Computational Cytometry Session
Chair: Bastian Höchst, München
Recent advancements in high-dimensional cytometry have revolutionised our understanding of cellular heterogeneity, particularly within complex immune landscapes. However, the exponentialincrease in data dimensionality presents significantchallenges for traditional manual gating strategies, which are prone to bias and lack scalability. To address this, novel computational frameworks integrating machine learning and automated clustering algorithms are essential to extractrobust, unbiased biological insights. This session highlights the development and application of cutting-edge bioinformatics tools designed to streamline data preprocessing, batch-effectcorrection, and single-cell phenotyping.
cyCONDOR: end-to-end solution for high-dimensional cytometry data analysis
Dr. rer. nat. Lorenzo Bonaguro
Group Leader Molecular and Translational ImmunomicsGerman Center for Neurodegenerative Diseases (DZNE), Bonn& University Hospital Bonn | Institute for Clinical Chemistry and Clinical Pharmacology
cyCONDOR provides a unified and open-source framework for the analysis of data from high-dimensional flow cytometry, spectral flow cytometry, mass cytometry (CyTOF), and proteogenomics (CITE-seq/Ab-seq) technologies. It accepts commonly used input formats (e.g. FCS files, including those exported from FlowJo) and emphasizes reproducibility, interpretability, and ease of use, making advanced cytometry analysis accessible not only to computational experts but also to wet-lab scientists with little or no prior bioinformatics experience.