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Shetab Boushehri, S. ; Kornivetc, A. ; Winter, D. ; Kazeminia, S. ; Essig, K.* ; Schmich, F.* ; Marr, C.

PXPermute reveals staining importance in multichannel imaging flow cytometry.

Cell Rep. Methods 4:100715 (2024)
DOI PMC
Creative Commons Lizenzvertrag
Imaging flow cytometry (IFC) allows rapid acquisition of numerous single-cell images per second, capturing information from multiple fluorescent channels. However, the traditional process of staining cells with fluorescently labeled conjugated antibodies for IFC analysis is time consuming, expensive, and potentially harmful to cell viability. To streamline experimental workflows and reduce costs, it is crucial to identify the most relevant channels for downstream analysis. In this study, we introduce PXPermute, a user-friendly and powerful method for assessing the significance of IFC channels, particularly for cell profiling. Our approach evaluates channel importance by permuting pixel values within each channel and analyzing the resulting impact on machine learning or deep learning models. Through rigorous evaluation of three multichannel IFC image datasets, we demonstrate PXPermute's potential in accurately identifying the most informative channels, aligning with established biological knowledge. PXPermute can assist biologists with systematic channel analysis, experimental design optimization, and biomarker identification.
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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Cp: Imaging ; Cp: Systems Biology ; Cell Profiling ; Channel Importance ; Computer Vision ; Deep Learning ; Image Flow Cytometry ; Interpretable Artificial Intelligence ; Machine Learning ; Staining Importance
ISSN (print) / ISBN 2667-2375
e-ISSN 2667-2375
Journal Cell Reports Methods
Quellenangaben Volume: 4, Issue: 2, Pages: , Article Number: 100715 Supplement: ,
Publisher Elsevier
Publishing Place 50 Hampshire St, Floor 5, Cambridge, Ma 02139 Usa
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of AI for Health (AIH)
Helmholtz Artifical Intelligence Cooperation Unit (HAICU)
Institute of Computational Biology (ICB)
Grants Hightech Agenda Bayern
Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)
European Research Council (ERC) under the European Union
Helmholtz Association
F. Hoffmann-la Roche Ltd.