Why weight? Modelling sample and observational level variability improves power in RNA-seq analyses

TLDR

Variations in sample quality in small RNA‑seq experiments pose a major challenge for differential expression analysis, and removing high‑variation samples reduces power while retaining them may mask true changes. The authors aim to improve power by modeling heterogeneity at both sample and observation levels in differential expression analysis. They fit a log‑linear variance model with sample‑specific or group‑specific parameters shared across genes, convert estimated variances to weights, and combine these with observation‑level weights from voom’s mean‑variance relationship. Simulations and real data show this weighted approach yields universally higher power and fewer false discoveries compared to conventional methods, and is available in the limma package.

Abstract

Variations in sample quality are frequently encountered in small RNA-sequencing experiments, and pose a major challenge in a differential expression analysis. Removal of high variation samples reduces noise, but at a cost of reducing power, thus limiting our ability to detect biologically meaningful changes. Similarly, retaining these samples in the analysis may not reveal any statistically significant changes due to the higher noise level. A compromise is to use all available data, but to down-weight the observations from more variable samples. We describe a statistical approach that facilitates this by modelling heterogeneity at both the sample and observational levels as part of the differential expression analysis. At the sample level this is achieved by fitting a log-linear variance model that includes common sample-specific or group-specific parameters that are shared between genes. The estimated sample variance factors are then converted to weights and combined with observational level weights obtained from the mean–variance relationship of the log-counts-per-million using 'voom'. A comprehensive analysis involving both simulations and experimental RNA-sequencing data demonstrates that this strategy leads to a universally more powerful analysis and fewer false discoveries when compared to conventional approaches. This methodology has wide application and is implemented in the open-source 'limma' package.