steinbock results of IMC example data

<p>If you are working with these files, please cite them as follows:<br><br>Windhager, J., Zanotelli, V.R.T., Schulz, D. et al. An end-to-end workflow for multiplexed image processing and analysis. Nat Protoc (2023). <a href="https://doi.org/10.1038/s41596-023-00881-0">https://doi.org/10.1038/s41596-023-00881-0</a></p><p>This repository hosts the results of processing example imaging mass cytometry (IMC) data hosted at&nbsp;<a href="https://zenodo.org/record/5949116">zenodo.org/record/5949116</a>&nbsp;using the <i>steinbock&nbsp;</i>framework available at&nbsp;<a href="https://github.com/BodenmillerGroup/steinbock">github.com/BodenmillerGroup/steinbock</a>. Please refer to <strong>steinbock.sh&nbsp;</strong>for how these data were generated from the raw data.</p><p>The following files are part of this repository:</p><ul><li><strong>panel.csv</strong>: contains channel information regarding the used antibodies in <i>steinbock</i> format</li><li><strong>img.zip</strong>: contains hot pixel filtered multi-channel images derived from the IMC raw data. One file per acquisition is generated</li><li><strong>images.csv</strong>: contains metadata per acquisition</li><li><strong>pixel_classifier.ilp</strong>: ilastik pixel classifier (same as the&nbsp; one in <a href="https://zenodo.org/record/6043544">zenodo.org/record/6043544</a>)</li><li><strong>ilastik_crops.zip</strong>: image crops on which the ilastik classifier was trained (same as the ones in <a href="https://zenodo.org/record/6043544">zenodo.org/record/6043544</a>)</li><li><strong>ilastik_img.zip</strong>: contains multi-channel images (one per acquisition) in .h5 format for ilastik pixel classification</li><li><strong>ilastik_probabilities.zip</strong>: 3 channel images containing the pixel probabilities after pixel classification</li><li><strong>masks_ilastik.zip</strong>: segmentation masks derived from the ilastik pixel probabilities using the <strong>cell_segmentation.cppipe</strong> pipeline</li><li><strong>masks_deepcell.zip</strong>: segmentation masks derived by <i>deepcell</i> segmentation</li><li><strong>intensities.zip</strong>: Contains one .csv file per acquisition. Each file contains single-cell measures of the mean pixel intensity per cell and channel based on the files in <strong>img.zip </strong>and <strong>masks_deepcell.zip</strong>.</li><li><strong>regionprops.zip</strong>: Contains one .csv file per acquisition. Each file contains single-cell measures of the morphological features and location of cells based on&nbsp;<strong>masks_deepcell.zip</strong>.</li><li><strong>neighbors.zip</strong>: Contains one .csv file per acquisition. Each file contains an edge list of cell IDs indicating cells in close proximity based on&nbsp;<strong>masks_deepcell.zip</strong>.</li><li><strong>ome.zip</strong>: contains .ome.tiff files derived from img.zip; one file per acquisition</li><li><strong>histocat.zip</strong>: contains single-channel .tiff files with segmentation masks derived from <strong>masks_deepcell.zip</strong> for upload to histoCAT (<a href="https://bodenmillergroup.github.io/histoCAT/">bodenmillergroup.github.io/histoCAT</a>)</li><li><strong>cells.csv</strong>: contains intensity and regionprop measurements of all cells</li><li><strong>cells_csv.zip</strong>: contains intensity and regionprop measurements of all cells per acquisition</li><li><strong>cells.fcs</strong>:&nbsp;contains intensity and regionprop measurements of all cells in fcs format</li><li><strong>cells_fcs.zip</strong>:&nbsp;contains intensity and regionprop measurements of all cells per acquisition in fcs format</li><li><strong>cells.h5ad</strong>: contains intensity, regionprop and neighbor measurements of all cells in <i>anndata </i>format</li><li><strong>cells_h5ad</strong>:&nbsp;contains intensity&nbsp;regionprop and neighbor measurements of all cells per acquisition in&nbsp;<i>anndata </i>format</li><li><strong>graphs.zip</strong>: contains spatial object graphs in .graphml format; one file per acquisition</li></ul>