Pathway Analysis is usually performed with aim to enrich the genes with its functional information and reveal the underlying biological mechanisms pursue by genes. Pathway Analysis is not only limited to what biological pathways a particular set of expressed genes follow but also to diclose the relationships between these genes. With availability of more genomics, transcriptomics and proteomics data, interactions between genes involve in multiple pathways become more clear and also relationships between the genes, and their gene products. However existing tools, and dbs more based on knowledge driven approach in which pathways will be identified by finding the correlation between the information in one of the pathway knowledge databases (KEGG,Reactome,Panther,BioCarta, Panther,GO,NCI,WikiPathways,etc) with gene expression result for a specific conditions for instance tumor, obesity , cold resistant crops/plants, etc.
Introductory Articles/ppt/sources:
http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002375
http://bioinformatics.mdanderson.org/MicroarrayCourse/Lectures09/Pathway%20Analysis.pdf
http://gettinggeneticsdone.blogspot.de/2012/03/pathway-analysis-for-high-throughput.html
http://davetang.org/muse/tag/pathway/
https://www.biostars.org/p/42219/
Impotant Database and Tools:
GeneMANIA, Cytoscape, IPA (Commerical), Pathway Commons, Reactome ,Panther, BioCyc, WikiPathways, Pathvisio, KEGG, NCI, Stringdb, WebGestalt ,ConsensusPathDB ,GSEA,Blast2go
Popular R based tools:
Reactome.db, ReactomePA, ClusterProfiler, Gage, SPIA, topGO, Pathview,DOSE
More:
http://www.bioconductor.org/help/search/index.html?q=Enrichment+analysis+
