In this analysis we are going to calculate correlations of the PARP1 reads with a variety of histone mark ChIP-seq data from UCSC/ENCODE. The PARP1 reads and histone mark data has already been processed and is made available as part of the fmdatabreastcaparp1 package.
library(GenomicRanges)
library(ggplot2)
library(fmcorrelationbreastcaparp1)
library(fmdatabreastcaparp1)
library(BSgenome.Hsapiens.UCSC.hg19)
data(parp1_ln4_unique)
data(parp1_ln5_unique)
data(histone_marks)
data(tss_windows)
Calculate the weighted coverage of the PARP1 mcf7 and mdamb231 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")
Get the averaged ChIP-seq peak intensity in each tss window.
for (i_name in names(histone_marks)){
histone_cov <- coverage(histone_marks[[i_name]], weight = "mcols.signal")
tss_windows <- binned_function(tss_windows, histone_cov, "mean_nozero", i_name)
}
Now with the Parp1 reads and histone mark signal added to the TSS's, we can start doing some correlations.
non_zero <- "both"
h3k4me3k_v_mcf7 <- subsample_nonzeros(mcols(tss_windows), c("H3k4me3_r1", "parp1_mcf7"), non_zero = non_zero, n_points = 10000)
ggplot(h3k4me3k_v_mcf7, aes(x = H3k4me3_r1, y = parp1_mcf7)) + geom_point() + scale_y_log10() + scale_x_log10()
cor(log10(h3k4me3k_v_mcf7[,1]+1), log10(h3k4me3k_v_mcf7[,2]+1))
## [1] 0.3960252
h3k27ac_v_mcf7 <- subsample_nonzeros(mcols(tss_windows), c("H3k27ac", "parp1_mcf7"), non_zero = non_zero, n_points = 10000)
ggplot(h3k27ac_v_mcf7, aes(x = H3k27ac, y = parp1_mcf7)) + geom_point() + scale_y_log10() + scale_x_log10()
cor(log10(h3k27ac_v_mcf7[,1]+1), log10(h3k27ac_v_mcf7[,2]+1))
## [1] -0.2865006
Cool. Now we are showing some promise. Let's do them all.
all_comb <- expand.grid(names(histone_marks), 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:
corr_value | p_value | |
---|---|---|
H3k09me3_v_parp1_mcf7 | -0.2667278 | 0 |
H3k27ac_v_parp1_mcf7 | -0.3056719 | 0 |
H3k27me3_v_parp1_mcf7 | -0.3466788 | 0 |
H3k36me3_v_parp1_mcf7 | -0.3065474 | 0 |
H3k4me3_r1_v_parp1_mcf7 | 0.3869864 | 0 |
H3k4me3_r2_v_parp1_mcf7 | 0.3607083 | 0 |
H3k09me3_v_parp1_mdamb231 | -0.1674508 | 0 |
H3k27ac_v_parp1_mdamb231 | -0.1899501 | 0 |
H3k27me3_v_parp1_mdamb231 | -0.1133071 | 0 |
H3k36me3_v_parp1_mdamb231 | -0.2365975 | 0 |
H3k4me3_r1_v_parp1_mdamb231 | 0.3623120 | 0 |
H3k4me3_r2_v_parp1_mdamb231 | 0.3497517 | 0 |
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
## [[1]]
##
## [[2]]
##
## [[3]]
##
## [[4]]
##
## [[5]]
##
## [[6]]
##
## [[7]]
##
## [[8]]
##
## [[9]]
##
## [[10]]
##
## [[11]]
##
## [[12]]
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")
for (i_name in names(histone_marks)){
histone_cov <- coverage(histone_marks[[i_name]], weight = "mcols.signal")
genome_tiles <- binned_function(genome_tiles, histone_cov, "mean_nozero", i_name)
}
genome_h3k4me3k_v_mcf7 <- subsample_nonzeros(mcols(genome_tiles), c("H3k4me3_r1", "parp1_mcf7"), non_zero = non_zero, n_points = 10000)
ggplot(genome_h3k4me3k_v_mcf7, aes(x = H3k4me3_r1, y = parp1_mcf7)) + scale_x_log10() + scale_y_log10() + geom_point()
cor(log(genome_h3k4me3k_v_mcf7[,1]+1), log(genome_h3k4me3k_v_mcf7[,2]+1))
## [1] 0.1492715
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:
corr_value | p_value | |
---|---|---|
H3k09me3_v_parp1_mcf7 | -0.4098493 | 0 |
H3k27ac_v_parp1_mcf7 | -0.4011359 | 0 |
H3k27me3_v_parp1_mcf7 | -0.4200107 | 0 |
H3k36me3_v_parp1_mcf7 | -0.3882289 | 0 |
H3k4me3_r1_v_parp1_mcf7 | 0.1566817 | 0 |
H3k4me3_r2_v_parp1_mcf7 | 0.1180702 | 0 |
H3k09me3_v_parp1_mdamb231 | -0.2499175 | 0 |
H3k27ac_v_parp1_mdamb231 | -0.2573392 | 0 |
H3k27me3_v_parp1_mdamb231 | -0.1890674 | 0 |
H3k36me3_v_parp1_mdamb231 | -0.2859072 | 0 |
H3k4me3_r1_v_parp1_mdamb231 | 0.2524751 | 0 |
H3k4me3_r2_v_parp1_mdamb231 | 0.2344856 | 0 |
We will save the correlation results in some plain text files.
saveloc <- "../inst/correlation_tables"
write.table(out_cor, file = file.path(saveloc, "histone_marks_tss.txt"), sep = "\t")
write.table(genome_cor, file = file.path(saveloc, "histone_marks_genome.txt"), sep = "\t")
## [1] "2014-12-11 11:08:09 EST"
## R version 3.1.1 (2014-07-10)
## Platform: x86_64-unknown-linux-gnu (64-bit)
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] parallel stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] hgu133plus2.db_2.14.0
## [2] org.Hs.eg.db_2.14.0
## [3] RSQLite_0.11.4
## [4] DBI_0.3.1
## [5] AnnotationDbi_1.26.1
## [6] Biobase_2.24.0
## [7] fmdatabreastcaparp1_0.0.1
## [8] fmcorrelationbreastcaparp1_0.0.1
## [9] pracma_1.7.9
## [10] BSgenome.Hsapiens.UCSC.hg19_1.3.1000
## [11] BSgenome_1.32.0
## [12] Biostrings_2.32.1
## [13] XVector_0.4.0
## [14] ggplot2_1.0.0
## [15] GenomicRanges_1.16.4
## [16] GenomeInfoDb_1.0.2
## [17] IRanges_1.22.10
## [18] BiocGenerics_0.10.0
## [19] devtools_1.6.1
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-6 colorspace_1.2-4 digest_0.6.4 evaluate_0.5.5
## [5] formatR_1.0 grid_3.1.1 gtable_0.1.2 knitr_1.7
## [9] labeling_0.3 magrittr_1.0.1 markdown_0.7.4 MASS_7.3-35
## [13] mime_0.2 munsell_0.4.2 plyr_1.8.1 proto_0.3-10
## [17] Rcpp_0.11.3 reshape2_1.4 Rsamtools_1.16.1 scales_0.2.4
## [21] stats4_3.1.1 stringr_0.6.2 tools_3.1.1 zlibbioc_1.10.0