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

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.

Researchers from the Walter and Eliza Hall Institute of Medical Research 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.

Availability – The weighting methods described in this paper are implemented in the voomWithQualityWeights function in the open-source ‘limma’ package distributed as part of the Bioconductor project (http://www.bioconductor.org/). A Galaxy tool that includes the option to apply ‘voom’ with sample-specific weights in an RNA-seq differential expression analysis is available from the Galaxy Toolshed at https://toolshed.g2.bx.psu.edu/view/shians/voom_rnaseq. The R code and plots of results for all simulation settings along with the R code to carry out the analyses of the ‘Control’ and ‘Smchd1’ RNA-seq experiments are provided as ‘Supplementary Materials’ at http://bioinf.wehi.edu.au/voomWithQualityWeights/.