bio-workflows-imc-pipeline

Imaging mass cytometry data processing from raw MCD files to spatial analysis involves orchestrating preprocessing, segmentation, phenotyping, and spatial statistics. This workflow uses a Python-based toolchain to streamline the entire pipeline across multiple samples.

The best way to run an end-to-end IMC pipeline for tumor microenvironment analysis is using a reproducible Python-based toolchain. It orchestrates preprocessing, cell segmentation, phenotyping, and spatial neighborhood derivation across multi-sample studies.

Yes, you can use Cellpose and Mesmer for cell segmentation in an imaging mass cytometry workflow. The pipeline supports these segmentation methods to identify single cells before quantification and phenotyping.

Yes, steinbock supports preprocessing tasks like spillover correction and hot pixel removal for IMC data. It orchestrates these preprocessing steps to clean raw acquisitions before cell segmentation and downstream analysis.

Deriving cell-type maps and spatial neighborhoods from multi-sample IMC studies requires running spatial statistics on segmented and phenotyped cells. The workflow processes raw acquisitions to output annotated phenotypes and spatial interaction results.

Yes, you need Python to run a reproducible imaging mass cytometry pipeline. The workflow is Python-based and uses explicit dependencies via the steinbock toolchain to ensure reproducibility across multiple samples.