ULV: A robust statistical method for clustered data, with applications to multisubject, single-cell omics data

TL;DR

ULV is a robust statistical method designed for clustered single-cell data, effectively handling challenges like small sample sizes, non-normality, and outliers, demonstrated through applications to AML proteomics and COVID-19 transcriptomics.

Contribution

We introduce ULV, a novel rank-based statistical framework that improves detection of differential expression in complex single-cell datasets with small samples and multiple covariates.

Findings

ULV identified differential proteins missed by traditional methods in AML data.

ULV uncovered genes associated with covariates in COVID-19 data.

ULV revealed gene pathways related to COVID-19 severity.

Abstract

Molecular and genomic technological advancements have greatly enhanced our understanding of biological processes by allowing us to quantify key biological variables such as gene expression, protein levels, and microbiome compositions. These breakthroughs have enabled us to achieve increasingly higher levels of resolution in our measurements, exemplified by our ability to comprehensively profile biological information at the single-cell level. However, the analysis of such data faces several critical challenges: limited number of individuals, non-normality, potential dropouts, outliers, and repeated measurements from the same individual. In this article, we propose a novel method, which we call U-statistic based latent variable (ULV). Our proposed method takes advantage of the robustness of rank-based statistics and exploits the statistical efficiency of parametric methods for small sample sizes. It is a computationally feasible framework that addresses all the issues mentioned above simultaneously. An additional advantage of ULV is its flexibility in modeling various types of single-cell data, including both RNA and protein abundance. The usefulness of our method is demonstrated in two studies: a single-cell proteomics study of acute myelogenous leukemia (AML) and a single-cell RNA study of COVID-19 symptoms. In the AML study, ULV successfully identified differentially expressed proteins that would have been missed by the pseudobulk version of the Wilcoxon rank-sum test. In the COVID-19 study, ULV identified genes associated with covariates such as age and gender, and genes that would be missed without adjusting for covariates. The differentially expressed genes identified by our method are less biased toward genes with high expression levels. Furthermore, ULV identified additional gene pathways likely contributing to the mechanisms of COVID-19 severity.