scSTEM: clustering pseudotime ordered single-cell data

Qi Song; Jingtao Wang; Ziv Bar-Joseph

doi:10.1186/s13059-022-02716-9

scSTEM: clustering pseudotime ordered single-cell data

Genome Biol. 2022 Jul 7;23(1):150. doi: 10.1186/s13059-022-02716-9.

Authors

Qi Song¹, Jingtao Wang², Ziv Bar-Joseph^{3

4}

Affiliations

¹ Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, 15213, USA.
² Department of Medicine, Division of Experimental Medicine, McGill University, Montreal, QC, Canada.
³ Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, 15213, USA. zivbj@cs.cmu.edu.
⁴ Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, 15213, USA. zivbj@cs.cmu.edu.

Abstract

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 .

Keywords: Gene clustering; Genomics; Single cell; Visualization.

Publication types

Research Support, N.I.H., Extramural

MeSH terms

Algorithms*
Cluster Analysis
Gene Expression Profiling / methods
Sequence Analysis, RNA / methods
Single-Cell Analysis* / methods
Software

Abstract

Publication types

MeSH terms

Grants and funding