Dream: powerful differential expression analysis for repeated measures designs

Abstract

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 Dream is available within the variancePartition Bioconductor package at http://bioconductor.org/packages/variancePartition Supplementary information Supplementary data are available at Bioinformatics online.

Atlas of genetic effects in human microglia transcriptome across brain regions, aging and disease pathologies

“…Brain region explained comparatively little variance overall (mean 2.95%), but we identified a subset of genes that were strongly variable between regions. We performed pairwise comparisons of differential gene expression between each pair of regions, accounting for shared donors in a linear mixed model 35 ( Figure 2B-C, Table S3-S8 ). The largest number of differentially expressed genes (FDR < 0.05; |log 2 fold change| > 1) were between the SVZ and the two cortical regions (609 in MFG, 909 in STG), whereas comparing STG to MFG found the fewest (6 genes), suggesting microglia in the two cortical regions to be similar.…”

“…Differential expression analysis was performed between the brain regions using the R package Differential expression for repeated measures (DREAM) from VariancePartition 35 . DREAM uses a linear model to increase power and decrease false positives for RNA-seq datasets with repeated measurements.…”

muscat detects subpopulation-specific state transitions from multi-sample multi-condition single-cell transcriptomics data

“…For the first approach (MM-dream), we relied on the variancePartition 52 package's implementation for repeated measurement bulk RNA-seq, using voom's 25 precision weights as originally described but without empirical Bayes moderation and the duplicateCorrelation 53 step, as this was computationally intensive and had a negligible impact on the significance (as also observed previously for batch effects 21 ). Method MM-dream2 uses an updated alternative to this approach using variancePartition's new weighting scheme 54 instead of voom.…”

The three-dimensional landscape of chromatin accessibility in Alzheimer’s disease

“…To predict enhancer-promoter interactions, we employed the "activity-by-contact" method that is based on the combination of frequency (derived from Hi-C) and enhancer activity (derived from ATAC-seq and H3K27ac ChIP-seq). To avoid the inflation of false discovery rate in the differential chromatin accessibility analysis, we applied dream (70), which properly accounts for correlation structures of repeated measures in the study designs utilizing cross-individual testing.…”