Description Details Author(s) References See Also
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.
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 |
Mark Cowley
Maintainer: Mark Cowley <m.cowley@garvan.org.au>
http://www.broadinstitute.org/gsea
import.gsea
, gsea.filter
, export.gsea
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