EnrichNet Network-based gene set enrichment analysis

2015-01-13

EnrichNet: network-based gene set enrichment analysis
http://www.enrichnet.org/

Luxembourg/France/Nottingham/CNIO paper
Bioinformatics journal 2012

EnrichNet Network-based gene set enrichment analysis
-I came across this when I searched google for "gene set enrichment analysis video";  I saw these two interesting links: (Gene Set Enrichment Analysis - YouTube (www.youtube.com/watch?v=5Y9rCYldlzo) and EnrichNet - Network-based gene set enrichment analysis ... (www.youtube.com/watch?v=rfyD9DJkAw4) )

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The analysis of functional genomics data from high-throughput
experiments often involves the assessment of potential functional
associations between a gene or protein set of interest, e.g.
differentially expressed genes in a microarray study and known
gene/protein sets representing cellular processes and pathways.

-over-representation analysis
-gene set enrichment analysis
-integrative and modular enrichment analysis (which account for dependencies between genes and proteins inferred from biological networks and ontology graphs)

?I keep forgetting what it means if something is parametric or non-parametric?

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 EnrichNet,
a new integrative enrichment analysis method.

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This combined network analysis and
visualization enables a direct molecular interpretation of how a user-
de?ned set of genes/proteins is related to a gene/protein set of known
function.

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To further facilitate the analysis, a complete
implementation of the integrative approach is made freely available
as a public web application with an exposed programmatic API
(www.enrichnet.org)

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The only required input is a list of 10 or more human gene
or protein identi?ers and the selection of a database of interest (KEGG
(Kanehisa et al., 2006), BioCarta (Nishimura, 2001), WikiPathways (Pico
et al., 2008), Reactome (Joshi-Tope et al., 2005), PID (Schaefer et al., 2009),
InterPro (Apweiler et al., 2001) or GO (Ashburner et al., 2002), from which
reference gene/protein sets will be extracted.

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A random walk on a graph is a stochastic process
modelling the iterative transition of an imaginary particle from a seed node. . .

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 in spite of its name, RWR is a
deterministic procedure modelling a random walk via matrix computations

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 Although the
entire procedure is more computationally expensive than a classical over-
representation analysis, this does not result in signi?cant limitations for
practical use, since an analysis takes only a few minutes for most pathway
databases, and the web-interface optionally provides an e-mail noti?cation
for completed tasks

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 Importantly, EnrichNet and ORA methods are
designed speci?cally for the analysis of gene/protein lists that are not
accompanied by any additional expression or activity measurements, and
microarray data are used only for validation purposes.

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 In all
cases, EnrichNet provides higher enrichment scores than the ORA
approach and its P-value estimates are either lower or below the
detection limit (0.001) for both methods.

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The high Xd-score
for this gene set pair (0.80, the signi?cance threshold is 0.45)
points to a functional association via multiple connecting molecular
interactions, which is con?rmed by the visualization

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 illustrate the utility of the approach for
identifying novel functional associations between gene/protein sets

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