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Related Experiment Video

Updated: Dec 28, 2025

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

Published on: October 25, 2018

GeneSwitches: ordering gene expression and functional events in single-cell experiments.

Elaine Y Cao¹, John F Ouyang¹, Owen J L Rackham¹

¹Program in Cardiovascular and Metabolic Disorders, Duke-NUS Medical School, 169857, Singapore.

Bioinformatics (Oxford, England)

|February 15, 2020

Summary

This summary is machine-generated.

GeneSwitches identifies the precise order of gene expression changes over pseudo-time in single-cell RNA sequencing data. This tool helps researchers understand dynamic biological processes and functional events by ordering gene switching events.

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Last Updated: Dec 28, 2025

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Single Cell Transcriptional Profiling of Adult Mouse Cardiomyocytes

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Published on: December 28, 2011

Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Single-cell RNA sequencing (scRNA-seq) enables gene expression analysis at the individual cell level.
Pseudo-time trajectory inference tools order cells based on gene expression similarity, but lack temporal ordering of gene changes.
Identifying the sequence of gene expression events is crucial for understanding dynamic biological processes.

Purpose of the Study:

To introduce GeneSwitches, a novel computational tool for ordering gene expression and functional events along pseudo-time trajectories.
To enable the precise determination of gene switching on/off dynamics in single-cell data.
To facilitate the investigation of dynamic changes in surface markers and functional ontologies.

Main Methods:

GeneSwitches employs a statistical framework utilizing logistic regression.
The tool analyzes single-cell pseudo-time trajectories to identify gene expression dynamics.
It determines the order of gene activation and inactivation along the inferred pseudo-time.

Main Results:

GeneSwitches accurately determines the precise order of gene expression changes over pseudo-time.
The tool allows for the identification of sequential appearance of surface markers.
It enables comparison of gene switching orders between different pseudo-temporal processes.

Conclusions:

GeneSwitches provides a robust method for analyzing dynamic gene expression patterns in single-cell data.
The tool enhances the interpretability of pseudo-time trajectories by ordering critical biological events.
GeneSwitches offers valuable insights into developmental trajectories and cellular transitions.