SCIP: A scalable, reproducible, and open‐source pipeline for morphological profiling image cytometry and microscopy data

Imaging flow cytometry (IFC) provides single‐cell imaging data at a high acquisition rate. It is increasingly used in image‐based profiling experiments consisting of hundreds of thousands of multi‐channel images of cells. Currently available software solutions for processing microscopy data can provide good results in downstream analysis, but are limited in efficiency and scalability, and often ill‐adapted to IFC data. In this work, we propose Scalable Cytometry Image Processing (SCIP), a Python software that efficiently processes images from IFC and standard microscopy datasets. We also propose a file format for efficiently storing IFC data. We showcase our contributions on two large‐scale microscopy and one IFC datasets, all of which are publicly available. Our results show that SCIP can extract the same kind of information as other tools, in a much shorter time and in a more scalable manner.