Modeling group heteroscedasticity in single-cell RNA-seq pseudo-bulk data

Abstract

Group heteroscedasticity is commonly observed in pseudo-bulk single-cell RNA-seq datasets and its presence can hamper the detection of differentially expressed genes. Since most bulk RNA-seq methods assume equal group variances, we introduce two new approaches that account for heteroscedastic groups, namely voomByGroup and voomWithQualityWeights using a blocked design (voomQWB). Compared to current gold-standard methods that do not account for group heteroscedasticity, we show results from simulations and various experiments that demonstrate the superior performance of voomByGroup and voomQWB in terms of error control and power when group variances in pseudo-bulk single-cell RNA-seq data are unequal.

Group Heteroscedasticity - A Silent Saboteur of Power and False Discovery in RNA-Seq Differential Expression

“…BCV, a key indicator of biological variability in gene expression among biological replicates, is widely used for estimating gene expression variance in RNA-Seq data 45 . Following previous studies 72 , here we demonstrate group heteroscedasticity by comparing the variability in group-specific BCV values and visualize this effect in the form of distinctive positions and shapes of group-specific mean-variance trend curves across these twelve diverse datasets (Fig. S1).…”

“…1). Our approach mirrors that of a previous study 72 which identified group heteroscedasticity through higher biological coefficient of variation (BCV) values in one group compared to another. BCV, a key indicator of biological variability in gene expression among biological replicates, is widely used for estimating gene expression variance in RNA-Seq data 45 .…”

“…Corroborating the presence of group heteroscedasticity in RNA-Seq data Previous studies have noted the presence of group heteroscedasticity in RNA-Seq data 15,39,10,50,72 ; however, the implementation of models that account for group heteroscedasticity remains scarce. This section aims to emphasize this phenomenon further by presenting additional evidence that group heteroscedasticity is not merely an incidental occurrence in RNA-Seq data but a common feature.…”

“…Two of these methods were based on negative binomial (NB) assumptions, which include the widely employed DESeq2 40 and edgeR.LRT (edgeR + likelihood-ratio test) 53 , while two operate under a nonparametric setup, including dearseq 21 and the classical Wilcoxon rank-sum test. The remaining two methods involve two variants of the limma model for differential expression analysis of RNA-Seq data, referred to as voom 33 (limma + voom transformation) and voomByGroup 72 (limma + group-specific trends).…”

“…Another study by Chen et al 10 highlighted unequal group variance in single-cell data and emphasized the under-utilization of tests accounting for varying variances. More recently, a study by You et al 72 highlighted that group heteroscedasticity is present in pseudo-bulk data and subsequently proposed a statistical model to account for it. However, the extent to which heteroscedasticity impacts false discovery and power, especially in large samples, remains to be investigated.…”