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. University of Tokyo researchers 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, the researchers 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, they 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.
Flowchart of the Landscape of Differentiation Dynamics (LDD) method
A: A pool of single cells, from which we can obtain the gene expression matrix by single cell sequencing. B: After preprocessing, quality control, and dimension reduction, a low-dimensional data matrix is obtained. C: The samples are clustered into different types. Undirected differentiation paths are determined by a transition matrix between clusters. D: After applying the continuous birth-death process to model the whole differentiation process, the potential V(x), differentiation directions, and landscape can be constructed.
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.
Availability – The codes of LDD and the pre-processed datasets used in the paper can be downloaded from https://github.com/smsxiaomayi/LDD/blob/master/LDDcode.zip.