Gene set analysis for longitudinal gene expression data

Background: Gene set analysis (GSA) has become a successful tool to interpret gene expression profiles in terms of biological functions, molecular pathways, or genomic locations. GSA performs statistical tests for independent microarray samples at the level of gene sets rather than individual genes. Nowadays, an increasing number of microarray studies are conducted to explore the dynamic changes of gene expression in a variety of species and biological scenarios. In these longitudinal studies, gene expression is repeatedly measured over time such that a GSA needs to take into account the within-gene correlations in addition to possible between-gene correlations. Results: We provide a robust nonparametric approach to compare the expressions of longitudinally measured sets of genes under multiple treatments or experimental conditions. The limiting distributions of our statistics are derived when the number of genes goes to infinity while the number of replications can be small. When the...

• Type: Journal
• Year: 2011
• Where: BMCBI
• Authors: Ke Zhang, Haiyan Wang, Arne C. Bathke, Solomon W. Harrar, Hans-Peter Piepho, Youping Deng
• Keywords: Biometrics, BMCBI 2011, Correlation Structures, Data Distributions, Gene Expression Profiles