## Monday, September 24, 2012

### Learn R and Python, and Have Fun Doing It

If you need to catch up on all those years you spent not learning how to code (you need to know how to code), here are a few resources to help you quickly learn R and Python, and have a little fun doing it.

First, the free online Coursera course Computing for Data Analysis just started. The 4 week course is being taught by Roger Peng, associate professor of biostatistics at Johns Hopkins, and blogger at Simply Statistics. From the course description:
This course is about learning the fundamental computing skills necessary for effective data analysis. You will learn to program in R and to use R for reading data, writing functions, making informative graphs, and applying modern statistical methods.
Here's a short video about the course from the instructor:

Next, for quickly learning Python, there's the Python track on Codeacademy. Codeacademy takes an interactive approach to teaching coding. The interface gives you some basic instruction and prompts you to enter short code snippets to accomplish a task. Codeacademy makes learning to code fun by giving you short projects to complete (e.g. a tip calculator), and rewarding you with badges for your accomplishments, which allow you to "compete" with friends.

Once you've learned some basic skills, you really only get better with practice and problem solving. Project Euler has been around for some time, and you can find many solutions out there on the web using many different languages, but the problems are more purely mathematical in nature. For short problems perhaps more relevant, head over to Rosalind.info for some bioinformatics programming challenges ranging from something as simple as counting nucleotides or computing GC content, to something more difficult, such as genome assembly.

Coursera: Computing for Data Analysis

Codeacademy Python Track

Rosalind.info bioinformatics programming challenges

## Tuesday, September 18, 2012

### DESeq vs edgeR Comparison

Update (Dec 18, 2012): Please see this related post I wrote about differential isoform expression analysis with Cuffdiff 2.

DESeq and edgeR are two methods and R packages for analyzing quantitative readouts (in the form of counts) from high-throughput experiments such as RNA-seq or ChIP-seq. After alignment, reads are assigned to a feature, where each feature represents a target transcript, in the case of RNA-Seq, or a binding region, in the case of ChIP-Seq. An important summary statistic is the count of the number of reads in a feature (for RNA-Seq, this read count is a good approximation of transcript abundance).

Methods used to analyze array-based data assume a normally distributed, continuous response variable. However, response variables for digital methods like RNA-seq and ChIP-seq are discrete counts. Thus, both DESeq and edgeR methods are based on the negative binomial distribution.

I see these two tools often used interchangeably, and I wanted to take a look at how they stack up to one another in terms of performance, ease of use, and speed. This isn't meant to be a comprehensive evaluation or "bake-off" between the two methods. This would require complex simulations, parameter sweeps, and evaluation with multiple well-characterized real RNA-seq datasets. Further, this is only a start - a full evaluation would need to be much more comprehensive.

Here, I used the newest versions of both edgeR and DESeq, using the well-characterized Pasilla dataset, available in the pasilla Bioconductor package. The dataset is from an experiment in Drosophila investigating the effect of RNAi knockdown of the splicing factor, pasilla. I used the GLM functionality of both packages, as recommended by the vignettes, for dealing with a multifactorial experiment (condition: treated vs. untreated; library type: single-end and paired-end).

Both packages provide built-in functions for assessing overall similarity between samples using either PCA (DESeq) or MDS (edgeR), although these methods operate on the same underlying data and could easily be switched.

PCA plot on variance stabilized data from DESeq:

MDS plot from edgeR:

Per gene dispersion estimates from DESeq:

Biological coefficient of variation versus abundance (edgeR):

Now, let's see how many statistically significant (FDR<0.05) results each method returns:

In this simple example, DESeq finds 820 genes significantly differentially expressed at FDR<0.05, while edgeR is finds these 820 and an additional 371. Let's take a look at the detected fold changes from both methods:

Here, if genes were found differentially expressed by edgeR only, they're colored red; if found by both, colored green. What's striking here is that for a handful of genes, DESeq is (1) reporting massive fold changes, and (2) not calling them statistically significant. What's going on here?

It turns out that these genes have extremely low counts (usually one or two counts in only one or two samples). The DESeq vignette goes through the logic of independent filtering, showing that the likelihood of a gene being significantly differentially expressed is related to how strongly it's expressed, and advocates for discarding extremely lowly expressed genes, because differential expression is likely not statistically detectable.

Count-based filtering can be achieved two ways. The DESeq vignette demonstrates how to filter based on quantiles, while I used the filtering method demonstrated in the edgeR vignette - removing genes without at least 2 counts per million in at least two samples. This filtering code is commented out above - uncomment to filter.

After filtering, all of the genes shown above with apparently large fold changes as detected by DESeq are removed prior to filtering, and the fold changes correlate much better between the two methods. edgeR still detects ~50% more differentially expressed genes, and it's unclear to me (1) why this is the case, and (2) if this is necessarily a good thing.

Conclusions:

Unfortunately, I may have oversold the title here - this is such a cursory comparison of the two methods that I would hesitate to draw any conclusions about which method is better than the other. In addition to finding more significantly differentially expressed genes (again, not necessarily a good thing), I can say that edgeR was much faster than DESeq for fitting GLM models, but it took slightly longer to estimate the dispersion. Further without any independent filtering, edgeR gave me moderated fold changes for the extremely lowly expressed genes for which DESeq returned logFCs in the 20-30 range (but these transcripts were so lowly expressed anyway, they should have been filtered out before any evaluation).

If there's one thing that will make me use edgeR over DESeq (until I have time to do a more thorough evaluation), it's the fact that using edgeR seems much more natural than DESeq, especially if you're familiar with the limma package (pretty much the standard for analyzing microarray data and other continuously distributed gene expression data). Setting up the design matrix and specifying contrasts feels natural if you're familiar with using limma. Further, the edgeR user guide weighs in at 67 pages, filled with many case studies that will help you in putting together a design matrix for nearly any experimental design: paired designs, time courses, batch effects, interactions, etc. The DESeq documentation is still fantastic, but could benefit from a few more case studies / examples.

What do you think? Anyone want to fork my R code and help do this comparison more comprehensively (more examples, simulated data, speed benchmarking)? Is the analysis above fair? What do you find more easy to use, or is ease-of-use (and thus, reproducibility) even important when considering data analysis?

Getting Genetics Done by Stephen Turner is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported License.