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

## TL;DR

This paper introduces voomCLR, a new method for analyzing changes in cell composition in single-cell studies, accounting for data uncertainty and variability.

## Contribution

The novelty lies in incorporating uncertainty in bias estimation and the mean–variance structure of data into the analysis of cell composition.

## Key findings

- voomCLR outperforms existing methods in simulated and real datasets.
- The method effectively handles the compositional nature of cell count data.
- Incorporating uncertainty improves the reliability of cell composition analysis.

## Abstract

In single-cell studies, a common question is whether there is a change in cell composition between conditions. While ideally, one needs absolute cell counts (number of cells per volumetric unit in a sample) to address these questions, current experimentation typically obtains cell counts that only carry relative information. Therefore, one should account for the compositional nature of cell count data in the statistical analysis. While recently developed methods address compositionality using compositional transformations together with a bias correction, they do not account for the uncertainty involved in estimation of the bias term, nor do they accommodate the mean–variance structure of the counts.

Here, we introduce a statistical method, voomCLR, for assessing differences in cell composition between conditions incorporating both uncertainty on the bias term as well as acknowledging the mean–variance structure of the transformed data, by leveraging developments from the differential gene expression literature. We demonstrate the performances of voomCLR, illustrate the benefit of all components, and compare the methodology to the state-of-the-art on simulated and real single-cell gene expression datasets.

voomCLR software is available as an open-source R package on GitHub at https://github.com/johnsonandjohnson/voomCLR.