Abstract
Automated flow cytometry (FC) data analysis tools for cell population identification and characterization are increasingly being used in academic, biotechnology, pharmaceutical, and clinical laboratories. The development of these computational methods is designed to overcome reproducibility and process bottleneck issues in manual gating, however, the take-up of these tools remains (anecdotally) low. Here, we performed a comprehensive literature survey of state-of-the-art computational tools typically published by research, clinical, and biomanufacturing laboratories for automated FC data analysis and identified popular tools based on literature citation counts. Dimensionality reduction methods ranked highly, such as generic t-distributed stochastic neighbor embedding (t-SNE) and its initial Matlab-based implementation for cytometry data viSNE. Software with graphical user interfaces also ranked highly, including PhenoGraph, SPADE1, FlowSOM, and Citrus, with unsupervised learning methods outnumbering supervised learning methods, and algorithm type popularity spread across K-Means, hierarchical, density-based, model-based, and other classes of clustering algorithms. Additionally, to illustrate the actual use typically within clinical spaces alongside frequent citations, a survey issued by UK NEQAS Leucocyte Immunophenotyping to identify software usage trends among clinical laboratories was completed. The survey revealed 53% of laboratories have not yet taken up automated cell population identification methods, though among those that have, Infinicyt software is the most frequently identified. Survey respondents considered data output quality to be the most important factor when using automated FC data analysis software, followed by software speed and level of technical support. This review found differences in software usage between biomedical institutions, with tools for discovery, data exploration, and visualization more popular in academia, whereas automated tools for specialized targeted analysis that apply supervised learning methods were more used in clinical settings.
Highlights
Flow cytometry (FC) is an important analytical technique for singlecell population identification and characterization
The environment in which users interact with the software range from basic command line inputs to full graphical user interfaces (GUI)
Flow cytometry has evolved to a stage where data analysis can be approached with unsupervised and supervised learning methods that automatically cluster cell populations and classify samples corresponding to clinical outcomes
Summary
Flow cytometry (FC) is an important analytical technique for singlecell population identification and characterization. It is widely used within biotechnology, pharmaceutical and clinical laboratories, and biomanufacturing spaces. Reproducibility and rigor in results are very important, driven by the needs of regulators around the world, a major source of variation in FC lies within data analysis [1]. The analysis is straightforward with three- to four-color immunofluorescence data but becomes significantly more complex when examining an increasing number of cellular markers, leading to increasing human operator variation and issues of reproducibility [2, 3]. Current state-of-the-art flow cytometers are capable of measuring over 40 parameters, generating challenging complex, and time-consuming multidimensional data sets for manual analysis [4,5,6]
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