An increment of diversity method for cell state trajectory inference of time-series scRNA-seq data

作     者：Yan Hong Hanshuang Li Chunshen Long Pengfei Liang Jian Zhou Yongchun Zuo 

作者机构：State Key Laboratory of Reproductive Regulation and Breeding of Grassland LivestockInstitutes of Biomedical SciencesCollege of Life SciencesInner Mongolia UniversityHohhot010020China 

出 版 物：《Fundamental Research》 (自然科学基础研究（英文版）)

年 卷 期：2024年第4卷第4期

页      面：770-776页

核心收录：

学科分类：1001[医学-基础医学(可授医学、理学学位)] 100101[医学-人体解剖与组织胚胎学] 10[医学] 

基　　金：the National Natural Science Foundation of China(62061034,62171241) the key technology research program of Inner Mongolia Autonomous Region(2021GG0398) the Science and Technology Leading Talent Team in Inner Mongolia Autonomous Region(2022LJRC0009) 

主　　题：Increment of diversity Time-series scRNA-seq data Cell state trajectory inference Topology similarity Branching accuracy 

摘      要：The increasing emergence of the time-series single-cell RNA sequencing(scRNA-seq)data,inferring developmental trajectory by connecting transcriptome similar cell states(i.e.,cell types or clusters)has become a major *** existing computational methods are designed for individual cells and do not take into account the available time series *** present IDTI based on the Increment of Diversity for Trajectory Inference,which combines time series information and the minimum increment of diversity method to infer cell state trajectory of time-series scRNA-seq *** apply IDTI to simulated and three real diverse tissue development datasets,and compare it with six other commonly used trajectory inference methods in terms of topology similarity and branching *** results have shown that the IDTI method accurately constructs the cell state trajectory without the requirement of starting *** the performance test,we further demonstrate that IDTI has the advantages of high accuracy and strong robustness.