Developmental, stem cell and cancer biologists are interested in the molecular definition of cellular differentiation. Although single-cell RNA sequencing represents a transformational advance for global gene analyses, novel obstacles have emerged, including the computational management of dropout events, the reconstruction of biological pathways and the isolation of target cell populations. We develop an algorithm named dpath that applies the concept of metagene entropy and allows the ranking of cells based on their differentiation potential. We also develop self-organizing map (SOM) and random walk with restart (RWR) algorithms to separate the progenitors from the differentiated cells and reconstruct the lineage hierarchies in an unbiased manner. We test these algorithms using single cells from Etv2-EYFP transgenic mouse embryos and reveal specific molecular pathways that direct differentiation programmes involving the haemato-endothelial lineages. This software program quantitatively assesses the progenitor and committed states in single-cell RNA-seq data sets in a non-biased manner.
Bibliographical noteFunding Information:
Support was obtained from the National Institutes of Health (R01HL122576 and U01HL100407 to D.J.G. and HL099997-101330A to T.L.R. and W.G.) and the Department of Defense (GRANT11763537). We acknowledge the support from the University of Minnesota Supercomputing Institute. We thank Rachel Gohla and Satyabrata Das for assistance in animal husbandry and cell isolation, Daniel Ly for histological sectioning and immunohistochemical techniques and Jerry Daniel and Kenneth Beckman at the University of Minnesota Genomics Center.