
),
"Foreground probes" = c(
rep("Significant probes w/ Positive estimate",nrow(plot.chrm.hyper$table)),
rep("Significant probes w/ Negative estimate",nrow(plot.chrm.hypo$table))
),
"Enrichment analysis" = c(
rep("Relation_to_Island",nrow(plot.chrm.hyper$table) + nrow(plot.chrm.hypo$table))
)
),
rbind(plot.chrm.hyper$table,
plot.chrm.hypo$table)
)
```
###### females vs. males
```{R, message = FALSE, echo = FALSE}
plot.chrm.hypo <- cpGsEnrichment_FvsM(
fg.probes = combine.female.neg,
bg.probes = combine.male.neg,
enrichment.type = "island",
fg.label = paste0("Significant probes w/ Negative estimate in female (n = ",
length(combine.female.neg)),
bg.label = paste0("Significant probes w/ Negative estimate in male (n = ",
length(combine.male.neg))
)
# plot.chrm.hypo
plot.chrm.hyper <- cpGsEnrichment_FvsM(
fg.probes = combine.female.pos,
bg.probes = combine.male.pos,
enrichment.type = "island",
fg.label = paste0("Significant probes w/ Positive estimate in female (n = ",
length(combine.female.pos)),
bg.label = paste0("Significant probes w/ Positive estimate in male (n = ",
length(combine.male.pos))
)
# plot.chrm.hyper
tab.island.FvsM <- cbind(
data.frame(
"Meta-Analysis" = c(
rep("Single CpGs + CpGs in DMRs",nrow(plot.chrm.hyper$table) + nrow(plot.chrm.hypo$table))
),
"Foreground probes" = c(
rep("Significant probes w/ Positive estimate",nrow(plot.chrm.hyper$table)),
rep("Significant probes w/ Negative estimate",nrow(plot.chrm.hypo$table))
),
"Enrichment analysis" = c(
rep("Relation_to_Island",nrow(plot.chrm.hyper$table) + nrow(plot.chrm.hypo$table))
)
),
rbind(plot.chrm.hyper$table,
plot.chrm.hypo$table)
)
```
##### For Gene regions
###### females vs. background
```{R, message = FALSE, echo = FALSE}
plot.chrm.hypo <- cpGsEnrichment(
fg.probes = combine.female.neg,
bg.probes = combine.background.probes,
enrichment.type = "gene",
fg.label = paste0("Significant probes w/ Negative estimate in female (n = ",
length(combine.female.neg)),
bg.label = paste0("Evaluated probes (n = ",
length(combine.background.probes))
)
# plot.chrm.hypo
plot.chrm.hyper <- cpGsEnrichment(
fg.probes = combine.female.pos,
bg.probes = combine.background.probes,
enrichment.type = "gene",
fg.label = paste0("Significant probes w/ Positive estimate in female (n = ",
length(combine.female.pos)),
bg.label = paste0("Evaluated probes (n = ",
length(combine.background.probes))
)
# plot.chrm.hyper
tab.gene.female <- cbind(
data.frame(
"Meta-Analysis" = c(
rep("Single CpGs + CpGs in DMRs",nrow(plot.chrm.hyper$table) + nrow(plot.chrm.hypo$table))
),
"Foreground probes" = c(
rep("Significant probes w/ Positive estimate",nrow(plot.chrm.hyper$table)),
rep("Significant probes w/ Negative estimate",nrow(plot.chrm.hypo$table))
),
"Enrichment analysis" = c(
rep("UCSC_RefGene_Group_hierarchy",nrow(plot.chrm.hyper$table) + nrow(plot.chrm.hypo$table))
)
),
rbind(plot.chrm.hyper$table,
plot.chrm.hypo$table)
)
```
###### males vs. background
```{R, message = FALSE, echo = FALSE}
plot.chrm.hypo <- cpGsEnrichment(
fg.probes = combine.male.neg,
bg.probes = combine.background.probes,
enrichment.type = "gene",
fg.label = paste0("Significant probes w/ Negative estimate in male (n = ",
length(combine.male.neg)),
bg.label = paste0("Evaluated probes (n = ",
length(combine.background.probes))
)
# plot.chrm.hypo
plot.chrm.hyper <- cpGsEnrichment(
fg.probes = combine.male.pos,
bg.probes = combine.background.probes,
enrichment.type = "gene",
fg.label = paste0("Significant probes w/ Positive estimate in male (n = ",
length(combine.male.pos)),
bg.label = paste0("Evaluated probes (n = ",
length(combine.background.probes))
)
# plot.chrm.hyper
tab.gene.male <- cbind(
data.frame(
"Meta-Analysis" = c(
