Sensitivity analysis of cell fate trajectories from single-cell transcriptomics

Cell differentiation is a dynamic process in which cells traverse through high-dimensional gene expression space under the influence of gene regulatory networks and environmental cues. Recent advances in single-cell RNA sequencing (scRNA-seq) have enabled us to measure high-resolution snapshots of this dynamic process. Several computational methods have been developed to reconstruct cellular flow maps from these snapshots, revealing the trajectories taken by cells in gene expression space. While existing methods provide increasingly detailed descriptions of cellular trajectories, the stability of these trajectories to perturbations is largely unexplored. As such, it remains unclear how robust inferred trajectories are to perturbations, which genes most strongly influence long-term fate outcomes, and where instability arises between competing fate commitments. While sensitivity and stability analysis tools from dynamical systems theory provide a principled way to study the stability of differentiation trajectories, existing approaches are not designed for the high-dimensionality and sparsity of scRNA-seq data. Here, we introduce FateSens, a sensitivity-based computational framework for analyzing gene regulatory dynamics using flow maps derived from scRNA-seq data. FateSens performs sensitivity analysis of differentiation trajectories derived from scRNA-seq data to identify regulatory genes and fate boundaries. To demonstrate its utility, we applied FateSens to study neutrophil-monocyte differentiation using scRNA-seq data of mouse hematopoiesis. While FateSens relies only on transcriptomic measurements, this dataset also contains lineage tracing barcodes that provide ground-truth fate relationships. Our results show that FateSens accurately recovers regulators consistent with known biology and identifies fate boundaries that are supported by lineage tracing data.

We implement FateSens in Python 3, with an open-source implementation available at: https://github.com/sashittal-group/FateSens.