Journal
JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS
Volume 20, Issue 4, Pages 577-597Publisher
SPRINGER
DOI: 10.1007/s13253-015-0226-1
Keywords
False discovery rate; Generalized linear model; Quasi-likelihood; Residual feed intake
Funding
- Agriculture and Food Research Initiative Competitive Grant from the United States Department of Agriculture (USDA) National Institute of Food and Agriculture (NIFA) [2011-68004-30336]
- National Institute of General Medical Sciences (NIGMS) of the National Institutes of Health (NIH)
- National Science Foundation (NSF)/NIGMS Mathematical Biology Program [R01GM109458]
- Vietnam Education Foundation (VEF)
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A common challenge in analysis of transcriptomic data is to identify differentially expressed genes, i.e., genes whose mean transcript abundance levels differ across the levels of a factor of scientific interest. Transcript abundance levels can be measured simultaneously for thousands of genes in multiple biological samples using RNA sequencing (RNA-seq) technology. Part of the variation in RNA-seq measures of transcript abundance may be associated with variation in continuous and/or categorical covariates measured for each experimental unit or RNA sample. Ignoring relevant covariates or modeling the effects of irrelevant covariates can be detrimental to identifying differentially expressed genes. We propose a backward selection strategy for selecting a set of covariates whose effects are accounted for when searching for differentially expressed genes. We illustrate our approach through the analysis of an RNA-seq study intended to identify genes differentially expressed between two lines of pigs divergently selected for residual feed intake. We use simulation to show the advantages of our backward selection procedure over alternative strategies that either ignore or adjust for all measured covariates.
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