Dpath software reveals hierarchical haemato-endothelial lineages of Etv2 progenitors based on single-cell transcriptome analysis.
Nat Commun 2017;
8:14362. [PMID:
28181481 PMCID:
PMC5309826 DOI:
10.1038/ncomms14362]
[Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2016] [Accepted: 12/20/2016] [Indexed: 01/04/2023] Open
Abstract
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.
Single-cell RNA sequencing has enabled great advances in understanding developmental biology but reconstructing cellular lineages from this data remains challenging. Here the authors develop an algorithm, dpath, which models the lineage relationships of underlying single cells based on single cell RNA seq data and apply it to study lineage progression of Etv2 expressing progenitors.
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