FateNet: an integration of dynamical systems and deep learning for cell fate prediction

Abstract

Understanding cellular decision-making, particularly its timing and impact on the biological system such as tissue health and function, is a fundamental challenge in biology and medicine. Existing methods for inferring fate decisions and cellular state dynamics from single-cell RNA sequencing data lack precision regarding decision points and broader tissue implications. Addressing this gap, we present FateNet, a computational approach integrating dynamical systems theory and deep learning to probe the cell decision-making process using scRNA-seq data. By leveraging information about normal forms and scaling behavior near tipping points common to many dynamical systems, FateNet accurately predicts cell decision occurrence and offers qualitative insights into the new state of the biological system. Also, through in-silico perturbation experiments, FateNet identifies key genes and pathways governing the differentiation process in hematopoiesis. Validated using different scRNA-seq data, FateNet emerges as a user-friendly and valuable tool for predicting critical points in biological processes, providing insights into complex trajectories.

Fatecode enables cell fate regulator prediction using classification-supervised autoencoder perturbation

“…However, these latter methods often have trouble identifying accurate trajectories and branchpoints. 15 , 16 Furthermore, none of the above methods are designed to identify cell fate regulators in normal developmental processes.…”