openDVP tutorial dataset

📂 Data Contents

image/mIF.ome.tif

• A multiplex immunofluorescence (mIF) image in OME-TIFF format.

• Serves as the base image for segmentation and analysis.

segmentation/segmentation_mask.tif

• Segmentation mask labeling individual cells.

• Same dimensions as the input image.

• Generated using Cellpose nuclei model + 5px expansion.

manual_artefact_annotations/artefacts.geojson

• Hand-curated annotations of tissue and image artefacts.

• Useful for excluding artefactual regions from downstream quantification.

phenotyping/celltype_matrix.csv

• Matrix of celltype identities/phenotypes. Follows scimap phenotyping matrix scheme.

phenotyping/gates.csv

• Definitions of gating strategies used to assign cell types (e.g., thresholds per marker).

• Enables reproducible phenotyping logic.

quantification/quant.csv

• Spatially resolved quantification (e.g., mean marker intensities per cell).

proteomics/DIANN_pg_matrix.csv

• Protein group abundance matrix generated from DIA-MS using DIA-NN.

• Rows = proteins; columns = regions collected from tissue.

proteomics/DIANN_metadata.csv

• Sample metadata for the proteomics matrix, e.g., region name, acquisition info, batch.

proteomics/collection_shapes.geojson

• Spatial outlines of tissue regions collected for LC-MS (e.g., laser capture shapes).

• Enables mapping proteomics data back to tissue context.

🧪 Intended Use

This dataset is designed for testing openDVP functionalities.

• Developing and demonstrating spatial multi-omic integration pipelines.

• Quality control single-cell segmentation, artefact removal, and phenotyping methods.

• Linking spatial image data with region-specific proteomic measurements.