rep("Single CpGs + CpGs in DMRs",nrow(plot.chrm.hyper$table) + nrow(plot.chrm.hypo$table))
),
"Foreground probes" = c(
rep("Significant probes w/ Positive estimate",nrow(plot.chrm.hyper$table)),
rep("Significant probes w/ Negative estimate",nrow(plot.chrm.hypo$table))
),
"Enrichment analysis" = c(
rep("UCSC_RefGene_Group_hierarchy",nrow(plot.chrm.hyper$table) + nrow(plot.chrm.hypo$table))
)
),
rbind(plot.chrm.hyper$table,
plot.chrm.hypo$table)
)
```
###### females vs. males
```{R, message = FALSE, echo = FALSE}
plot.chrm.hypo <- cpGsEnrichment_FvsM(
fg.probes = combine.female.neg,
bg.probes = combine.male.neg,
enrichment.type = "gene",
fg.label = paste0("Significant probes w/ Negative estimate in female (n = ",
length(combine.female.neg)),
bg.label = paste0("Significant probes w/ Negative estimate in male (n = ",
length(combine.male.neg))
)
# plot.chrm.hypo
plot.chrm.hyper <- cpGsEnrichment_FvsM(
fg.probes = combine.female.pos,
bg.probes = combine.male.pos,
enrichment.type = "gene",
fg.label = paste0("Significant probes w/ Positive estimate in female (n = ",
length(combine.female.pos)),
bg.label = paste0("Significant probes w/ Positive estimate in male (n = ",
length(combine.male.pos))
)
# plot.chrm.hyper
tab.gene.FvsM <- cbind(
data.frame(
"Meta-Analysis" = c(
rep("Single CpGs + CpGs in DMRs",nrow(plot.chrm.hyper$table) + nrow(plot.chrm.hypo$table))
),
"Foreground probes" = c(
rep("Significant probes w/ Positive estimate",nrow(plot.chrm.hyper$table)),
rep("Significant probes w/ Negative estimate",nrow(plot.chrm.hypo$table))
),
"Enrichment analysis" = c(
rep("UCSC_RefGene_Group_hierarchy",nrow(plot.chrm.hyper$table) + nrow(plot.chrm.hypo$table))
)
),
rbind(plot.chrm.hyper$table,
plot.chrm.hypo$table)
)
```
### Chromotin state
#### Generate enrichment plots
```{R}
file <- paste0("https://egg2.wustl.edu/roadmap/data/byFileType/chromhmmSegmentations",
"/ChmmModels/coreMarks/jointModel/final/E073_15_coreMarks_segments.bed")
ChmmModels <- readr::read_tsv(
file,
col_names = FALSE,
col_types = readr::cols()
)
colnames(ChmmModels) <- c("chr","start","end","state")
states <- readr::read_csv(
"../../coMethDMR_metaAnalysis/DNAm_RNA/data/chromHMM_labels.csv",
col_names = FALSE,
col_types = readr::cols()
)
states$X1 <- paste0("E",states$X1)
ChmmModels$state <- states$X3[match(ChmmModels$state,states$X1)]
ChmmModels.gr <- makeGRangesFromDataFrame(ChmmModels,keep.extra.columns = TRUE)
```
```{R}
pos.single.cpg.plot <- cpGsGenomicFeatures(
list(
"All probes (n = 464371)" = combine.background.probes,
"Significant w/ Positive estimate in female (n = 451)" = combine.female.pos,
"Significant w/ Positive estimate in male (n = 218)" = combine.male.pos
),
bar.colors = c("#999999", "#D55E00", "#0072B2"),
annotation.gr = ChmmModels.gr,
plot.width = 10,
plot.height = 5,
plot.title = "Meta-analysis positive probes\nChroMHMM: E073 - 15 coreMarks segments",
enrichment.type = "customized",
plot.filename = file.path(
dir.result, "enrichment_meta_analysis_chromHMM_states_positive.pdf"
)
)
pos.single.cpg.plot$plot
neg.single.cpg.plot <- cpGsGenomicFeatures(
list(
"All probes (n = 464371)" = combine.background.probes,
"Significant w/ Negative estimate in female (n = 250)" = combine.female.neg,
"Significant w/ Negative estimate in male (n = 41)" = combine.male.neg
),
bar.colors = c("#999999", "#E69F00", "#56B4E9"),
annotation.gr = ChmmModels.gr,
plot.width = 10,
plot.height = 5,
plot.title = "Meta-analysis negative probes\nChroMHMM: E073 - 15 coreMarks segments",
enrichment.type = "customized",
plot.filename = file.path(
dir.result, "enrichment_meta_analysis_chromHMM_states_negative.pdf"
)
)
neg.single.cpg.plot$plot
```
