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Rotation gene set testing for longitudinal expression data
Author(s) -
Dørum Guro,
Snipen Lars,
Solheim Margrete,
Sæbø Solve
Publication year - 2014
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.201100178
Subject(s) - permutation (music) , data set , rotation (mathematics) , set (abstract data type) , correlation , data mining , microarray analysis techniques , algorithm , mathematics , statistical hypothesis testing , computer science , dna microarray , resampling , gene , computational biology , statistics , gene expression , biology , genetics , artificial intelligence , physics , geometry , acoustics , programming language
Gene set analysis methods are popular tools for identifying differentially expressed gene sets in microarray data. Most existing methods use a permutation test to assess significance for each gene set. The permutation test's assumption of exchangeable samples is often not satisfied for time‐series data and complex experimental designs, and in addition it requires a certain number of samples to compute p ‐values accurately. The method presented here uses a rotation test rather than a permutation test to assess significance. The rotation test can compute accurate p ‐values also for very small sample sizes. The method can handle complex designs and is particularly suited for longitudinal microarray data where the samples may have complex correlation structures. Dependencies between genes, modeled with the use of gene networks, are incorporated in the estimation of correlations between samples. In addition, the method can test for both gene sets that are differentially expressed and gene sets that show strong time trends. We show on simulated longitudinal data that the ability to identify important gene sets may be improved by taking the correlation structure between samples into account. Applied to real data, the method identifies both gene sets with constant expression and gene sets with strong time trends.