PhenoComb: a discovery tool to assess complex phenotypes in high-dimensional single-cell datasets

Paulo E P Burke; Ann Strange; Emily Monk; Brian Thompson; Carol M Amato; David M Woods

doi:10.1093/bioadv/vbac052

PhenoComb: a discovery tool to assess complex phenotypes in high-dimensional single-cell datasets

Bioinform Adv. 2022 Aug 3;2(1):vbac052. doi: 10.1093/bioadv/vbac052. eCollection 2022.

Authors

Paulo E P Burke, Ann Strange, Emily Monk, Brian Thompson, Carol M Amato, David M Woods¹

Affiliation

¹ Division of Medical Oncology, Department of Medicine, University of Colorado School of Medicine, Aurora, CO 80045, USA.

Abstract

Motivation: High-dimensional cytometry assays can simultaneously measure dozens of markers, enabling the investigation of complex phenotypes. However, as manual gating relies on previous biological knowledge, few marker combinations are often assessed. This results in complex phenotypes with the potential for biological relevance being overlooked. Here, we present PhenoComb, an R package that allows agnostic exploration of phenotypes by assessing all combinations of markers. PhenoComb uses signal intensity thresholds to assign markers to discrete states (e.g. negative, low, high) and then counts the number of cells per sample from all possible marker combinations in a memory-safe manner. Time and disk space are the only constraints on the number of markers evaluated. PhenoComb also provides several approaches to perform statistical comparisons, evaluate the relevance of phenotypes and assess the independence of identified phenotypes. PhenoComb allows users to guide analysis by adjusting several function arguments, such as identifying parent populations of interest, filtering of low-frequency populations and defining a maximum complexity of phenotypes to evaluate. We have designed PhenoComb to be compatible with a local computer or server-based use.

Results: In testing of PhenoComb's performance on synthetic datasets, computation on 16 markers was completed in the scale of minutes and up to 26 markers in hours. We applied PhenoComb to two publicly available datasets: an HIV flow cytometry dataset (12 markers and 421 samples) and the COVIDome CyTOF dataset (40 markers and 99 samples). In the HIV dataset, PhenoComb identified immune phenotypes associated with HIV seroconversion, including those highlighted in the original publication. In the COVID dataset, we identified several immune phenotypes with altered frequencies in infected individuals relative to healthy individuals. Collectively, PhenoComb represents a powerful discovery tool for agnostically assessing high-dimensional single-cell data.

Availability and implementation: The PhenoComb R package can be downloaded from https://github.com/SciOmicsLab/PhenoComb.

Supplementary information: Supplementary data are available at Bioinformatics Advances online.