During cell differentiation,there typically exists a drastic and sudden shift called cell fate decision-making or bifurcation.Revealing such critical phenomena can provide deeper insights into the fundamental mechanis...During cell differentiation,there typically exists a drastic and sudden shift called cell fate decision-making or bifurcation.Revealing such critical phenomena can provide deeper insights into the fundamental mechanisms that govern the complex intricacies of living organisms.However,many conventional statistical methods fail to predict the specific types of critical transitions and accurately infer cell fate dynamics from singlecell RNA sequencing data.To address this challenge,we develop FatePredictor,a novel computational framework grounded in bifurcation theory and optimal transport theory,to predict cell fate bifurcation based on locally observed information of single-cell data.Specifically,the proposed FatePredictor employs a dynamic unbalanced optimal transport method to reconstruct dynamic cell trajectories,based on which an ensemble deep learning model is utilized to predict the type of dynamics involved in a cell fate bifurcation during cellular processes.The applications on both simulated and real single-cell data demonstrate that FatePredictor serves as a user-friendly and powerful tool for predicting bifurcations of complex biological systems and unveiling intricate cellular trajectories,with higher accuracy compared with many existing methods.Additionally,our FatePredictor has the capacity to pinpoint key genes and pathways related to significant cellular processes.展开更多
基金supported by the National Natural Science Foundation of China(nos.42450084,T2341022,12322119,and 12401630)Guangdong Provincial Key Laboratory of Mathematical and Neural Dynamical Systems(2024B1212010004)+1 种基金the Educational Commission of Guangdong Province of China(2023KQNCX073)the Natural Science Foundation of Guangdong Province of China(2023A1515110558 and 2024A1515011797).
文摘During cell differentiation,there typically exists a drastic and sudden shift called cell fate decision-making or bifurcation.Revealing such critical phenomena can provide deeper insights into the fundamental mechanisms that govern the complex intricacies of living organisms.However,many conventional statistical methods fail to predict the specific types of critical transitions and accurately infer cell fate dynamics from singlecell RNA sequencing data.To address this challenge,we develop FatePredictor,a novel computational framework grounded in bifurcation theory and optimal transport theory,to predict cell fate bifurcation based on locally observed information of single-cell data.Specifically,the proposed FatePredictor employs a dynamic unbalanced optimal transport method to reconstruct dynamic cell trajectories,based on which an ensemble deep learning model is utilized to predict the type of dynamics involved in a cell fate bifurcation during cellular processes.The applications on both simulated and real single-cell data demonstrate that FatePredictor serves as a user-friendly and powerful tool for predicting bifurcations of complex biological systems and unveiling intricate cellular trajectories,with higher accuracy compared with many existing methods.Additionally,our FatePredictor has the capacity to pinpoint key genes and pathways related to significant cellular processes.