#### Generate enrichment tables
##### females vs. background
```{R}
plot.chrm.hypo <- cpGsEnrichment(
fg.probes = combine.female.neg,
bg.probes = combine.background.probes,
annotation.gr = ChmmModels.gr,
fg.label = paste0("Significant probes w/ Negative estimate in female (n = ",
length(combine.female.neg),")"),
bg.label = paste0("Evaluated probes (n = ",
length(combine.background.probes),")"),
enrichment.type = "customized",
save.plot = FALSE
)
#plot.chrm.hypo$plot
plot.chrm.hyper <- cpGsEnrichment(
fg.probes = combine.female.pos,
bg.probes = combine.background.probes,
annotation.gr = ChmmModels.gr,
fg.label = paste0("Significant probes w/ Positive estimate in female (n = ",
length(combine.female.pos),")"),
bg.label = paste0("Evaluated probes (n = ",
length(combine.background.probes),")"),
enrichment.type = "customized",
save.plot = FALSE
)
#plot.chrm.hyper$plot
tab.chrm.female <- cbind(
data.frame(
"Meta-Analysis" = c(
rep("Single CpGs + CpGs in DMRs",nrow(plot.chrm.hyper$table) + nrow(plot.chrm.hypo$table))
),
"Foreground probes" = c(
rep("Significant probes w/ Positive estimate",nrow(plot.chrm.hyper$table)),
rep("Significant probes w/ Negative estimate",nrow(plot.chrm.hypo$table))
),
"Enrichment analysis" = c(
rep("ChroMHMM: E073",nrow(plot.chrm.hyper$table) + nrow(plot.chrm.hypo$table))
)
),
rbind(plot.chrm.hyper$table,
plot.chrm.hypo$table)
)
```
##### males vs. background
```{R}
plot.chrm.hypo <- cpGsEnrichment(
fg.probes = combine.male.neg,
bg.probes = combine.background.probes,
annotation.gr = ChmmModels.gr,
fg.label = paste0("Significant probes w/ Negative estimate in male (n = ",
length(combine.male.neg),")"),
bg.label = paste0("Evaluated probes (n = ",
length(combine.background.probes),")"),
enrichment.type = "customized",
save.plot = FALSE
)
#plot.chrm.hypo$plot
plot.chrm.hyper <- cpGsEnrichment(
fg.probes = combine.male.pos,
bg.probes = combine.background.probes,
annotation.gr = ChmmModels.gr,
fg.label = paste0("Significant probes w/ Positive estimate in male (n = ",
length(combine.male.pos),")"),
bg.label = paste0("Evaluated probes (n = ",
length(combine.background.probes),")"),
enrichment.type = "customized",
save.plot = FALSE
)
#plot.chrm.hyper$plot
tab.chrm.male <- cbind(
data.frame(
"Meta-Analysis" = c(
rep("Single CpGs + CpGs in DMRs",nrow(plot.chrm.hyper$table) + nrow(plot.chrm.hypo$table))
),
"Foreground probes" = c(
rep("Significant probes w/ Positive estimate",nrow(plot.chrm.hyper$table)),
rep("Significant probes w/ Negative estimate",nrow(plot.chrm.hypo$table))
),
"Enrichment analysis" = c(
rep("ChroMHMM: E073",nrow(plot.chrm.hyper$table) + nrow(plot.chrm.hypo$table))
)
),
rbind(plot.chrm.hyper$table,
plot.chrm.hypo$table)
)
```
##### females vs. males
```{R}
plot.chrm.hypo <- cpGsEnrichment_FvsM(
fg.probes = combine.female.neg,
bg.probes = combine.male.neg,
annotation.gr = ChmmModels.gr,
fg.label = paste0("Significant probes w/ Negative estimate in female (n = ",
length(combine.female.neg),")"),
bg.label = paste0("Significant probes w/ Negative estimate in male (n = ",
length(combine.male.neg),")"),
enrichment.type = "customized",
save.plot = FALSE
)
#plot.chrm.hypo$plot
plot.chrm.hyper <- cpGsEnrichment_FvsM(
fg.probes = combine.female.pos,
bg.probes = combine.male.pos,
annotation.gr = ChmmModels.gr,
fg.label = paste0("Significant probes w/ Positive estimate in female (n = ",
length(combine.female.pos),")"),
bg.label = paste0("Significant probes w/ Positive estimate in male (n = ",
length(combine.male.pos),")"),
enrichment.type = "customized",
save.plot = FALSE
)
#plot.chrm.hyper$plot
tab.chrm.FvsM <- cbind(
data.frame(
"Meta-Analysis" = c(
rep("Single CpGs + CpGs in DMRs",nrow(plot.chrm.hyper$table) + nrow(plot.chrm.hypo$table))
