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

By Lukas M. Weber, Mark D. Robinson. 2016

362 citations recorded by the source corpus.

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.

Subfields: Biophysics, Molecular Biology

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Citations in the corpus

Stereotypic Immune System Development in Newborn Children (2018, methodology): “From 202 blood samples (parents excluded), we integrated 48 non-negative immune cell population frequencies identified by an unsupervised clustering algorithm (Weber and Robinson, 2016) (STAR Methods), and 250 plasma proteins with variable concentrations (STAR Methods).”
Automated Optimal Parameters for T-Distributed Stochastic Neighbor Embedding Improve Visualization and Allow Analysis of Large Datasets (2018, methodology): “Louvain cluster classification was adopted from the SCANPY data analysis study 17 The quality of the embeddings was assessed by a nearest neighbor (NN) classifier strategy as described in previous reports on t-SNE accuracy evaluation 1,2,10 .”
SOM-based embedding improves efficiency of high-dimensional cytometry data analysis (2019, background): “at is both more natural to scientists who are used to the prevalent dot-plot view, and less prone to various statistical and bias-derived errors, since human choice is restricted to a discrete and reproducible selection 185 of FlowSOM clusters that have previously been proven to capture the respect”
Current best practices in single‐cell RNA‐seq analysis: a tutorial (2019, methodology): “It has been shown to outperform other clustering methods for single-cell RNA-seq data (Duo` et al , 2018; Freytag et al , 2018), and flow and mass cytometry data (Weber & Robinson, 2016).”