Robust expression variability testing reveals heterogeneous T cell responses

Abstract

SummaryCell-to-cell transcriptional variability in otherwise homogeneous cell populations plays a crucial role in tissue function and development. Single-cell RNA sequencing can characterise this variability in a transcriptome-wide manner. However, technical variation and the confounding between variability and mean expression estimates hinders meaningful comparison of expression variability between cell populations. To address this problem, we introduce a novel analysis approach that extends the BASiCS statistical framework to derive a residual measure of variability that is not confounded by mean expression. Moreover, we introduce a new and robust procedure for quantifying technical noise in experiments where technical spike-in molecules are not available. We illustrate how our method provides biological insight into the dynamics of cell-to-cell expression variability, highlighting a synchronisation of the translational machinery in immune cells upon activation. Additionally, our approach identifies new patterns of variability across CD4 + T cell differentiation.

BASiCS workflow: a step-by-step analysis of expression variability using single cell RNA sequencing data

“…The Bioconductor package BASiCS implements a Bayesian hierarchical framework that accounts for both technical and biological sources of noise in scRNA-seq datasets. 23 – 25 BASiCS jointly performs data normalisation, technical noise quantification and downstream analyses, whilst propagating statistical uncertainty across these steps. These features are implemented within a probabilistic model that builds upon a negative binomial framework, a widely used distribution in the context of bulk and scRNA-seq experiments.…”