Supplementary Information Files for Current trends in flow cytometry automated data analysis software

Supplementary Information Files for Current trends in flow cytometry automated data analysis software<br>Automated flow cytometry (FC) data analysis tools for cell population identification and characterisation are increasingly being used in academic, biotechnology, pharmaceutical and clinical laboratories. Development of these computational methods are 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 neighbour 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 amongst 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 visualisation more popular in academia, whereas automated tools for specialised targeted analysis that apply supervised learning methods were more used in clinical settings.