Reconstructing differentiation networks and their regulation from time series single-cell expression data.

Generating detailed and accurate organogenesis models using single-cell RNA-seq data remains a major challenge. Current methods have relied primarily on the assumption that descendant cells are similar to their parents in terms of gene expression levels. These assumptions do not always hold for in vivo studies, which often include infrequently sampled, unsynchronized, and diverse cell populations. Thus, additional information may be needed to determine the correct ordering and branching of progenitor cells and the set of transcription factors (TFs) that are active during advancing stages of organogenesis. To enable such modeling, we have developed a method that learns a probabilistic model that integrates expression similarity with regulatory information to reconstruct the dynamic developmental cell trajectories. When applied to mouse lung developmental data, the method accurately distinguished different cell types and lineages. Existing and new experimental data validated the ability of the method to identify key regulators of cell fate.