Genome Spot

For first-pass pathway analysis I find Reactome to be the most useful database of gene sets for biologists to understand. For a long time I have been using Reactome gene sets as deposited to the GSEA/MSigDB website. Recently a colleague pointed me to the gene matrix file offered directly on the Reactome webpage (Thanks Dr Okabe).

There are some differences. Firstly there are more gene sets in the one from the Reactome webpage (accessed 2018-05-09)
$ wc -l *gmt     674 c2.cp.reactome.v6.1.symbols.gmt    2022 ReactomePathways.gmt    2696 total
Secondly, there are more genes included in one or more gene sets:
$ cut -f3- c2.cp.reactome.v6.1.symbols.gmt | tr '\t' '\n' | sort -u | wc -l 6025 $ cut -f3- ReactomePathways.gmt | tr '\t' '\n' | sort -u | wc -l 10852
And overall there are about threefold more gene-pathway entries 
$ cut -f3- ReactomePathways.gmt | wc -w 106405 $ cut -f3- c2.cp.reactome.v6.1.symbols.gmt | wc -w 37601
I also looked at whether the gen…

Previously I wrote about how to make a gene expression rankfile using the unix shell. While this works for me just fine, there are some differences in the behaviour of shell tools like awk an sed that make it unusable for mac users.

Therefore, I think the best solution is to show you how to do this in R, which you can install on Linux, Mac and Windows systems.

The expression data for this demo looks like this (the first 5 columns).


Name                        logFC               logCPM            LR                PValue ENSG00000134294.9_SLC38A2   0.365464972841137   8.35504063447063  80.9697378748286  2.29200821312451e-19 ENSG00000164692.13_COL1A2   0.369440969815233   6.9371167845581   59.239238358843   1.39621819298599e-14 ENSG00000117152.9_RGS4      0.417512339007825   6.27805181231213  48.690887963755   2.99655418444362e-12 ENSG00000120738.7_EGR1      -0.448872068345368  5.72373823077344  40.5159113813129  1.95021420597484e-10 ENSG00000236552.1_RPL13AP5  -0.263118776572713  7.87437056…

In my previous posts, I've recommended using GSEA pre-ranked for performing pathway analysis through the graphical user interface. If you have a more elaborate experiment with several sample groups and a few different gene set libraries to analyse, then you'll find that process quite tedious.

So in this post, I'd like to share with you my GSEA wrapper written in bash to make this process a bit easier. The wrapper script simply accepts two user defined arguments, the rank file and the gmt file. Take note that this works only when the gene IDs are already gene symbols. If you have probe IDs or some other type of accession number, then you'll need to convert them to gene symbols or use a "chip file". The rank files and GMT files need to be in the current directory. You may need to adjust the memory up or down depending on your machine spec and the memory required for your jobs.

#!/bin/bash
RNK=$1
GMT=$2
java -Xmx4096m -cp /path/to/gsea2-2.2.1.jar xtools.gsea.GseaPre…

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…