A variety of pathway/gene-set approaches have been proposed to provide evidence of higher-level biological phenomena in the association of expression with experimental condition or clinical outcome.Among these approaches,it has been repeatedly shown that resampling methods are far preferable to approaches that implicitly assume independence of genes.However,few approaches have been optimized for the specific characteristics of RNA-Seq transcription data,in which mapped tags produce discrete counts with varying library sizes,and with potential outliers or skewness patterns that violate parametric assumptions.We describe transformations to RNA-Seq data to improve power for linear associations with outcome and flexibly handle normalization factors.