Has this cell type annotation worked?

Present dataset is the result of applying reference-based RCTD cell typing method on the 8um bin of the public P2 human CRC dataset as downloaded from 10x website. Public single-cell HTAN CRC data (validation cohort) from CellXGene was used as a reference to assign cell labels. The choice of CRC for this illustration is purely random. The dataset structure is as follows: visiumhd-crc-p2 adata visium-hd-crc-p2 square_008um.h5ad atlas 811ac326-d5a0-468e-9306-2a6f9874df0b.h5ad visium-hd-crc-p2 adata_matched.h5ad example.ipynb pipeline_info versions.yml squidpy visium-hd-crc-p2 figures centrality_scores.png co_occurrence_abnormal cell.png co_occurrence_goblet cell.png co_occurrence_gut absorptive cell.png co_occurrence_intestinal crypt stem cell of colon.png co_occurrence_intestinal enteroendocrine cell.png co_occurrence_intestinal epithelial cell.png co_occurrence_neoplastic cell.png co_occurrence_transit amplifying cell.png co_occurrence_tuft cell.png interaction_matrix.png nhood_enrichment.png spatial_scatter.png spatially_variable_genes.csv squidpy.h5ad Interested parties are invited to look at `squidpy/visium-hd-crc-p2/squidpy.h5ad`, it contains the original counts matrix in `adata.X`, associated spatial information, as well as most abundant cell type in `adata.obs['cell_type']` and raw RCTD outputs in `adata.uns['cell_types']`. Furthermore, `example.ipynb` contains some toy scripts to get started with analysis. Other files are untransformed and transformed versions of source data and some basic squidpy plots. Those feeling more comfortable with R fill find `crc-p2-visiumHD.h5seurat` useful, along with `crc-p2-rctd-cell_types.csv.zip` that contains cell type predictions.