ChIP-seq example

Initial setup instructions

To run this example on your own computer, please follow these setup instructions. These instructions assume you already have R (optionally, RStudio) installed on your computer.

Download or clone the git repository on your computer.

Launch R, and change the working directory to be the “analysis” folder inside your local copy of the git repository.

Install the devtools, ggplot2 and cowplot packages used here and in the code below:

install.packages(c("devtools","ggplot2","cowplot"))

Finally, install the smashr package from GitHub:

devtools::install_github("stephenslab/smashr")

See the “Session Info” at the bottom for the versions of the software and R packages that were used to generate the results shown below.

Set up R environment

Loading the smashr, ggplot2 and cowplot packages, as well as some additional functions used to implement the analysis below.

source("../code/chipseq.functions.R")
library(smashr)
library(ggplot2)
library(cowplot)

Load the ChIP-seq data

The ChIP-seq data are sequencing read counts for transcription factor YY1 in cell line GM12878, restricted to 880,001–1,011,072 bp on chromosome 1. These data were collected as part of the ENCODE (“Encyclopedia Of DNA Elements”) project. The data are included with the git repository.

load("../data/reg_880000_1011072.RData")
bppos  <- 880001:1011072
counts <- M[1,] + M[,2]

Note that there are two replicates of the GM12878 cell line, so we analyze the combined read counts from both replicates, stored in the counts vectors.

Run SMASH

Next, we apply SMASH to the read counts to estimate the mean and variance of the underlying signal. It may take several minutes to complete this step.

res <- smash.poiss(counts,post.var = TRUE)

Plot the SMASH estimates

To provide a “baseline” to compare against the SMASH estimates, we retrieve the peaks identified in the same ChIP-seq data using the MACS software. Again, these data should have been included with the git repository you downloaded.

macs.file <- "../data/Gm1287peaks_chr1_sorted.txt"
peaks <- read.macs.peaks(macs.file,min(bppos),max(bppos))

This next plot shows the intensity function estimated by SMASH (orange line). The read count data are depicted as light blue circles, in which the area of each circle is scaled by the number of data points that fall within each 1.6-kb “bin”. (We show counts summarized within bins because there are too many data points to plot them individually.)

The peaks identified by the MACS software are shown as red triangles. (Specifically, these are the mean positions of the identified peak intervals; the peak intervals are short enough that it is not useful to show both the start and end positions of these intervals.)

create.chipseq.plot(bppos/1e6,counts,res$est,(peaks$start + peaks$end)/2e6,
                    nbreaks = 80) +
  scale_x_continuous(limits = c(0.88,1.02),breaks = seq(0.88,1.02,0.02)) +
  scale_y_continuous(limits = c(-1,9),breaks = seq(0,8,2))

Based on this plot, it is clear that the the strongest SMASH intensity estimates align very closely with the peaks found by MACS. Intriguingly, the SMASH estimates also suggest the presence of several additional weaker peaks that were not identified by MACS.

Session information

sessionInfo()
# R version 3.4.3 (2017-11-30)
# Platform: x86_64-apple-darwin15.6.0 (64-bit)
# Running under: macOS High Sierra 10.13.6
# 
# Matrix products: default
# BLAS: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRblas.0.dylib
# LAPACK: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRlapack.dylib
# 
# locale:
# [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
# 
# attached base packages:
# [1] stats     graphics  grDevices utils     datasets  methods   base     
# 
# other attached packages:
# [1] cowplot_0.9.3 ggplot2_3.1.0 smashr_1.2-0 
# 
# loaded via a namespace (and not attached):
#  [1] Rcpp_1.0.0        bindr_0.1.1       pillar_1.2.1     
#  [4] plyr_1.8.4        compiler_3.4.3    git2r_0.23.0     
#  [7] workflowr_1.1.1   R.methodsS3_1.7.1 R.utils_2.6.0    
# [10] bitops_1.0-6      iterators_1.0.9   tools_3.4.3      
# [13] digest_0.6.17     tibble_1.4.2      evaluate_0.11    
# [16] gtable_0.2.0      lattice_0.20-35   pkgconfig_2.0.2  
# [19] rlang_0.2.2       Matrix_1.2-12     foreach_1.4.4    
# [22] yaml_2.2.0        parallel_3.4.3    bindrcpp_0.2.2   
# [25] withr_2.1.2       dplyr_0.7.6       stringr_1.3.1    
# [28] knitr_1.20        REBayes_1.3       caTools_1.17.1   
# [31] tidyselect_0.2.4  rprojroot_1.3-2   grid_3.4.3       
# [34] glue_1.3.0        data.table_1.11.4 R6_2.2.2         
# [37] rmarkdown_1.10    purrr_0.2.5       ashr_2.2-23      
# [40] magrittr_1.5      whisker_0.3-2     backports_1.1.2  
# [43] scales_0.5.0      codetools_0.2-15  htmltools_0.3.6  
# [46] MASS_7.3-48       assertthat_0.2.0  colorspace_1.4-0 
# [49] labeling_0.3      wavethresh_4.6.8  stringi_1.2.4    
# [52] Rmosek_8.0.69     lazyeval_0.2.1    doParallel_1.0.11
# [55] pscl_1.5.2        munsell_0.4.3     truncnorm_1.0-8  
# [58] SQUAREM_2017.10-1 R.oo_1.21.0