Abstract
Gene set analysis methods, which consider predefined groups of genes in the analysis of genomic data, have been successfully applied for analyzing gene expression data in cross-sectional studies. The time-course gene set analysis (TcGSA) introduced here is an extension of gene set analysis to longitudinal data. The proposed method relies on random effects modeling with maximum likelihood estimates. It allows to use all available repeated measurements while dealing with unbalanced data due to missing at random (MAR) measurements. TcGSA is a hypothesis driven method that identifies a priori defined gene sets with significant expression variations over time, taking into account the potential heterogeneity of expression within gene sets. When biological conditions are compared, the method indicates if the time patterns of gene sets significantly differ according to these conditions. The interest of the method is illustrated by its application to two real life datasets: an HIV therapeutic vaccine trial (DALIA-1 trial), and data from a recent study on influenza and pneumococcal vaccines. In the DALIA-1 trial TcGSA revealed a significant change in gene expression over time within 69 gene sets during vaccination, while a standard univariate individual gene analysis corrected for multiple testing as well as a standard a Gene Set Enrichment Analysis (GSEA) for time series both failed to detect any significant pattern change over time. When applied to the second illustrative data set, TcGSA allowed the identification of 4 gene sets finally found to be linked with the influenza vaccine too although they were found to be associated to the pneumococcal vaccine only in previous analyses. In our simulation study TcGSA exhibits good statistical properties, and an increased power compared to other approaches for analyzing time-course expression patterns of gene sets. The method is made available for the community through an R package.
Highlights
Microarray experiments are increasingly used for evaluating changes in gene expression over time
Gene set analysis methods have been successfully applied in PLOS Computational Biology | DOI:10.1371/journal.pcbi
The gene set analysis [14,15,16] is supposed to be more powerful than a gene-by-gene analysis because it can detect a change of expression of a group of genes none of them show a very high absolute fold change
Summary
Microarray experiments are increasingly used for evaluating changes in gene expression over time. A gene set is a group of genes that are a priori co-regulated or functionally linked. Examples of such gene set relating to biological processes or pathways are those defined by KEGG [11], Gene Ontology [12] or Chaussabel’s functional modules [13]. The gene set analysis [14,15,16] is supposed to be more powerful than a gene-by-gene analysis because it can detect a change of expression of a group of genes none of them show a very high absolute fold change. Gene set analysis avoids a second step for a global interpretation as described in the “bottom up” approach [10, 17]
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