Literature DB >> 19645689

Rotation testing in gene set enrichment analysis for small direct comparison experiments.

Guro Dørum1, Lars Snipen, Margrete Solheim, Solve Saebø.   

Abstract

Gene Set Enrichment Analysis (GSEA) is a method for analysing gene expression data with a focus on a priori defined gene sets. The permutation test generally used in GSEA for testing the significance of gene set enrichment involves permutation of a phenotype vector and is developed for data from an indirect comparison design, i.e. unpaired data. In some studies the samples representing two phenotypes are paired, e.g. samples taken from a patient before and after treatment, or if samples representing two phenotypes are hybridised to the same two-channel array (direct comparison design). In this paper we will focus on data from direct comparison experiments, but the methods can be applied to paired data in general. For these types of data, a standard permutation test for paired data that randomly re-signs samples can be used. However, if the sample size is very small, which is often the case for a direct comparison design, a permutation test will give very imprecise estimates of the p-values. Here we propose using a rotation test rather than a permutation test for estimation of significance in GSEA of direct comparison data with a limited number of samples. Our proposed rotation test makes GSEA applicable to direct comparison data with few samples, by depending on rotations of the data instead of permutations. The rotation test is a generalisation of the permutation test, and can in addition be used on indirect comparison data and for testing significance of other types of test statistics outside the GSEA framework.

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Year:  2009        PMID: 19645689     DOI: 10.2202/1544-6115.1418

Source DB:  PubMed          Journal:  Stat Appl Genet Mol Biol        ISSN: 1544-6115


  14 in total

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2.  Likelihood-Based Approach to Gene Set Enrichment Analysis with a Finite Mixture Model.

Authors:  Sang Mee Lee; Baolin Wu; John H Kersey
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3.  Camera: a competitive gene set test accounting for inter-gene correlation.

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Journal:  Nucleic Acids Res       Date:  2012-05-25       Impact factor: 16.971

4.  ROAST: rotation gene set tests for complex microarray experiments.

Authors:  Di Wu; Elgene Lim; François Vaillant; Marie-Liesse Asselin-Labat; Jane E Visvader; Gordon K Smyth
Journal:  Bioinformatics       Date:  2010-07-07       Impact factor: 6.937

5.  Network-based biomarkers enhance classical approaches to prognostic gene expression signatures.

Authors:  Rebecca L Barter; Sarah-Jane Schramm; Graham J Mann; Yee Hwa Yang
Journal:  BMC Syst Biol       Date:  2014-12-08

6.  Transcriptome analysis of genes regulated by cholesterol loading in two strains of mouse macrophages associates lysosome pathway and ER stress response with atherosclerosis susceptibility.

Authors:  Stela Z Berisha; Jeffrey Hsu; Peggy Robinet; Jonathan D Smith
Journal:  PLoS One       Date:  2013-05-21       Impact factor: 3.240

7.  Differential expression analysis for pathways.

Authors:  Winston A Haynes; Roger Higdon; Larissa Stanberry; Dwayne Collins; Eugene Kolker
Journal:  PLoS Comput Biol       Date:  2013-03-14       Impact factor: 4.475

8.  Ensuring the statistical soundness of competitive gene set approaches: gene filtering and genome-scale coverage are essential.

Authors:  Shailesh Tripathi; Galina V Glazko; Frank Emmert-Streib
Journal:  Nucleic Acids Res       Date:  2013-02-06       Impact factor: 16.971

9.  GSVA: gene set variation analysis for microarray and RNA-seq data.

Authors:  Sonja Hänzelmann; Robert Castelo; Justin Guinney
Journal:  BMC Bioinformatics       Date:  2013-01-16       Impact factor: 3.169

Review 10.  Harnessing the complexity of gene expression data from cancer: from single gene to structural pathway methods.

Authors:  Frank Emmert-Streib; Shailesh Tripathi; Ricardo de Matos Simoes
Journal:  Biol Direct       Date:  2012-12-10       Impact factor: 4.540

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