Gene Vector Analysis (Geneva): a unified method to detect differentially-regulated gene sets and similar microarray experiments.

BMC Bioinformatics
Stephen W Tanner, Pankaj Agarwal

Abstract

Microarray experiments measure changes in the expression of thousands of genes. The resulting lists of genes with changes in expression are then searched for biologically related sets using several divergent methods such as the Fisher Exact Test (as used in multiple GO enrichment tools), Parametric Analysis of Gene Expression (PAGE), Gene Set Enrichment Analysis (GSEA), and the connectivity map. We describe an analytical method (Geneva: Gene Vector Analysis) to relate genes to biological properties and to other similar experiments in a uniform way. This new method works on both gene sets and on gene lists/vectors as input queries, and can effectively query databases consisting of sets of biologically related sets, or of results from other microarray experiments. We also present an improvement to the null model estimate by using the empirical background distribution drawn from previous experiments. We validated Geneva by rediscovering a number of previous findings, and by finding significant relationships within microarrays in the GEO repository. Provided a reasonable corpus of previous experiments is available, this method is more accurate than the class label permutation model, especially for data sets with limited number of r...Continue Reading

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Citations

Apr 3, 2010·Proceedings of the National Academy of Sciences of the United States of America·Haiyan HuangXianghong Jasmine Zhou
Aug 7, 2009·PloS One·Guanghui Hu, Pankaj Agarwal
Nov 28, 2019·Briefings in Bioinformatics·Kequan LinDong Wang

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Methods Mentioned

BETA
chip

Software Mentioned

BioCarta
Cyber
HumanCyc
MAS5
GSEA
Geneva
GOA
MIAME
GenMAPP
PAGE

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