CAFE: An open-source web application platform for interactive analysis of spectral flow cytometry data 3081

Abstract Description Spectral flow cytometry (SFC) allows us to study cellular heterogeneity with greater detail by simultaneously measuring up to 50 markers. Analysis of such high-dimensional data presents substantial challenges in immunophenotyping since manual gating strategies rely on drilling down into populations using two markers at a time. Approaches have been developed to perform dimensionality reduction, unbiased clustering, and visualization of high-dimensional data inspired by single-cell RNA sequencing workflow; however, there is a scarcity of freely available graphical-user-interface based tools to analyze SFC data. To address this gap, we developed CAFE (Cell Analyzer for Flow Experiments), a Python-based web application platform for SFC data analysis that requires no coding expertise. Built with Streamlit, CAFE integrates Scanpy for general workflow, Pandas and pyArrow for efficient data processing, and Matplotlib, Seaborn, and Plotly for customizable visualization. CAFE provides a robust suite of tools, including density-based down-sampling, dimensionality reduction, batch correction, Leiden algorithm-based community detection, cluster merging and annotation, and advanced visualization capabilities. Users can interactively adjust parameters using dropdown menus, sliders, buttons, and tick boxes to generate publication-ready figures in real time. CAFE is open-source under the MIT license and freely available at https://github.com/mhbsiam/cafe. Funding Sources This work was supported by the National Institutes of Health/National Institute on Aging Grant R00 AG068309 (to D.J.T.); Nathan Shock Center, which is supported by the National Institutes of Health/National Institute on Aging Grant P30 AG050886; and by the Glenn Foundation for Medical Research and American Federation for Aging Research Junior Faculty Award A24063 (to D.J.T.). Topic Categories Computational and Systems Immunology (COMP)