Enrichment Analysis for Customized Organism
- From this version, the EnrichmentPipeline has been adopted by EACO, which is more powerfull and comprehensive.
The EnrichmentPipeline package has been designed initially to do enrichment analysis for locust transciptome, which was published on PLoS ONE, 5(12): e15633 at 2010. Because of its easy to use, afterwards, the pipeline was applied to many projects at BGI.
This pipeline is designed to do enrichment analysis for the category data, such as GO/KEGG/IPR, etc. These data are characterised as one class containing many genes and one gene is involved in many categories. So, for every class, a p value is calculated representing the probability that the observed numbers of counts could have resulted from randomly distributing this class between the tested gene list and the reference gene list. Usually, the reference gene list is the total genes of one organism annotated, but one can use customized gene list as reference. The p value can be approximated by ChiSquare-Fisher test or hypergeometric test. The p values are corrected by fdr or other methods.
For GO enrichment, first the hierarchy feature of GO information is converted to MetaGO_release.RData object from GO.db package of bioconductor. This package will be updated every half year. Second, the GOdata_organism.RData are constructed for specific organisms. This file contain the map relationship for GOs at level 3 to genes and genes to the lowest level GO annotated, several functions used in the enrichment analysis. Finally the enrichment analysis is done based on the GOdata_organism.RData and supplied gene list at level 3 and all levels. To remove the redundance, if the GOs enriched at different levels with parent-child relationship and have the same gene list, the lowest level is choose and other levels are filtered. The results file is suffix "merg".
Reference:
- Chen S, Yang P, Jiang F, Wei Y, Ma Z, Kang L: De Novo Analysis of Transcriptome Dynamics in the Migratory Locust during the Development of Phase Traits. PLoS One 2010, 5(12):e15633.
- Huang da, W., B.T. Sherman, and R.A. Lempicki. 2009. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res 37: 1-13.
- Beissbarth, T. and T.P. Speed. 2004. GOstat: find statistically overrepresented Gene Ontologies within a group of genes. Bioinformatics 20: 1464-1465.