Landscape-inspired order parameters for classifying cell fate using single-cell RNA-seq data

Recent advances in single cell RNA sequencing (scRNA-seq) have made it possible to measure the gene expression profiles of individual cells on an enormous scale. However, with thousands of genes per measurement and high rates of dropout, the question of how to analyze this high-dimensional noisy data to understand and identify cell fate remains a pressing problem.

We show that we can use a spin-glass inspired epigenetic landscape model of cell identity to classify cell type quickly and accurately from scRNA-seq measurements using “order parameters” derived from single cell expression atlases (for example, the Mouse Cell Atlas). We demonstrate the efficacy of our method by analyzing directed stem cell differentiation protocols designed to produce murine lung lineages.