PT  - JOURNAL ARTICLE
AU  - Nils Eling
AU  - Arianne C. Richard
AU  - Sylvia Richardson
AU  - John C. Marioni
AU  - Catalina A. Vallejos
TI  - Robust expression variability testing reveals heterogeneous T cell responses
AID  - 10.1101/237214
DP  - 2017 Jan 01
TA  - bioRxiv
PG  - 237214
4099  - http://biorxiv.org/content/early/2017/12/21/237214.short
4100  - http://biorxiv.org/content/early/2017/12/21/237214.full
AB  - Cell-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.