IMC Segmentation Pipeline results of example IMC data

Description

This repository hosts the results of processing example imaging mass cytometry (IMC) data hosted at 10.5281/zenodo.5949116 using the IMC Segmentation Pipeline available at https://github.com/BodenmillerGroup/ImcSegmentationPipeline (DOI: 10.5281/zenodo.6402666). Please refer to https://github.com/BodenmillerGroup/steinbock as alternative processing framework and 10.5281/zenodo.6043600 for the data generated by steinbock. The following files are part of the analysis.zip folder when running the IMC Segmentation Pipeline: cpinp: contains input files for the segmentation pipeline cpout: contains all final output files of the pipeline: cell.csv containing the single-cell features; Experiment.csv containing CellProfiler metadata; Image.csv containing acquisition metadata; Object relationships.csv containing an edge list indicating interacting cells; panel.csv containing channel information; var_cell.csv containing cell feature information; var_Image.csv containing acquisition feature information; images containing the hot pixel filtered multi-channel images and the channel order; masks containing the segmentation masks; probabilities containing the pixel probabilities. histocat: contains single channel .tiff files per acquisition for upload to histoCAT (https://bodenmillergroup.github.io/histoCAT/) crops: contains upscaled image crops in .h5 format for ilastik (https://www.ilastik.org/) training  ometiff: contains .ome.tiff files per acquisition, .png files per panorama and additional metadata files per slide ilastik: multi channel images for ilastik pixel classification (_ilastik.full) and their channel order (_ilastik.csv); upscaled multi channel images for ilastik pixel prediction (_ilastik_s2.h5); upscaled 3 channel images containing ilastik pixel probabilities (_ilastik_s2_Probabilities.tiff). The remaining files are part of the root directory: docs.zip: Documentation of the pipeline in markdown format IMCWorkflow.ilp: Ilastik pixel classifier pre-trained on the example data resources.zip: The CellProfiler pipelines and CellProfiler plugins used for the analysis sample_metadata.xlsx: Metadata per sample including the cancer type scripts.zip: Python notebooks used for pre-processing and downloading the example data src.zip: Scripts for the imcsegpipe python package

Resources

Name Format Description Link
0 http://data.europa.eu/88u/dataset/oai-zenodo-org-6404960
0 http://data.europa.eu/88u/dataset/oai-zenodo-org-6404960
0 http://data.europa.eu/88u/dataset/oai-zenodo-org-6404960
0 http://data.europa.eu/88u/dataset/oai-zenodo-org-6404960
0 http://data.europa.eu/88u/dataset/oai-zenodo-org-6404960
0 http://data.europa.eu/88u/dataset/oai-zenodo-org-6404960
0 http://data.europa.eu/88u/dataset/oai-zenodo-org-6404960
0 http://data.europa.eu/88u/dataset/oai-zenodo-org-6404960
0 http://data.europa.eu/88u/dataset/oai-zenodo-org-6404960
0 http://data.europa.eu/88u/dataset/oai-zenodo-org-6404960
0 http://data.europa.eu/88u/dataset/oai-zenodo-org-6404960
0 http://data.europa.eu/88u/dataset/oai-zenodo-org-6404960
0 http://data.europa.eu/88u/dataset/oai-zenodo-org-6404960
0 http://data.europa.eu/88u/dataset/oai-zenodo-org-6404960
0 http://data.europa.eu/88u/dataset/oai-zenodo-org-6404960
0 http://data.europa.eu/88u/dataset/oai-zenodo-org-6404960

Tags

  • cellprofiler
  • imaging-mass-cytometry
  • image-analysis

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