),
"Foreground probes" = c(
rep("Significant probes w/ Positive estimate",nrow(plot.chrm.hyper$table)),
rep("Significant probes w/ Negative estimate",nrow(plot.chrm.hypo$table))
),
"Enrichment analysis" = c(
rep("ChroMHMM: E073",nrow(plot.chrm.hyper$table) + nrow(plot.chrm.hypo$table))
)
),
rbind(plot.chrm.hyper$table,
plot.chrm.hypo$table)
)
```
### Combine and create final enrichment tables
```{R include = FALSE}
colnames(tab.gene.female)[4] <- colnames(tab.chrm.female)[4] <- colnames(tab.island.female)[4] <- "Variable"
tab.female <- tab.gene.female %>% rbind(tab.island.female) %>% rbind(tab.chrm.female)
colnames(tab.female)[5:ncol(tab.female)] <- paste0(
"female_",colnames(tab.female)[5:ncol(tab.female)])
write.csv(
tab.female,
file.path(dir.result, "enrichment_table_female.csv"),
row.names = FALSE
)
```
```{R include = FALSE}
colnames(tab.gene.male)[4] <- colnames(tab.chrm.male)[4] <- colnames(tab.island.male)[4] <- "Variable"
tab.male <- tab.gene.male %>% rbind(tab.island.male) %>% rbind(tab.chrm.male)
colnames(tab.male)[5:ncol(tab.male)] <- paste0(
"male_",colnames(tab.male)[5:ncol(tab.male)])
write.csv(
tab.male,
file.path(dir.result, "enrichment_table_male.csv"),
row.names = FALSE
)
```
```{R include = FALSE}
colnames(tab.gene.FvsM)[4] <- colnames(tab.chrm.FvsM)[4] <- colnames(tab.island.FvsM)[4] <- "Variable"
tab.FvsM <- tab.gene.FvsM %>% rbind(tab.island.FvsM) %>% rbind(tab.chrm.FvsM)
colnames(tab.FvsM)[5:ncol(tab.FvsM)] <- paste0(
"FvsM_",colnames(tab.FvsM)[5:ncol(tab.FvsM)])
write.csv(
tab.FvsM,
file.path(dir.result, "enrichment_table_FvsM.csv"),
row.names = FALSE
)
```
# Prepare datasets for pathway analysis with GSEA
```{R, message = FALSE, results = 'hide'}
library(gam)
```
## For female
### Prepare dataset for single cpg analysis
```{R}
cpg_all <- readr::read_csv(
file.path(
dir.result.cpg,
"single_cpg_female_meta_bacon_annot_great_state_df.csv"
),
col_types = readr::cols()
) %>%
filter(!is.na(pVal.final)) %>%
dplyr::mutate(`-log10pVal.final` = -log10(pVal.final)) %>%
select(cpg, GREAT_annotation, `-log10pVal.final`, pVal.final)
```
### Prepare dataset for region based analysis
```{R}
region_all <- read.csv(
file.path(
dir.result.region,
"region_based_female_meta_bacon_annot_df.csv"
)
) %>%
filter(!is.na(pVal.final)) %>%
dplyr::mutate(`-log10pVal.final` = -log10(pVal.final)) %>%
select(inputRegion, `-log10pVal.final`, pVal.final)
```
#### Extract cpgs from each inputRegion
```{R}
probes.cluster.gene <- readRDS(
"../cpgList_3_200_withChrX/Gene_3_200_withChrX.RDS"
)
probes.cluster.intergene <- readRDS(
"../cpgList_3_200_withChrX/InterGene_3_200_withChrX.RDS"
)
probes.cluster.all <- c(
probes.cluster.gene, probes.cluster.intergene
)
idx <- names(probes.cluster.all) %in% region_all$inputRegion
region_all$cpg <- plyr::laply(probes.cluster.all[idx], function(term){
paste(term, collapse = ";")
})
region_all <- region_all %>%
select(cpg, pVal.final, `-log10pVal.final`) %>%
tidyr::separate_rows(cpg, sep = ";")
```
#### Annotate cpgs
```{R, message = FALSE, warning = FALSE, results = 'hide'}
library(sesame)
library(rGREAT)
library(dplyr)
```
```{R}
probes.info <- sesameDataGet("HM450.hg19.manifest") %>% as.data.frame()
probes.info <- probes.info[, c("seqnames", "start", "end")]
colnames(probes.info)[1] <- "chr"
probes.info$chr <- as.character(probes.info$chr)
### add chrom and position
region_all_annot <- merge(
probes.info, region_all, by.x = "row.names", by.y = "cpg", sort = FALSE
)
colnames(region_all_annot)[1] <- "cpg"
region_all_annot$cpg <- as.character(region_all_annot$cpg)
```
```{R}
job_regions <- submitGreatJob(
gr = region_all_annot[, c("chr", "start", "end")],
species = "hg19" # or "hg38".
