scSTEM: clustering pseudotime ordered single-cell data

被引：6
作者：
Song, Qi [1 ]
Wang, Jingtao [2 ]
Bar-Joseph, Ziv [1 ,3 ]
机构：
[1] Carnegie Mellon Univ, Sch Comp Sci, Computat Biol Dept, Pittsburgh, PA 15213 USA
[2] McGill Univ, Dept Med, Div Expt Med, Montreal, PQ, Canada
[3] Carnegie Mellon Univ, Sch Comp Sci, Machine Learning Dept, Pittsburgh, PA 15213 USA
来源：
GENOME BIOLOGY | 2022年 / 23卷 / 01期
关键词：
Single cell; Genomics; Gene clustering; Visualization;
D O I：
10.1186/s13059-022-02716-9
中图分类号：
Q81 [生物工程学（生物技术）]; Q93 [微生物学];
学科分类号：
071005 ; 0836 ; 090102 ; 100705 ;
摘要：
We develop scSTEM, single-cell STEM, a method for clustering dynamic profiles of genes in trajectories inferred from pseudotime ordering of single-cell RNA-seq (scRNA-seq) data. scSTEM uses one of several metrics to summarize the expression of genes and assigns a p-value to clusters enabling the identification of significant profiles and comparison of profiles across different paths. Application of scSTEM to several scRNA-seq datasets demonstrates its usefulness and ability to improve downstream analysis of biological processes. scSTEM is available at https://github.com/alexQiSong/scSTEM.
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页数：17
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