Spatial transition tensor of single cells.
Peijie Zhou, Federico Bocci, Tiejun Li, Qing Nie
Author Information
Peijie Zhou: Department of Mathematics, University of California, Irvine, Irvine, CA, USA. ORCID
Federico Bocci: Department of Mathematics, University of California, Irvine, Irvine, CA, USA.
Tiejun Li: LMAM and School of Mathematical Sciences, Peking University, Beijing, China.
Qing Nie: Department of Mathematics, University of California, Irvine, Irvine, CA, USA. qnie@uci.edu. ORCID
中文译文
English
Spatial transcriptomics and messenger RNA splicing encode extensive spatiotemporal information for cell states and transitions. The current lineage-inference methods either lack spatial dynamics for state transition or cannot capture different dynamics associated with multiple cell states and transition paths. Here we present spatial transition tensor (STT), a method that uses messenger RNA splicing and spatial transcriptomes through a multiscale dynamical model to characterize multistability in space. By learning a four-dimensional transition tensor and spatial-constrained random walk, STT reconstructs cell-state-specific dynamics and spatial state transitions via both short-time local tensor streamlines between cells and long-time transition paths among attractors. Benchmarking and applications of STT on several transcriptome datasets via multiple technologies on epithelial-mesenchymal transitions, blood development, spatially resolved mouse brain and chicken heart development, indicate STT's capability in recovering cell-state-specific dynamics and their associated genes not seen using existing methods. Overall, STT provides a consistent multiscale description of single-cell transcriptome data across multiple spatiotemporal scales.
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R01 AR079150/NIAMS NIH HHS
R01AR079150/Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
U01 AR073159/NIAMS NIH HHS
MCB2028424/National Science Foundation (NSF)
U01AR073159/Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
Animals
Single-Cell Analysis
Mice
Transcriptome
RNA Splicing
Brain
Epithelial-Mesenchymal Transition
Gene Expression Profiling
Chickens
RNA, Messenger
Algorithms