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Related Experiment Video

Updated: Jun 21, 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

scMaSigPro: Differential Expression Analysis along Single-Cell Trajectories.

Priyansh Srivastava^1,2, Marta Benegas Coll¹, Stefan Götz¹

¹BioBam Bioinformatics S.L, Valencia, Spain.

Bioinformatics (Oxford, England)

|July 8, 2024

Summary

This summary is machine-generated.

scMaSigPro identifies differential gene expression along cell differentiation paths. This new method offers improved accuracy and computational efficiency for analyzing single-cell RNA sequencing data.

Keywords:

Differential Genes Pseudotime Software Time-Series Differential Expression Trajectory Branches

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Last Updated: Jun 21, 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

Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules

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Published on: September 5, 2019

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Published on: March 12, 2021

Area of Science:

Computational biology
Genomics
Single-cell analysis

Background:

Understanding cell differentiation requires analyzing gene expression dynamics.
Genes driving cell fate decisions show varied expression across pseudotime and branching trajectories.

Purpose of the Study:

Introduce scMaSigPro, a novel method for identifying differential gene expression patterns.
Simultaneously analyze gene expression along pseudotime and branching paths in single-cell data.

Main Methods:

scMaSigPro is an R package (version 4.0+).
Available on GitHub and Zenodo.

Main Results:

scMaSigPro was evaluated on synthetic and public datasets.
Outperforms existing methods in False Positive Rate control.
Demonstrates computational efficiency.

Conclusions:

scMaSigPro is an effective tool for analyzing single-cell gene expression.
Facilitates the study of cellular differentiation mechanisms.