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

Cell-to-cell gene expression variability is an inherent feature of complex biological systems, such as immunity and development. Single-cell RNA sequencing a powerful tool to quantify this heterogeneity, but it prone strong technical noise. In article, we describe step-by-step computational workflow that uses the BASiCS Bioconductor package robustly within between known groups cells (such experimental conditions or cell types). integrated framework for data normalisation, noise quantification downstream analyses, propagating statistical uncertainty across these steps. Within single seemingly homogeneous population, can identify highly variable genes exhibit heterogeneity well lowly with stable expression. also probabilistic decision rule changes in populations, whilst avoiding confounding effects related differences overall abundance. Using publicly available dataset, guide users through complete pipeline includes preliminary steps quality control, exploration using scater scran packages. The accompanied by Docker image ensures reproducibility our results.