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Abstract:
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With the reduction in price of next generation sequencing technologies, gene expression profiling using RNA-seq has increased the scope of sequencing experimental designs to include more complex designs, such as time-course repeated-measures designs. In such designs, RNA samples are extracted from each experimental unit at multiple time points. The read counts that result from RNA sequencing of the samples extracted from the same experimental unit tend to be temporally correlated. Although there are many methods for RNA-seq differential expression analysis, existing methods do not properly account for within-unit correlations that arise in repeated-measures designs. We address this shortcoming by using normalized log-counts and associated precision weights in a linear model pipeline with continuous autoregressive structure to account for correlation among observations within each experimental unit. We then utilize parametric bootstrap to conduct differential expression inference. Simulation studies show the advantages of our method over alternatives that do not account for correlation among observations within experimental units.
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