Genome Spot

Pathway analysis has become such a common procedure in bioinformatics, especially in studying gene expression. If you look at a survey of recent papers it seems that there is a bunch of ways that it can be done. In this post, I'll discuss the differences between two commonly used tools; DAVID and GSEA.
The concepts behind the two algorithms are very different. DAVID determines overlaps between user-supplied gene lists and the curated databases, looking for overlaps that are bigger than that expected by random chance. You can improve the accuracy of the algorithm by providing a background file that contains all genes that were considered/detected in the experiment. The user-supplied gene sets are normally generated by selecting genes that pass a significance threshold. The DAVID procedure is similar to others available such as Ingenuity, AmiGO and GeneGO. The selection of significance values is largely arbitrary, but it is common to set the threshold at FDR adjusted p<0.05. Modi…

Pathway analysis is a common procedure for determining the regulation of groups of functionally linked genes. There are a lot of pathway analyses strategies available and I can break them down into these groups:

Bioconductor/R-based: It makes sense to run pathway analysis in the same environment that runs the major differential expression software Limma, edgeR, DESeq, etc. These include CAMERA, MRGST, WilcoxGST, Roast, etcCommercial, GUI based. Such as Ingenuity IPA or MetaCore GeneGO.Java based such as GSEA and GSAAWeb-based tools such as DAVID, WebGestalt, GO Enrichment analysis Now these each have their advantages and disadvantages. I wanted to see whether I could make a tool pathway analysis tool that could be run with just one simple command and didn't require expertise in R. It would run quickly for >10k gene sets and have a lower memory footprint than GSEA.
I wrote a pathway analysis in Bash. I know. Its crazy. But it works. It works in Ubuntu, Fedora, Debian and Mint. It…

Gene sets are distilled information about molecular profiling experiments and can generated based on other features shared by groups of genes such as chromosomal position, sequence, co-regulation, functional information, etc.

These are a valuable resource because they suggest similarities between different molecular profiling experiments or phenomona and lead researchers into understanding the factors that drive the trends in profiling experiments such as gene expression assays by microarray or RNA-seq.

To truly grasp the importance of quality gene sets, consider that the original paper describing the GSEA algorithm has accumulated 3144 citations since 2003, while the paper describing the software and wider applicability of GSEA has 7166 citations. The latter paper has also attracted positive comments from experts in the field on PubMed, here is one that I couldn't agree with more. In the words of Rafael Irizarry, "The idea of analyzing differential expression for groups of g…