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Updated: Jul 18, 2025

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

Differential variability analysis of single-cell gene expression data.

Jiayi Liu^1,2,3, Anat Kreimer^2,3, Wei Vivian Li^4,5

¹Graduate Programs in Molecular Biosciences, Rutgers, The State University of New Jersey, 604 Allison Rd, Piscataway, 08854, NJ, USA.

Briefings in Bioinformatics

|August 20, 2023

Summary

This summary is machine-generated.

We developed statistical pipelines to analyze gene expression variability in single-cell RNA sequencing (scRNA-seq) data. The best pipeline uses simple normalization and identifies cellular variability changes in COVID-19 and autism patients.

Keywords:

data normalization differential variability analysis hypothesis testing single-cell genomics

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Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards

Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards

Published on: February 25, 2017

Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

Single-cell RNA sequencing (scRNA-seq) allows gene expression analysis at the individual cell level.
Understanding transcriptional variability is crucial for various biological states, but robust statistical methods are lacking.
Existing methods struggle to quantify and test differential variability between cell groups.

Purpose of the Study:

To identify optimal statistical pipelines for differential variability analysis in scRNA-seq data.
To compare various normalization, feature selection, dimensionality reduction, and variability calculation methods.
To establish best practices for analyzing transcriptional variability in single cells.

Main Methods:

Proposed and evaluated 12 distinct statistical pipelines for scRNA-seq data analysis.
Utilized synthetic scRNA-seq datasets for benchmarking pipeline performance and accuracy.
Employed denSNE-based distances to cluster medoids as the primary variability measure.

Main Results:

Identified a highly accurate and powerful pipeline involving simple library size normalization and retaining all genes.
The optimal pipeline uses denSNE-based distances for variability calculation.
Successfully applied the validated pipeline to real-world scRNA-seq datasets from COVID-19 and autism patients.

Conclusions:

The developed pipeline provides a robust method for quantifying differential gene expression variability in scRNA-seq data.
This approach successfully identified significant cellular variability changes in COVID-19 and autism patient cohorts.
Highlights the potential of analyzing transcriptional variability for understanding disease states and patient stratification.