Dynamic inference of cell developmental complex energy landscape from time series single-cell transcriptomic data.

ABSTRACT: Time series single-cell RNA sequencing (scRNA-seq) data are emerging. However, dynamic inference of an evolving cell population from time series scRNA-seq data is challenging owing to the stochasticity and nonlinearity of the underlying biological processes. This calls for the development of mathematical models and methods capable of reconstructing cellular dynamic transition processes and uncovering the nonlinear cell-cell interactions. In this study, we present GraphFP, a nonlinear Fokker-Planck equation on graph based model and dynamic inference framework, with the aim of reconstructing the cell state-transition complex potential energy landscape from time series single-cell transcriptomic data. The free energy of our model explicitly takes into account of the cell-cell interactions in

SUBMITTER: Jiang Q 

PROVIDER: S-EPMC8812873 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature