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CAFE: an integrated web app for high-dimensional analysis and visualization in spectral flow cytometry
Md Hasanul Banna Siam1, Md Akkas Ali1, Donald Vardaman1
1Department of Pathology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35205, United States.
Motivation:
Spectral flow cytometry provides greater insights into cellular heterogeneity by simultaneous measurement of up to 50 markers. However, analysing such high-dimensional (HD) data is complex through traditional manual gating strategy. To address this gap, we developed CAFEs (Cell Analyzer for Flow Experiments) as an open-source Python-based web application with a graphical user interface. Built with Streamlit, CAFE incorporates libraries such as Scanpy for single-cell analysis, Pandas and PyArrow for efficient data handling, and Matplotlib, Seaborn, Plotly for creating customizable figures. Its robust toolset includes density-based downsampling, dimensionality reduction, batch correction, Leiden-based clustering, cluster merging, and annotation.
Results:
Using CAFE, we demonstrated analysis of a human PBMC dataset of 350 000 cells identifying 16 distinct cell clusters. CAFE can generate publication-ready figures in real time via interactive slider controls and dropdown menus, eliminating the need for coding expertise and making HD data analysis accessible to all.
Availability And Implementation:
CAFE is licensed under MIT and is freely available at https://github.com/mhbsiam/cafe.
