Untangling biological factors influencing trajectory inference from single cell data (vol 2, 10.1093/nargab/lqaa053, 2020)

Advances in single-cell RNA sequencing over the past decade has shifted the discussion of cell identity towards the transcriptional state of the cell. While the incredible resolution provided by single-cell RNA sequencing has led to great advances in unravelling tissue heterogeneity and inferring cell differentiation dynamics, it raises the question of which sources of variation are important for determining cellular identity. Here we show that confounding biological sources of variation, most notably the cell cycle, can distort the inference of differentiation trajectories. We show that by factorizing single cell data into distinct sources of variation, we can select a relevant set of factors that constitute the core regulators for trajetory inference, while filtering out confounding sources of variation (e.g. cell cycle) which can perturb the inferred trajectory. Script are available publicly on https://github.com/mochar/cell_variation.Significance StatementPseudotime inference is a bioinformatics tool used to characterize and understand the role and activity of genes involved in cell differentiation. To achieve this, the level of expression of thousands of genes are simultaneously used to order cells along a developmental axis. However, this may result in distorted trajectories as many genes are not necessary involved in cell differentiation, and might even provide the pseudotime inference tool with conflicting (confounding) information. Here we present a methodology for improving inference of the differentiation trajectories by restricting it to a small set of genes assumed to regulate cell differentiation.