Hi -
Has anyone else used the GSEA analysis feature in CLC Genomics work bench? How do you interpret the results? How does the test statistic provided relate to a p value? I can't find anything in their online tutorials.
Thanks, Sharon
Hi -
Has anyone else used the GSEA analysis feature in CLC Genomics work bench? How do you interpret the results? How does the test statistic provided relate to a p value? I can't find anything in their online tutorials.
Thanks, Sharon
There's a section in the manual that explains how the GSEA test is implemented: http://resources.qiagenbioinformatics.com/manuals/clcmainworkbench/780/index.php?manual=Gene_set_enrichment_analysis.html
Conceptually, the entry to wikipedia (https://en.wikipedia.org/wiki/Gene_set_enrichment_analysis) is helpful. But remember, GSEA is largely human focussed, which may not be relevant to non-human systems.
In essence, GSEA identifies (statistically) over/under represented gene functions based on what is expected (human).
There are a number of ways to use GSEA analysis in CLC Genomics Workbench and Server - for RNAseq, miRNAseq, microarray analysis, and functional metagenomics profiling. In general, the CLC platform allows for Gene Ontology files to either be imported directly or directly downloaded (from GOC) and then used to annotate your results.
Given that this post was from a couple of years ago - if you're referring to the GSEA that was part of the RNAseq tools prior to Genomics Workbench v12- the details on it's implementation are here http://resources.qiagenbioinformatics.com/manuals/clcgenomicsworkbench/current/index.php?manual=Gene_Set_Enrichment_Analysis.html
(also Tian et. al. 2005 - https://www.ncbi.nlm.nih.gov/pubmed/16174746)
We also have a hypergeometric test (GOStats) from Falcon et. al 2007.
I'll also add that we improved the implementation of these tests for (mRNA) RNAseq data specifically in the current release (details here: http://resources.qiagenbioinformatics.com/manuals/clcgenomicsworkbench/current/index.php?manual=Gene_Set_Test.html)
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