Benchmarking association analyses of continuous exposures with RNA-seq in observational studies.

ABSTRACT: Large datasets of hundreds to thousands of individuals measuring RNA-seq in observational studies are becoming available. Many popular software packages for analysis of RNA-seq data were constructed to study differences in expression signatures in an experimental design with well-defined conditions (exposures). In contrast, observational studies may have varying levels of confounding transcript-exposure associations; further, exposure measures may vary from discrete (exposed, yes/no) to continuous (levels of exposure), with non-normal distributions of exposure. We compare popular software for gene expression-DESeq2, edgeR and limma-as well as linear regression-based analyses for studying the association of continuous exposures with RNA-seq. We developed a computation pipeline that includes

SUBMITTER: Sofer T 

PROVIDER: S-EPMC8574950 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature