PXPermute: Unveiling staining importance in multichannel fluorescence microscopy

Abstract Imaging Flow Cytometry (IFC) enables rapid acquisition of thousands of single-cell images per second, capturing information from multiple fluorescent channels. However, the conventional process of staining cells with fluorescently labeled conjugated antibodies for IFC analysis is labor-intensive, costly, and potentially detrimental to cell viability. To streamline experimental workflows and reduce expenses, it is imperative to identify the most relevant channels for downstream analysis. In this study, we present PXPermute, a user-friendly and powerful method that assesses the significance of IFC channels for a given task, such as cell profiling. Our approach evaluates channel importance by permuting pixel values within each channel and analyzing the resulting impact on the performance of machine learning or deep learning models. Through rigorous evaluation on three multi-channel IFC image datasets, we demonstrate the superiority of PXPermute in accurately identifying the most informative channels, aligning with established biological knowledge. To facilitate systematic investigations of channel importance and aid biologists in optimizing their experimental designs, we have released PXPermute as an easy-to-use open-source Python package.