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Variability: Analysis

Variability: Analysis

Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...

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Updated: Jul 19, 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

sccomp: Robust differential composition and variability analysis for single-cell data.

Stefano Mangiola^1,2, Alexandra J Roth-Schulze^1,2, Marie Trussart¹

¹Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC 3052, Australia.

Proceedings of the National Academy of Sciences of the United States of America

|August 7, 2023

Summary

This summary is machine-generated.

A new statistical method, sccomp, analyzes cellular omics data for differential composition and variability. It improves upon existing methods by modeling data properties, aiding disease marker discovery in areas like breast cancer.

Keywords:

cell-type proportion compositional microbiome single-cell variability

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Area of Science:

Multi-omics data analysis
Computational biology
Statistical modeling

Background:

Cellular omics (genomics, proteomics, microbiomics) characterize tissue and microbial communities.
Comparing omics data between conditions identifies disease progression markers.
Existing statistical methods for omics data lack dedicated differential variability analysis and do not fully model compositional data properties.

Purpose of the Study:

To introduce sccomp, a novel statistical method for differential composition and variability analyses of cellular omics data.
To address limitations in existing methods by jointly modeling data count distribution, compositionality, group-specific variability, and mean-variability association.
To provide a comprehensive framework for realistic data simulation and cross-study knowledge transfer.

Main Methods:

Developed sccomp, a statistical method incorporating data count distribution, compositionality, group-specific variability, and mean-variability association.
Modeled outlier awareness within the analysis framework.
Validated sccomp's performance against state-of-the-art algorithms using experimental data.

Main Results:

Demonstrated the ubiquity of mean-variability association across technologies, challenging the adequacy of the Dirichlet-multinomial distribution.
Showcased sccomp's accurate fit to experimental data, outperforming existing methods.
Identified differential constraints and composition in the microenvironment of primary breast cancer using sccomp.

Conclusions:

sccomp offers a significant improvement for differential composition and variability analyses in cellular omics.
The method accurately models key data properties, leading to enhanced performance.
sccomp facilitates the discovery of biological drivers and disease markers, as exemplified by its application to breast cancer microenvironment analysis.