共 102 条
Temporal modelling using single-cell transcriptomics
被引:103
作者:

Ding, Jun
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机构:
McGill Univ, Dept Med, Meakins Christie Labs, Ctr Hlth, Montreal, PQ, Canada McGill Univ, Dept Med, Meakins Christie Labs, Ctr Hlth, Montreal, PQ, Canada

Sharon, Nadav
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Technion Israel Inst Technol, Dept Biol, Haifa, Israel McGill Univ, Dept Med, Meakins Christie Labs, Ctr Hlth, Montreal, PQ, Canada

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机构:
[1] McGill Univ, Dept Med, Meakins Christie Labs, Ctr Hlth, Montreal, PQ, Canada
[2] Technion Israel Inst Technol, Dept Biol, Haifa, Israel
[3] Carnegie Mellon Univ, Sch Comp Sci, Machine Learning Dept, Pittsburgh, PA 15213 USA
[4] Carnegie Mellon Univ, Sch Comp Sci, Computat Biol Dept, Pittsburgh, PA 15213 USA
基金:
美国国家卫生研究院;
关键词:
REGULATORY NETWORKS;
EXPRESSION;
RNA;
DYNAMICS;
TIME;
DIFFERENTIATION;
NORMALIZATION;
RESOLVES;
SEQ;
D O I:
10.1038/s41576-021-00444-7
中图分类号:
Q3 [遗传学];
学科分类号:
071007 ;
090102 ;
摘要:
Methods for profiling genes at the single-cell level have revolutionized our ability to study several biological processes and systems including development, differentiation, response programmes and disease progression. In many of these studies, cells are profiled over time in order to infer dynamic changes in cell states and types, sets of expressed genes, active pathways and key regulators. However, time-series single-cell RNA sequencing (scRNA-seq) also raises several new analysis and modelling issues. These issues range from determining when and how deep to profile cells, linking cells within and between time points, learning continuous trajectories, and integrating bulk and single-cell data for reconstructing models of dynamic networks. In this Review, we discuss several approaches for the analysis and modelling of time-series scRNA-seq, highlighting their steps, key assumptions, and the types of data and biological questions they are most appropriate for.
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页码:355 / 368
页数:14
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