Quantifying pluripotency landscape of cell differentiation from scRNA-seq data by continuous birth-death process

Jifan Shi, Tiejun Li, Luonan Chen and Kazuyuki Aihara

PLOS Computational Biology, 2019, vol. 15, issue 11, 1-17

Abstract: Modeling cell differentiation from omics data is an essential problem in systems biology research. Although many algorithms have been established to analyze scRNA-seq data, approaches to infer the pseudo-time of cells or quantify their potency have not yet been satisfactorily solved. Here, we propose the Landscape of Differentiation Dynamics (LDD) method, which calculates cell potentials and constructs their differentiation landscape by a continuous birth-death process from scRNA-seq data. From the viewpoint of stochastic dynamics, we exploited the features of the differentiation process and quantified the differentiation landscape based on the source-sink diffusion process. In comparison with other scRNA-seq methods in seven benchmark datasets, we found that LDD could accurately and efficiently build the evolution tree of cells with pseudo-time, in particular quantifying their differentiation landscape in terms of potency. This study provides not only a computational tool to quantify cell potency or the Waddington potential landscape based on scRNA-seq data, but also novel insights to understand the cell differentiation process from a dynamic perspective.Author summary: Quantifying the Waddington landscape of cell differentiation from high throughput data is a challenging problem in systems biology and biophysics. Here, we propose a theoretical method named LDD (Landscape of Differentiation Dynamics), which builds cell potentials and constructs their differentiation landscape by a continuous birth-death process from scRNA-seq data. This method well exploits the dynamical features of the differentiation process, thus quantifying the differentiation landscape in an accurate manner. We show that LDD can accurately and efficiently build the evolution tree of cells with pseudo-time, in particular quantifying their differentiation landscape in terms of potency. Taken together, this study provides not only a computational tool to quantify cell potency based on scRNA-seq data, but also a theoretical approach to understand the cell differentiation process from a dynamic perspective.

Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1007488

DOI: 10.1371/journal.pcbi.1007488

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