A User-Friendly, Modality-Agnostic, Scalable Pipeline for High-Dimensional Immune Data Across Cytometry and Imaging Platforms 2310246

Abstract Introduction High-dimensional immune profiling technologies, including flow cytometry, mass cytometry, spatial proteomics, and fluorescence imaging, generate complex datasets that remain challenging to analyze in a unified and reproducible manner. Existing analytical approaches often require substantial computational expertise and are limited to specific platforms, creating barriers for broad adoption by wet-lab scientists and clinicians. There is a need for user-friendly, modality-agnostic pipelines that enable scalable analysis. Methods We developed a user-friendly, modality-agnostic pipeline for end-to-end analysis of high-dimensional immune data across cytometry and imaging platforms. The pipeline is built on robust and widely adopted R and Python packages and is delivered through an intuitive graphical user interface (GUI). The pipeline supports standard data formats, including FCS, MCD, and TIFF files The pipeline provides standardized, scalable workflows for data preprocessing, visualization, and downstream analysis, while remaining extensible for advanced users. Results To demonstrate functionality and versatility, the pipeline was applied to a practice dataset examining immune cell infiltrates in early colorectal cancer lesions. The pipeline enabled efficient preprocessing, visualization, and comparative analysis of high-dimensional immune profiles, illustrating its ability to handle complex cytometric and spatial data within a unified analytical framework. Conclusion Our GUI guided pipeline lowers technical barriers to high-dimensional immune data analysis by providing a scalable, user-friendly, and modality-agnostic framework. By enabling reproducible and interpretable analysis across cytometry and imaging platforms, our pipeline facilitates biological discovery and supports the broader adoption of advanced immune profiling technologies in translational and clinical research. Funding Source R01CA258882, HT94252510707 Topic Categories Computational and Systems Immunology (COMP)