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Updated: Jan 10, 2026

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

Assessing differential cell composition in single-cell studies using voomCLR.

Alemu Takele Assefa¹, Bie Verbist¹, Koen Van den Berge¹

¹Statistics and Decision Sciences, Johnson & Johnson, Beerse 2340, Belgium.

Bioinformatics (Oxford, England)

|November 23, 2025

Summary

This summary is machine-generated.

This study introduces voomCLR, a new statistical method for analyzing cell composition changes in single-cell studies. It accurately accounts for relative cell counts and improves upon existing methods by incorporating bias uncertainty and mean-variance structures.

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Area of Science:

Single-cell biology
Computational biology
Statistical genetics

Background:

Single-cell studies often require assessing changes in cell composition between experimental conditions.
Current cell count data is relative, necessitating specialized statistical approaches to handle compositionality.
Existing methods for compositional data analysis may not fully address bias uncertainty or the mean-variance structure of counts.

Purpose of the Study:

To introduce voomCLR, a novel statistical method for analyzing cell composition differences in single-cell data.
To address limitations of current methods by incorporating bias term uncertainty and mean-variance structures.
To provide a robust tool for differential cell composition analysis.

Main Methods:

Developed voomCLR, a statistical method leveraging developments from differential gene expression analysis.
Incorporated uncertainty estimation for the bias term in compositional data analysis.
Accounted for the mean-variance structure of transformed count data.

Main Results:

voomCLR demonstrates effective assessment of cell composition differences between conditions.
The method successfully incorporates bias term uncertainty and mean-variance structures.
Performance comparisons on simulated and real datasets show voomCLR's advantages over state-of-the-art methods.

Conclusions:

voomCLR offers an improved statistical framework for analyzing cell composition in single-cell studies.
The method provides a more accurate and comprehensive approach to differential cell composition analysis.
The open-source R package facilitates the application of voomCLR in biological research.