In rmflight/fmcorrelationbreastcaparp1: Functions for calculating the correlations in PARP1 nucleosome data in breast cancer cell lines

Correlation of PARP1 with CTCF ChIP-Seq

We will analyze the correlation of nucleosome associated PARP1 reads with CTCF ChIP-seq. The CTCF ChIP-seq is from the UCSC/ENCODE data portal. The PARP1 reads and CTCF ChIP-seq data has already been processed and is made available as part of the fmdatabreastcaparp1 package.

knitr::opts_chunk$set(dev='png')

library(GenomicRanges)
library(ggplot2)
library(BSgenome.Hsapiens.UCSC.hg19)
library(fmcorrelationbreastcaparp1)
library(fmdatabreastcaparp1)

Load the PARP1 data.

data(tss_windows)
data(parp1_ln4_unique)
data(parp1_ln5_unique)

Calculate the weighted coverage of the ln4 and ln5 sample reads, and then sum the reads in each TSS window.

mcf7_cov <- coverage(parp1_ln4_unique, weight = "n_count")
mdamb231_cov <- coverage(parp1_ln5_unique, weight = "n_count")

tss_windows <- binned_function(tss_windows, mcf7_cov, "sum", "parp1_mcf7")
tss_windows <- binned_function(tss_windows, mdamb231_cov, "sum", "parp1_mdamb231")

Load the CTCT ChIP-seq data, and average the ChIP-seq peaks in each TSS window.

data(ctcf_rep1)
data(ctcf_rep2)
ctcf_r1_cov <- coverage(ctcf_rep1, weight = "mcols.signal")
ctcf_r2_cov <- coverage(ctcf_rep2, weight = "mcols.signal")
tss_windows <- binned_function(tss_windows, ctcf_r1_cov, "mean_nozero", "ctcf_r1")
tss_windows <- binned_function(tss_windows, ctcf_r2_cov, "mean_nozero", "ctcf_r2")

Now with the Parp1 reads and CTCF ChIP-Seq data, we can start doing some correlations.

non_zero <- "both"

Start with a sampling of points and graph and generate a correlation.

r1_v_mcf7 <- subsample_nonzeros(mcols(tss_windows), c("ctcf_r1", "parp1_mcf7"), non_zero = non_zero, n_points = 10000)
ggplot(r1_v_mcf7, aes(x = ctcf_r1, y = parp1_mcf7)) + geom_point() + scale_y_log10() + scale_x_log10()
cor(log10(r1_v_mcf7[,1]+1), log10(r1_v_mcf7[,2]+1))

r2_v_mcf7 <- subsample_nonzeros(mcols(tss_windows), c("ctcf_r2", "parp1_mcf7"), non_zero = non_zero, n_points = 10000)
ggplot(r2_v_mcf7, aes(x = ctcf_r2, y = parp1_mcf7)) + geom_point() + scale_y_log10() + scale_x_log10()
cor(log10(r2_v_mcf7[,1]+1), log10(r2_v_mcf7[,2]+1))

Now do them all.

all_comb <- expand.grid(c("ctcf_r1", "ctcf_r2"), c("parp1_mcf7", "parp1_mdamb231"), stringsAsFactors = FALSE)
out_cor <- lapply(seq(1, nrow(all_comb)), function(i_row){
  #print(i_row)
  correlate_non_zero(mcols(tss_windows), as.character(all_comb[i_row,]), log_transform = TRUE, non_zero = non_zero, test = TRUE)
})
all_comb_names <- paste(all_comb[,1], all_comb[,2], sep = "_v_")
out_cor <- do.call(rbind, out_cor)
rownames(out_cor) <- all_comb_names

TSS correlations:

knitr::kable(out_cor)

out_graphs <- lapply(seq(1, nrow(all_comb)), function(i_row){
  use_vars <- as.character(all_comb[i_row,])
  subpoints <- subsample_nonzeros(mcols(tss_windows), use_vars, non_zero = non_zero, n_points = 10000)
  ggplot(subpoints, aes_string(x = use_vars[1], y = use_vars[2])) + geom_point() + scale_y_log10() + scale_x_log10()
})
out_graphs

Genome Wide Comparison

Are these correlations a result of association with the TSS's? One way to test this is to set up a calculation genome-wide.

genome_tiles <- tileGenome(seqinfo(Hsapiens), tilewidth = 2000, cut.last.tile.in.chrom = TRUE)

genome_tiles <- binned_function(genome_tiles, mcf7_cov, "sum", "parp1_mcf7")
genome_tiles <- binned_function(genome_tiles, mdamb231_cov, "sum", "parp1_mdamb231")
genome_tiles <- binned_function(genome_tiles, ctcf_r1_cov, "mean_nozero", "ctcf_r1")
genome_tiles <- binned_function(genome_tiles, ctcf_r2_cov, "mean_nozero", "ctcf_r2")

genome_r1_v_mcf7 <- subsample_nonzeros(mcols(genome_tiles), c("ctcf_r1", "parp1_mcf7"), non_zero = non_zero, n_points = 10000)
ggplot(genome_r1_v_mcf7, aes(x = ctcf_r1, y = parp1_mcf7)) + scale_x_log10() + scale_y_log10() + geom_point()
cor(log(genome_r1_v_mcf7[,1]+1), log(genome_r1_v_mcf7[,2]+1))

genome_cor <- lapply(seq(1, nrow(all_comb)), function(i_row){
  #print(i_row)
  correlate_non_zero(mcols(genome_tiles), as.character(all_comb[i_row,]), log_transform = TRUE, non_zero = non_zero, test = TRUE)
})
all_comb_names <- paste(all_comb[,1], all_comb[,2], sep = "_v_")
genome_cor <- do.call(rbind, genome_cor)
rownames(genome_cor) <- all_comb_names

Genome wide correlations:

knitr::kable(genome_cor)

Save the correlation results in some plain text files.

saveloc <- "../inst/correlation_tables"
write.table(out_cor, file = file.path(saveloc, "ctcf_tss.txt"), sep = "\t")
write.table(genome_cor, file = file.path(saveloc, "ctcf_genome.txt"), sep = "\t")

Session Info

Sys.time()
sessionInfo()

rmflight/fmcorrelationbreastcaparp1 documentation built on May 27, 2019, 9:31 a.m.