Published November 4, 2019
| Version v1
Dataset
Open
The Single-Cell Pathology Landscape of Breast Cancer
Creators
- 1. Department of Quantitative Biomedicine, University of Zurich, Switzerland
- 2. Department of Quantitative Biomedicine, University of Zurich, Switzerland; Life Science Zurich Graduate School, ETH Zurich and University of Zurich, Switzerland
- 3. Department of Quantitative Biomedicine, University of Zurich, Switzerland; CRUK Cambridge Institute, University of Cambridge, Cambridge, UK
- 4. Department of Surgery, University of Basel and University Hospital Basel, Switzerland
- 5. Visceral Surgery Research Laboratory, Clarunis, Department of Biomedicine, University of Basel, Basel, Switzerland; Department of Surgery, Clarunis University Center for Gastrointestinal and Liver Diseases Basel, Basel, Switzerland
- 6. Institute of Pathology and Molecular Pathology, University Hospital Zurich, Zurich, Switzerland
- 7. Institute of Pathology and Genetics, University Hospital Basel, Switzerland
Description
All data supporting the findings of the publication "The Single-Cell Pathology Landscape of Breast Cancer", including high-dimension tiff images, single-cell and tumor/stroma masks, single-cell and patient data. The code used to produce the results of this study is available at https://github.com/BodenmillerGroup/SCPathology_publication.
OMEandSingleCellMasks.zip contains the ome-tiff stacks and the single-cell masks.
TumorStroma_masks.zip contains masks for tumor and stromal regions.
SingleCell_and_Metadata.zip contains the single-cell and patient data as well as all other input data for the R pipelines on the linked Github repository.
Important notes when working with the data:
- The single-cell data provided for downstream R analysis is already spillover corrected.
- The single-cell masks that were generated using CellProfiler do not always contain strictly sequential single-cell labels. Every now and then an ID is skipped due to excluded edge cells. This can cause issues in histoCAT and therefore the single cells are automatically relabelled sequentially during loading into histoCAT. We exported the single-cell data from histoCAT for downstream R analysis and therefore the single-cell labels are the newly assigned sequential ones and match the labels in the histoCAT sessions. However, the original mask files that are also provided here still contain the original labels from CellProfiler. For matching the single-cell data provided here directly to the masks (e.g. for visualization of single-cell features on the image outside of histoCAT), the single-cell labels in the mask need to be relabelled as well or matched based on the rank.