Monday, April 13, 2015

Comparing the execution time between foverlaps and findOverlaps [data.table vs GenomicRanges]

Both of these functions find overlaps between genomic intervals. The findOverlaps function is from the Bioconductor package GenomicRanges (or IRanges if you don't need to compare intervals with an associated chromosome and strand). foverlaps is from the data.table package and is inspired by findOvelaps.

In genomics, we often have one large data set X with small interval ranges (usually sequenced reads) and another smaller data set Y with larger interval spans (usually exons, introns etc.). Generally, we are tasked with finding which intervals in X overlap with which intervals in Y.

In the foverlaps function Y has to be indexed using the setkey function (we don't have to do it on X). The key is intended to speed-up finding overlaps.

Which one is faster?

To check this we used the benchmark function from the rbenchmark package. It's a simple wrapper of the system.time function.

The code below plots the execution time of both functions for increasing numbers of rows of data set X.

Interestingly,  foverlaps is the fastest way to solve the problem of finding overlaps, but only when the large data set has less than 200k rows.

We also plotted situation when we exchanged the place of X and Y in arguments of both functions. In this case you can see that almost from the beginning foverlaps is much slower than findOverlaps.

Information about my R session:

> sessionInfo()
R version 3.1.3 (2015-03-09)
Platform: x86_64-apple-darwin13.4.0 (64-bit)
Running under: OS X 10.9.4 (Mavericks)

[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] stats4    parallel  stats     graphics  grDevices utils     datasets 
[8] methods   base     

other attached packages:
[1] data.table_1.9.4     rbenchmark_1.0.0     GenomicRanges_1.18.4
[4] GenomeInfoDb_1.2.4   IRanges_2.0.1        S4Vectors_0.4.0     
[7] BiocGenerics_0.12.1 

loaded via a namespace (and not attached):
[1] chron_2.3-45   plyr_1.8.1     Rcpp_0.11.5    reshape2_1.4.1 stringr_0.6.2 
[6] tools_3.1.3    XVector_0.6.0

Tuesday, March 31, 2015

Using genomation to analyze methylation profiles from Roadmap epigenomics and ENCODE

The genomation package is a toolkit for annotation and visualization of various genomic data. The package is currently also in BioC. It allows to analyze high-throughput data, including bisulfite sequencing data. Here, we will visualize the distribution of CpG methylation around promoters and their locations within gene structures on human chromosome 3.

Heatmap and plot of meta-profiles of CpG methylation around promoters

In this example we use data from Reduced Representation Bisulfite Sequencing (RRBS) and
Whole-genome Bisulfite Sequencing (WGBS) techniques and H1 and IMR90 cell types
derived from the ENCODE and the Roadmap Epigenomics Project databases.

We download the datasets and convert them to GRanges objects. Using rtracklayer and genomation functions. We also use a refseq bed file for annotation and extraction of promoter regions using readTranscriptFeatures function.

Since we have read the files now we can build base-pair resolution matrices of scores(methylation values) for each experiment. The returned list of matrices can be used to draw heatmaps or meta profiles of methylation ratio around promoters.

Distribution of covered CpGs across gene regions

genomation facilitates visualization of given locations of features aggregated by  exons, introns, promoters and TSSs. To find the distribution of covered CpGs within these gene structures, we will use transcript features we previously obtained. Here is the breakdown of the code

  1. Count overlap statistics between our CpGs from WGBS and RRBS H1 cell type and gene structures
  2. Calculate percentage of CpGs overlapping with annotation
  3. plot them in a form of pie charts

Tuesday, February 18, 2014

summary, annotation and visualization of genomic intervals with genomation package

We have been working on a new R package, genomation, that will help with the analysis of genomic intervals. Briefly, the package contains a collection of tools for visualizing and analyzing genome-wide data sets. The package works with a variety of genomic interval file types (BAM, bigWig, BED, GFF and any tabular format containing genomic locations) and enables easy summarization and annotation of high throughput data sets with given genomic annotations. Below is a presentation I'm preparing for an upcoming internal meeting. You can find more information and the vignette on the webpage.

