ROAST: rotation gene set tests for complex microarray experiments

Abstract

MOTIVATION A gene set test is a differential expression analysis in which a P-value is assigned to a set of genes as a unit. Gene set tests are valuable for increasing statistical power, organizing and interpreting results and for relating expression patterns across different experiments. Existing methods are based on permutation. Methods that rely on permutation of probes unrealistically assume independence of genes, while those that rely on permutation of sample are suitable only for two-group comparisons with a good number of replicates in each group. RESULTS We present ROAST, a statistically rigorous gene set test that allows for gene-wise correlation while being applicable to almost any experimental design. Instead of permutation, ROAST uses rotation, a Monte Carlo technology for multivariate regression. Since the number of rotations does not depend on sample size, ROAST gives useful results even for experiments with minimal replication. ROAST allows for any experimental design that can be expressed as a linear model, and can also incorporate array weights and correlated samples. ROAST can be tuned for situations in which only a subset of the genes in the set are actively involved in the molecular pathway. ROAST can test for uni- or bi-direction regulation. Probes can also be weighted to allow for prior importance. The power and size of the ROAST procedure is demonstrated in a simulation study, and compared to that of a representative permutation method. Finally, ROAST is used to test the degree of transcriptional conservation between human and mouse mammary stems. AVAILABILITY ROAST is implemented as a function in the Bioconductor package limma available from www.bioconductor.org.

DOI: 10.1093/bioinformatics/btq401

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@inproceedings{Wu2010ROASTRG, title={ROAST: rotation gene set tests for complex microarray experiments}, author={Di Wu and Elgene Lim and François Vaillant and Marie-Liesse Asselin-Labat and Jane E. Visvader and Gordon K. Smyth}, booktitle={Bioinformatics}, year={2010} }