Dream: powerful differential expression analysis for repeated measures designs.

ABSTRACT:

Summary

Large-scale transcriptome studies with multiple samples per individual are widely used to study disease biology. Yet, current methods for differential expression are inadequate for cross-individual testing for these repeated measures designs. Most problematic, we observe across multiple datasets that current methods can give reproducible false-positive findings that are driven by genetic regulation of gene expression, yet are unrelated to the trait of interest. Here, we introduce a statistical software package, dream, that increases power, controls the false positive rate, enables multiple types of hypothesis tests, and integrates with standard workflows. In 12 analyses in 6 independent datasets, dream yields biological insight not found with existing software while address

SUBMITTER: Hoffman GE 

PROVIDER: S-EPMC8055218 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature