Abstract 1910: flowTOTAL: A comprehensive bioinformatics workflow for flow cytometry automatic analysis

Flow cytometry is a technique for analyzing cells that are suspended in a buffered salt-based solution and flow past one or more lasers. Visible light scatter and one or more fluorescence characteristics are assessed for each particle. The most common use of flow cytometry is immunophenotyping. In traditional flow cytometry analysis, a region around a population of cells is manually drawn (gating) in two-dimensional scatter plots. This enables the measuring of specific groups of cells. However, manual gating could increase the overall error rate of the study and makes the analysis hardly reproducible since it is done in less controlled settings. Furthermore, it represents a bottleneck in the analysis of large amounts of data. Some processes, such as gating, have recently been automated using R packages. In addition, dimensionality reduction approaches have been developed in the flow cytometry environment to take advantage of information from several markers at once. Despite this, none of them are integrated in a harmonized way, and none of them allow back-gating to emphasize the study population and eliminate false positives. To tackle this problem, our team has developed flowTOTAL (github.com/ImmunoOncology/flowTOTAL), a user-friendly command line workflow to analyze flow cytometry data. The major attractive feature is the facility to perform with one command not only a traditional analysis, but also an unsupervised analysis. As input, the user has to indicate the folder with the .FCS files, the metadata associated with each file, and the marker to be used during back-gating. The pipeline is divided into three main sections: preprocessing, traditional analysis, and unsupervised analysis. During preprocessing each. FCS will be subjected to correcting for fluorescence spillover (compensation), detection of anomalies by checking flow rate and signal acquisition as well as removing doublets based on forward scatter (QC). For the traditional analysis, auto-gating will be performed for the identification of the target population using back-gating. For each set of given markers, the number of events obtained and the scatter plot will be generated. Finally, in the unsupervised analysis, the population of interest will be specified and the pipeline will proceed with normalization, dimensionality reduction using PCA or UMAP and finally a clustering approach for subpopulation identification. In addition, differential abundance analysis can be performed with metadata