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Single-cell Gene Expression Using Multiplex RT-qPCR to Characterize Heterogeneity of Rare Lymphoid Populations

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Published on: January 19, 2017

Measuring cell-to-cell expression variability in single-cell RNA-sequencing data: a comparative analysis and

Huiwen Zheng¹, Jan Vijg^2,3, Atefeh Taherian Fard⁴

¹Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, QLD, Australia.

|October 20, 2023

Summary

This study evaluates statistical methods for measuring cell-to-cell gene expression variability using single-cell RNA sequencing (scRNA-seq) data. The scran metric demonstrated superior performance, revealing key gene signatures during B cell differentiation and aging.

Keywords:

Aging B lymphocytes differentiation Cell-to-cell variability Evaluation framework Single-cell RNA-seq

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Last Updated: Jun 9, 2026

Single-cell Gene Expression Using Multiplex RT-qPCR to Characterize Heterogeneity of Rare Lymphoid Populations

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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

Area of Science:

Genomics
Computational Biology
Molecular Biology

Background:

Single-cell RNA sequencing (scRNA-seq) captures gene expression heterogeneity.
Quantifying cell-to-cell variability is crucial but challenging due to scRNA-seq data structures like zero inflation.

Purpose of the Study:

To systematically evaluate 14 different metrics for measuring cell-to-cell variability in transcriptomic data.
To identify the optimal statistical approach for analyzing scRNA-seq data, considering data-specific features and biological properties.

Main Methods:

Systematic evaluation of 14 variability metrics using simulations and real datasets.
Benchmarking metric performance against data features (sparsity, platform) and biological variability.
Application of the top-performing metric (scran) to analyze B cell differentiation and aging.

Main Results:

The scran metric showed the strongest all-round performance in quantifying cell-to-cell variability.
Analysis revealed unique gene signatures with distinct expression profiles during B cell differentiation.
Differentially variable genes between young and old cells identified regulatory changes potentially missed by mean expression analysis.

Conclusions:

Capturing cell-to-cell gene expression variability is vital for understanding complex biological processes like differentiation and aging.
The findings emphasize the value of these methods for analyzing individual cell types and uncovering regulatory insights.