Cell Type Diversity Statistic: An Entropy-Based Metric to Compare Overall Cell Type Composition Across Samples

Area of Science:

• Immunology
• Genomics
• Computational Biology

Background:

• Cell type composition changes are crucial for understanding health and disease.
• Single-cell transcriptomics offers high-resolution insights into cellular heterogeneity.
• Existing methods often analyze cell types independently, neglecting compositional constraints.

Purpose of the Study:

• To develop a novel metric for assessing overall cell type distribution in single-cell data.
• To enable comparative analysis of cell type composition across multiple samples and conditions.
• To investigate age-related compositional changes in peripheral blood mononuclear cells (PBMCs).

Main Methods:

• Development of a new metric to quantify cell type proportions within samples.
• Application of the metric to diverse single-cell transcriptomic datasets.
• Analysis of peripheral blood mononuclear cells (PBMCs) from individuals across the human lifespan.

Main Results:

• The developed metric effectively measures overall cell type composition at the single-cell level.
• Significant alterations in PBMC composition were identified in relation to aging.
• The study provides a new tool for analyzing compositional dynamics in biological samples.

Conclusions:

• The novel metric offers a robust approach to studying cell type distribution in single-cell studies.
• Aging is associated with distinct changes in PBMC composition.
• This work advances the understanding of cellular dynamics in aging and other biological processes.