This entry provides access to processed CODEX data files of three studies analyzed in the article "Graph deep learning for the characterization of tumour microenvironments from spatial protein profiles in tissue specimens". Details of datasets can be found in the Methods section of the article. For each dataset: A comma-separated values (CSV) file containing metadata of regions is included A zip file containing multiple CSV files is included: `{region_id}.cell_data.csv`, a table containing three columns: "CELL_ID", "X", and "Y". This table provides centroid locations for all cells segmented in this region. `{region_id}.expression.csv`, a table containing multiple columns: "CELL_ID", "DAPI", "CD45", etc. This table provides detailed protein biomarker expression quantified and normalized for all cells in this region. `{region_id}.cell_types.csv`, a table containing two columns: "CELL_ID" and "CELL_TYPE". This table provides cell type annotations for all cells in this region. `{region_id}.cell_features.csv`, a table containing two columns: "CELL_ID" and "SIZE". This table provides morphology descriptors (only containing cell size for these studies) for all cells in this region. These data files are also available through the Enable Medicine Public Study page: https://app.enablemedicine.com/portal/atlas-library/studies/92394a9f-6b48-4897-87de-999614952d94?sid=1168. Raw multiplexed immunofluorescence images will be accessible through the visualizer app of Enable Medicine Portal. Codes for this study are stored in https://gitlab.com/enable-medicine-public/space-gm. Please direct all further questions and/or issues to the gitlab repository or lead contact (A.E.T.).