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Updated: Sep 5, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

scSTEM: clustering pseudotime ordered single-cell data.

Qi Song¹, Jingtao Wang², Ziv Bar-Joseph^3,4

¹Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, 15213, USA.

|July 7, 2022

Summary

This summary is machine-generated.

We developed single-cell STEM (scSTEM), a new method to cluster gene expression dynamics in single-cell RNA sequencing data. This tool identifies significant gene profiles within developmental trajectories, improving biological process analysis.

Keywords:

Gene clustering Genomics Single cell Visualization

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Last Updated: Sep 5, 2025

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Published on: June 9, 2023

Area of Science:

Computational biology
Genomics
Bioinformatics

Background:

Single-cell RNA sequencing (scRNA-seq) enables studying cellular heterogeneity.
Understanding dynamic gene expression changes is crucial for deciphering biological processes.
Pseudotime analysis orders cells along inferred developmental trajectories.

Purpose of the Study:

To introduce scSTEM, a novel method for clustering gene expression profiles in single-cell RNA-seq data.
To enable the identification and comparison of significant gene expression dynamics across different cellular pathways.

Main Methods:

scSTEM clusters dynamic gene profiles within trajectories inferred from scRNA-seq data.
It employs various metrics to summarize gene expression.
Assigns p-values to clusters for statistical significance.

Main Results:

scSTEM effectively clusters gene expression dynamics in pseudotime-ordered scRNA-seq data.
The method identifies significant gene profiles and facilitates cross-path comparisons.
Demonstrated utility across multiple scRNA-seq datasets.

Conclusions:

scSTEM enhances the analysis of biological processes by providing robust clustering of gene expression trajectories.
The tool improves downstream analysis of scRNA-seq data.
scSTEM is publicly available for use in biological research.