Cell-to-cell transcriptional variability in seemingly homogeneous cell populations plays a crucial role in tissue function and development. Single-cell RNA sequencing (scRNAseq) can characterise this variability in a transcriptome-wide manner. However, scRNAseq is prone to high levels of technical noise, creating new challenges for identifying genes that show genuine heterogeneous expression. I will discuss some of the challenges that arise when using normalisation methods developed for bulk data in the context of scRNAseq analyses. Moreover, I will introduce BASiCS - an integrated Bayesian framework to perform data normalisation, technical noise quantification and selected downstream analyses. Beyond traditional mean expression testing, BASiCS can robustly identify changes in transcriptional variability between cell populations (such as experimental conditions or cell types). I will illustrate this feature in the context of immune cell populations. Finally, I will discuss ongoing efforts to extend and improve the scalability of BASiCS.