metaGSEA-package: Meta-analysis of GSEA analyses, including intra- and...

In drmjc/metaGSEA: Meta-analysis of GSEA analyses, including intra- and inter-experiment comparisons

Description

metaGSEA is a collection of R and bash code to simplify the down-stream analysis of GSEA results. Efficient methods for importing and storing GSEA outputs, comparing genesets within a single GSEA run, and comparing genesets between different GSEA runs. Useful visualisation is the key strength of this package, utilising GSEA bar plots, correspondence at the top (CAT) plots, and hierarchical clustering plots of geneset similarities. Also the ability to clean up GSEA output on unix-alike systems, by improving the GSEA preranked html output, and the gene tables in each GSEA report by adding the gene symbol, description and hyperlinks to Entrez Gene.

Details

Background
GSEA is a popular program from the Broad Institute for performing functional analysis of gene expression data. It generates rich, interactive outputs, however each GSEA run is limited to one biological comparison, vs 1 collection of genesets. In more complex experimental designs, its easy to end up with many GSEA result sets, and comparing between them all to find consistent genesets, or inconsistent genesets across all comparisons becomes very challenging. Enter metaGSEA.

Leading edge genes
One of the unique properties of GSEA is the concept of leading edge genes within a geneset. It's very rare for all genes within a geneset to be dramatically up- or down-regulated; by way of example, if we identify a strongly up-regulated geneset, there's usually ~60% of the genes that are up-regulated, ~30% that are unchanged, and the remaining ~10% are down-regulated. The leading edge genes are those that contribute towards the geneset getting its up-regulated score. It's quite possible for 2 GSEA runs to find the same geneset strongly up-regulated, however with almost opposite usage of genes within their leading edges. If we were studying hypoxia via 2 different experimental systems, we might be pleased to see that the hypoxic geneset is ranked #1 in 2 different microarray studies, however knowing whether the hypoxic signature is driven by the same genes in the 2 systems is extremely important. The bulk of the uniqueness of metaGSEA is that it can compare between multiple GSEA runs using these leading edge genes, thereby revealing more molecular detail than just comparing 2 GSEA runs at the level of the geneset names.

Expected input types
metaGSEA supports a number of usage modes, including: analysis of just 1 GSEA result, analysis of multiple GSEA results generated using different comparisons to the same GMT file (eg c2_all), or analysis of multiple GSEA runs on the same comparison vs multiple GMT files. metaGSEA supports GSEA and GseaPreRanked results.

Usage
You can import, filter and export GSEA results, allowing better control over what data you send to an external GSEA visualisation tool, such as the LeadingEdge viewer tool in the GSEA GUI, or via GenePattern. Filtering can be done on all the columns that you see in the up/down-regulated geneset summaries, namely geneset name, and all the statistics.

If you have just 1 GSEA result, besides filtering it, you can assess the similarity between genesets using plot_gsea.leadingedge, which compares genesets using the leading edge genes, and creates a Heirarchical clustering dendrogram, heatmap, barplot, and adjacency matrix.

If you have multiple GSEA results compared to the same GMT file (ie the geneset names will overlap), then you can compare results using gsea.compare.runs.1gmt, then filter those results via gsea.compare.runs.filter, and plot the [dis]similarities via: plot_gsea.venn, plot_gseacmp.barplot, CAT plots, and by a combined HCL plot.

If you have multiple GSEA resuls vs different GMT files (ie the geneset names will mostly not match), then in addition to performing the analyses described above on each individual result, we find a multi-panel CAT plot to be quite useful.

EnrichmentMap
The EnrichmentMap plugin for Cytoscape lets you load GSEA results. it then generates networks of geneset similarity, just like the HCL plots that metaGSEA creates. However, EnrichmentMap compares all genes in the geneset, so you end you looking at the structure of the genesets as they are stored in MsigDB, not as they relate to your own dataset. You can create a custom leading-edge only genes version of the GMT file, using import.gsea.leadingedge (which is automatically run when you do an import.gsea), followed by export.gsea.gmt.

Package:	metaGSEA
Type:	Package
Version:	1.0
Date:	2009-09-24
License:	GPL
LazyLoad:	yes

Author(s)

Mark Cowley

Maintainer: Mark Cowley <[e-mail]>

References

http://www.broadinstitute.org/gsea

See Also

import.gsea, gsea.filter, export.gsea

drmjc/metaGSEA documentation built on Aug. 8, 2020, 1:53 p.m.