# Comparison of Clustering Methods for High-Dimensional Single-Cell Flow and Mass Cytometry Data

## TL;DR

This paper compares different clustering methods for analyzing high-dimensional flow and mass cytometry data to help researchers choose the best approach for identifying cell populations.

## Contribution

The study provides an up-to-date, comprehensive comparison of clustering methods, including new ones, for high-dimensional cytometry data.

## Key findings

- FlowSOM performed well across all data sets and had fast runtimes, making it a recommended first choice for analysis.
- Several methods were sensitive to random starts when detecting rare cell populations, suggesting the need for multiple random starts in such cases.
- Reproducible R scripts and data files are provided to allow future comparisons with new methods and data sets.

## Abstract

Recent technological developments in high-dimensional flow cytometry and mass cytometry (CyTOF) have made it possible to detect expression levels of dozens of protein markers in thousands of cells per second, allowing cell populations to be characterized in unprecedented detail. Traditional data analysis by “manual gating” can be inefficient and unreliable in these high-dimensional settings, which has led to the development of a large number of automated analysis methods. Methods designed for unsupervised analysis use specialized clustering algorithms to detect and define cell populations for further downstream analysis. Here, we have performed an up-to-date, extensible performance comparison of clustering methods for high-dimensional flow and mass cytometry data. We evaluated methods using several publicly available data sets from experiments in immunology, containing both major and rare cell populations, with cell population identities from expert manual gating as the reference standard. Several methods performed well, including FlowSOM, X-shift, PhenoGraph, Rclusterpp, and flowMeans. Among these, FlowSOM had extremely fast runtimes, making this method well-suited for interactive, exploratory analysis of large, high-dimensional data sets on a standard laptop or desktop computer. These results extend previously published comparisons by focusing on high-dimensional data and including new methods developed for CyTOF data. R scripts to reproduce all analyses are available from GitHub (https://github.com/lmweber/cytometry-clustering-comparison), and pre-processed data files are available from FlowRepository (FR-FCM-ZZPH), allowing our comparisons to be extended to include new clustering methods and reference data sets.