scSTEM: clustering pseudotime ordered single-cell data

被引：0
作者：
Qi Song
Jingtao Wang
Ziv Bar-Joseph
机构：
[1] Carnegie Mellon University,Computational Biology Department, School of Computer Science
[2] McGill University,Department of Medicine, Division of Experimental Medicine
[3] Carnegie Mellon University,Machine Learning Department, School of Computer Science
来源：
Genome Biology | / 23卷
关键词：
Single cell; Genomics; Gene clustering; Visualization;
D O I：
暂无
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学科分类号：
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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