Quantitative gene set analysis generalized for repeated measures, confounder adjustment, and continuous covariates

BMC Bioinformatics
Jacob A TurnerDerek M Blankenship

Abstract

Gene set analysis (GSA) of gene expression data can be highly powerful when the biological signal is weak compared to other sources of variability in the data. However, many gene set analysis approaches utilize permutation tests which are not appropriate for complex study designs. For example, the correlation of subjects is broken when comparing time points within a longitudinal study. Linear mixed models provide a method to analyze longitudinal studies as well as adjust for potential confounding factors and account for sources of variability that are not of primary interest. Currently, there are no known gene set analysis approaches that fully account for these study design and analysis aspects. In order to do so, we generalize the QuSAGE gene set analysis algorithm, denoted Q-Gen, and provide the necessary estimation adjustments to incorporate linear mixed model analyses. We assessed the performance of our generalized method in comparison to the original QuSAGE method in settings such as longitudinal repeated measures analysis and accounting for potential confounders. We demonstrate that the original QuSAGE method can not control for type-I error when these complexities exist. In addition to statistical appropriateness, analy...Continue Reading

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Citations

Jun 9, 2017·Bioinformatics·Jinghua GuJacob A Turner
Apr 3, 2019·PLoS Computational Biology·Hailong MengSteven H Kleinstein
Apr 10, 2019·The Journal of Experimental Medicine·Seunghee HongVirginia Pascual
Dec 12, 2019·BMC Bioinformatics·Elham Khodayari MoezIrina Dinu
Jun 27, 2021·Nature Communications·Romain BanchereauThomas Powles

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Datasets Mentioned

BETA
GSE30550

Software Mentioned

Qusage
- Gen
CAMERA
GSEA
Quantitative Set Analysis for Gene Expression ( QuSAGE )
Bioconductor
Gen

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