genomation is published in Bioinformatics
Akalin A, Franke V, Vlahoviček K, Mason CE, Schübeler D. genomation: a toolkit to summarize, annotate and visualize genomic intervals. Bioinformatics. 2014 Nov 21. pii: btu775

It is also currently in the Bioconductor development version

Sunday, August 25, 2013

Using JavaScript visualization libraries with R

This is a short tutorial on knitr/markdown and JS visualization packages googleVis and rCharts. With these packages you can create web pages with interactive visualizations just using R. This will require minimal or no knowledge of HTML or JavaScript. 
You need to have the following R packages and their dependencies installed:
  • knitr
  • googleVis
  • rCharts (not on CRAN)
The tutorial is organized in four parts. First two parts introduce basics about knitr and markdown. The last parts are about using googleVis and rCharts packages in markdown documents. However, the tutorial does not have in-depth examples that show all the capabilities of rCharts and googleVis.

You can download the .Rmd files (or clone the repository from github) and run knit2html() on them in your R console, or if you are using RStudio you can click "knit HTML" button on the upper left corner.

The best way to go through the tutorial is to examine the code chunks and explanations in .Rmd files, and then check the HTML output from knit2html().

1. R and markdown

markdown_knitr.Rmd shows basics of markdown and knitr integration. These tools will help you create an HTML document using R. knit2html()output is here. In addition, R markdown basics are described here.

2. Customizing code output in R markdown documents

controlling_knitr.Rmd shows how code chunk output can be controlled by knitr options. knit2html() output is here

3. Using Google visualization API in R markdown documents

googleVis.Rmd shows how to use googleVis package in a markdown document.You can incorporate plots from Google Visualization API in your R markdown document, which will be converted to an HTML document by knit2html(), the HTML output is here.

4. Using multiple JS visualization libraries in R markdown documents

rCharts.Rmd shows how to use rCharts package in a markdown document. Using rCharts, you can incorporate various JS visualizations (such as Polychart, NVD3 etc.) on your HTML document. knit2html() output is here.

The project page is:

Wednesday, July 24, 2013

Q&A forum for methylKit

methylKit is an R package for DNA methylation analysis and annotation using high-throughput bisulfite sequencing data. We recently activated a Q&A forum for methylKit related questions. If you are using methylKit and if you have questions about it,  it is a great place to ask questions and browse previously answered questions.

You can reach it via!forum/methylkit_discussion

Tuesday, March 19, 2013

EpiWorkshop 2013: DNA methylation analysis in R

Elemento Lab at Weill Cornell Medical College organized a workshop on Epigenomics. I had the opportunity to give a tutorial on DNA methylation analysis in R. The tutorial demonstrates how to analyze high-throughput bisulfite sequencing data ( RRBS is used as an example in the tutorial) with R package methylKit.

My slides are below and all of the workshop material is here at the workshop website.

Friday, March 1, 2013

Using ENCODE methylation data (RRBS) in R

ENCODE project has generated reduced-representation bilsulfite sequencing data for multiple cell lines. The data is organized in an extended bed format with additional columns denoting % methylation and coverage per base. Luckily, this sort of generic % methylation information can be read in by R package methylKit, which is a package for analyzing basepair resolution 5mC and 5hmC data.

The code snippets below show how to read RRBS bed file produced by ENCODE. But, let's first download the files.


Unfortunately, methylKit currently can not read them directly because the track definition line causes a problem. It should be deleted from each bed file. Ideally, methylKit should be able to skip over lines (this issue should be fixed in later versions)
For now, we have to use some unix tools to remove the first lines from the bed files. You run the code below in your terminal. This set of commands will delete the first line from every *.gz file in the directory so be careful.
for files in *.gz
  gzip -dc "$files" | tail +2 | gzip -c > "$files".tmp
  if [ "$?" -eq 0 ]; then
    mv "$files".tmp "$files"

Now we can read the files using methylKit. The pipeline argument defines which columns in the file are corresponding to chr,start,end,strand, percent methylation and coverage:
You can also read multiple files at a time:

Since we have read the files and now they are methylKit objects, we can use all of the methylKit functionality on these objects. For example, the code below plots the distribution of methylation % for covered bases.

getMethylationStats(obj[[1]], plot = TRUE)

You can check the methylKit vignette and the website for the rest of the functionality and details.