Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.10.24.512549v1?rss=1

Authors: Lippeveld, M., Peralta, D., Filby, A., Saeys, Y.

Abstract: Morphologically profiling large-scale, single-cell bioimaging datasets poses a significant computational challenge. Here, we present Scalable Cytometry Image Processing (SCIP), a software package implemented in Python and aimed at running on high performance computing infrastructure. SCIP is scalable, flexible, open-source and enables reproducible image processing. It performs projection, illumination correction, segmentation, background masking and extensive feature extraction. We showcase SCIP's capabilities on three large, bioimaging datasets. First, we process an imaging flow cytometry (IFC) dataset of human white blood cells and show how the obtained features are used to classify the cells into 8 cell types based on bright- and darkfield imagery. Secondly, we process an automated microscopy dataset of human white blood cells to divide them into cell types in an unsupervised manner. Finally, a high-content screening dataset of breast cancer cells is processed to predict the mechanism-of-action of a large set of compound treatments. The software is available for install from the PyPi repository. The source code is available at https://github.com/ScalableCytometryImageProcessing/SCIP under the GNU General Public License version 3. It has been tested on Unix operating systems. Issues with the software can be submitted at https://github.com/ScalableCytometryImageProcessing/SCIP/issues.

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