)
regionsToGenes <- data.frame(plotRegionGeneAssociationGraphs(job_regions))
regionsToGenes$GREAT_annotation <- ifelse(
regionsToGenes$distTSS > 0,
paste0(regionsToGenes$gene, "(+", regionsToGenes$distTSS, ")"),
paste0(regionsToGenes$gene, "(", regionsToGenes$distTSS, ")")
)
regionsToGenes <- regionsToGenes[
,c("seqnames", "start", "end", "GREAT_annotation")
]
great <- group_by(regionsToGenes, seqnames, start, end) %>%
summarise_each(funs(paste(., collapse = "; ")))
great_df <- as.data.frame(great)
colnames(great_df)[1] <- "chr"
region_all_annot <- merge(great_df, region_all_annot)
region_all_annot <- region_all_annot %>%
select(GREAT_annotation, cpg, `-log10pVal.final`, pVal.final)
```
### Merge datasets
```{R}
all_genes <- rbind(cpg_all, region_all_annot) %>%
tidyr::separate_rows(GREAT_annotation, sep = "; ") %>%
tidyr::separate(GREAT_annotation, into = "Gene", sep = "\\(", remove = TRUE) %>%
filter(!is.na(Gene)) %>%
group_by(Gene) %>%
mutate(`-log10pVal.final` = max(`-log10pVal.final`)) %>%
mutate(pVal.final = min(pVal.final)) %>%
select(-cpg) %>%
unique %>%
as.data.frame()
all_genes_nProbes <- rbind(cpg_all, region_all_annot) %>%
tidyr::separate_rows(GREAT_annotation, sep = "; ") %>%
tidyr::separate(GREAT_annotation, into = "Gene", sep = "\\(", remove = TRUE) %>%
filter(!is.na(Gene)) %>%
select(Gene, cpg) %>%
unique() %>%
group_by(Gene) %>%
summarise(num.probes = n()) %>%
as.data.frame()
all_genes_final <- merge(all_genes, all_genes_nProbes)
colnames(all_genes_final)[2] <- "neglog10pVal.final"
```
### Calculate residuals
```{R}
cor.test(
all_genes_final$pVal.final,
all_genes_final$num.probes,
method = "spearman"
)
all_genes_final$resid_neglog10pval <- resid(
gam(
neglog10pVal.final ~ s(num.probes),
family = Gamma(link=identity),
data = all_genes_final
)
)
hist(all_genes_final$resid_neglog10pval)
cor.test(
all_genes_final$resid_neglog10pval,
all_genes_final$num.probes,
method = "spearman"
)
write.csv(
all_genes_final %>% select(Gene, resid_neglog10pval),
"females_neglog10Pvals.csv",
row.names = FALSE
)
```
## For male
### Prepare dataset for single cpg analysis
```{R}
cpg_all <- readr::read_csv(
file.path(
dir.result.cpg,
"single_cpg_male_meta_bacon_annot_great_state_df.csv"
),
col_types = readr::cols()
) %>%
filter(!is.na(pVal.final)) %>%
dplyr::mutate(`-log10pVal.final` = -log10(pVal.final)) %>%
select(cpg, GREAT_annotation, `-log10pVal.final`, pVal.final)
```
### Prepare dataset for region based analysis
```{R}
region_all <- read.csv(
file.path(
dir.result.region,
"region_based_male_meta_bacon_annot_df.csv"
)
) %>%
filter(!is.na(pVal.final)) %>%
dplyr::mutate(`-log10pVal.final` = -log10(pVal.final)) %>%
select(inputRegion, `-log10pVal.final`, pVal.final)
```
#### Extract cpgs from each inputRegion
```{R}
probes.cluster.gene <- readRDS(
"../cpgList_3_200_withChrX/Gene_3_200_withChrX.RDS"
)
probes.cluster.intergene <- readRDS(