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 addressing the issue of reproducible false-positive findings.

Availability and implementation

Dream is available within the variancePartition Bioconductor package at http://bioconductor.org/packages/variancePartition.

Contact

[e-mail].

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Hoffman GE 

PROVIDER: S-EPMC8055218 | biostudies-literature |

REPOSITORIES: biostudies-literature