Version 5.4
Help: Gene Ontology Over-representation Analysis
To do gene ontology over-representation analysis (ORA) you first need to upload a list of gene identifiers and associated fold-change in gene expression values (and P values) as described here.
Gene annotation including gene ontology terms associated with the uploaded genes will be returned. (To show associated gene ontology terms go into the show/hide options at the top of the page and select the relevant columns to display - this is not necessary for the ORA tool).
To do the gene ontology ORA click on the red Ontology
ORA button at the top of the page. This will take you to a page where you can choose the parameters for
the over-representation analysis.
First you need to specify whether you are analyzing an entire
array dataset or just a subset of genes. If you try to analyze a subset
of genes using the entire dataset algorithm or vice versa your results
will NOT be correct.
- Fold-Change Cutoff (+/-): choose what fold-change in gene expression threshold should be used to determine which genes are differently expressed. Default = +/- 1.5.
- Expression P-Value Cutoff: choose what P value threshold associated with each fold-change in gene expression value should be used to determine which genes are differently expressed. Default P < 0.05.
- Choose algorithm: several different statistical methods are available to determine if pathways are significantly associated with DE genes - Hypergeometric, Fisher & Chi Square.
- Choose Correction Method: two options to correct for multiple testing are included - The Benjamini & Hochberg correction for the FDR and the more conservative Bonferroni correction.
Hit submit.
A new page will be returned showing the GO terms that are significantly associated with up-regulated genes.
Click the green button to see the GO terms that are significantly associated with down-regulated genes.
Click on the 'summary' link to see information for a specific GO term.