Last updated: 2019-12-18

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File Version Author Date Message
Rmd e576395 Peter Carbonetto 2019-12-18 wflow_publish(“chipseq.Rmd”)
Rmd f899adf Peter Carbonetto 2019-12-18 Working on code to run HF method in chipseq example.
Rmd 500381a Zhengrong Xing 2019-10-28 move gausdemo; add HF for chipseq
html 99d1f34 Peter Carbonetto 2018-12-07 Re-built all the outdated workflowr webpages.
Rmd a09d13e Peter Carbonetto 2018-11-09 Adjusted setup steps in a few of the R Markdown files.
html 0fa970b Peter Carbonetto 2018-11-07 Some small adjustments to the chipseq example text.
Rmd d9f6b81 Peter Carbonetto 2018-11-07 wflow_publish(“chipseq.Rmd”)
html 0b15924 Peter Carbonetto 2018-11-07 Added setup instructions to chipseq analysis.
Rmd f5dec9c Peter Carbonetto 2018-11-07 wflow_publish(“chipseq.Rmd”)
Rmd f9f193c Peter Carbonetto 2018-11-07 Revised setup instructions for a couple .Rmd files.
html 9cf40ea Peter Carbonetto 2018-11-07 Build site.
Rmd 63bc42d Peter Carbonetto 2018-11-07 wflow_publish(“chipseq.Rmd”)
html b66089f Peter Carbonetto 2018-10-19 Added a bit of explanatory text to the end of the chipseq.Rmd analysis.
Rmd 891e862 Peter Carbonetto 2018-10-19 wflow_publish(“chipseq.Rmd”)
html f49c927 Peter Carbonetto 2018-10-18 Adjusted size of figure in chipseq example.
Rmd 3683070 Peter Carbonetto 2018-10-18 wflow_publish(“chipseq.Rmd”)
html 17b5a47 Peter Carbonetto 2018-10-18 Adjusted chipseq R Markdown source.
Rmd 38211a8 Peter Carbonetto 2018-10-18 wflow_publish(“chipseq.Rmd”)
html b9c076d Peter Carbonetto 2018-10-18 First wflow_publish(“chipseq.Rmd”).
Rmd 90e3ac9 Peter Carbonetto 2018-10-18 wflow_publish(“chipseq.Rmd”)
Rmd 1ef7492 Peter Carbonetto 2018-10-18 Working on chipseq R Markdown file.

This is an illustration of “SMoothing by Adaptive SHrinkage” (SMASH) applied to chromatin immunoprecipitation sequencing (“ChIP-seq”) data. This implements the SMASH analysis presented in Sec. 5.2 of the manuscript.

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)
library(haarfisz)

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 and Haar-Fisz methods

The Haar-Fisz method transforms the Poisson counts, then applies Gaussian wavelet methods to the transformed data. Note that this call can take several minutes to run on a modern desktop computer.

res.hf <- denoise.poisson(counts,meth.1 = hf.la10.ti4,cs.1 = 50,hybrid = FALSE)

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

res <- smash.poiss(counts,post.var = TRUE)
# Warning in estimate_mixprop(data, g, prior, optmethod = optmethod, control
# = control, : Optimization failed to converge. Results may be unreliable.
# Try increasing maxiter and rerunning.

# Warning in estimate_mixprop(data, g, prior, optmethod = optmethod, control
# = control, : Optimization failed to converge. Results may be unreliable.
# Try increasing maxiter and rerunning.

# Warning in estimate_mixprop(data, g, prior, optmethod = optmethod, control
# = control, : Optimization failed to converge. Results may be unreliable.
# Try increasing maxiter and rerunning.

# Warning in estimate_mixprop(data, g, prior, optmethod = optmethod, control
# = control, : Optimization failed to converge. Results may be unreliable.
# Try increasing maxiter and rerunning.

# Warning in estimate_mixprop(data, g, prior, optmethod = optmethod, control
# = control, : Optimization failed to converge. Results may be unreliable.
# Try increasing maxiter and rerunning.

# Warning in estimate_mixprop(data, g, prior, optmethod = optmethod, control
# = control, : Optimization failed to converge. Results may be unreliable.
# Try increasing maxiter and rerunning.

Plot the SMASH and Haar-Fisz 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 functions estimated by SMASH (orange line) and the Haar-Fisz method (dark blue 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$est,res.hf,
                    (peaks$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))

Version Author Date
f49c927 Peter Carbonetto 2018-10-18
17b5a47 Peter Carbonetto 2018-10-18
b9c076d Peter Carbonetto 2018-10-18

Based on this plot, it is clear that the strongest Haar-Fisz and 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.


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] haarfisz_4.5     wavethresh_4.6.8 MASS_7.3-48      cowplot_0.9.4   
# [5] ggplot2_3.2.0    smashr_1.2-5    
# 
# loaded via a namespace (and not attached):
#  [1] tidyselect_0.2.5     xfun_0.7             ashr_2.2-39         
#  [4] purrr_0.2.5          lattice_0.20-35      colorspace_1.4-0    
#  [7] htmltools_0.3.6      yaml_2.2.0           rlang_0.4.2         
# [10] mixsqp_0.3-6         later_0.8.0          pillar_1.3.1        
# [13] glue_1.3.1           withr_2.1.2.9000     foreach_1.4.4       
# [16] plyr_1.8.4           stringr_1.4.0        munsell_0.4.3       
# [19] gtable_0.2.0         workflowr_1.5.0.9000 caTools_1.17.1.2    
# [22] codetools_0.2-15     evaluate_0.13        labeling_0.3        
# [25] knitr_1.23           pscl_1.5.2           doParallel_1.0.14   
# [28] httpuv_1.5.0         parallel_3.4.3       Rcpp_1.0.1          
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