| # AIDO.Tissue Dataset Collection | |
| ## niche type classification | |
| Niche is the microenvironment in which each cell exists and is able to keep its own peculiar characteristics ([Giacomo Donati, 2015](https://onlinelibrary.wiley.com/doi/10.1038/icb.2015.107)). Based on spatial transcriptomic data, one can annotate niche label with established tools, which integrates similarity in gene expression profiles, spatial neighborhood structures and histological information in the tissue. | |
| The task is to predict niche type of each cell given spatial expression data (in total 6 types). The raw dataset is from [human liver sample](https://nanostring.com/products/cosmx-spatial-molecular-imager/ffpe-dataset/human-liver-rna-ffpe-dataset), including a healthy sample slide. We collected the data and randomly split the total set into train, valid and split. | |
| Each `.h5ad` file contains spatial coordinate information (`x`, `y`) and niche type (`niche_label`). The obs `niche` is exact name and `niche_label` is corresponding name index (this is input label column for running modelgenerator). | |
| ```bash | |
| >>> import anndata as ad | |
| >>> file = 'niche_type_classification/cosmx_liver_for_celltype_niche.test.h5ad' | |
| >>> adata = ad.read_h5ad(file) | |
| >>> adata | |
| AnnData object with n_obs × n_vars = 34573 × 19264 | |
| obs: 'cellType', 'niche', 'split', 'x', 'y', 'cellType_label', 'niche_label' | |
| >>> adata.obs | |
| cellType niche split x y cellType_label niche_label | |
| obs_id | |
| c_1_103_1 Hep.5 Zone_2b test 10.25828 9.73440 1 0 | |
| c_1_103_10 Hep.6 Zone_3a test 10.61444 9.73356 7 2 | |
| c_1_103_100 Hep.1 Zone_3a test 10.57556 9.67620 3 2 | |
| c_1_103_1000 Hep.5 Zone_2a test 10.64528 9.24828 1 1 | |
| c_1_103_1001 Hep.4 Zone_2b test 10.70828 9.24180 0 0 | |
| ... ... ... ... ... ... ... ... | |
| c_1_99_995 Hep.5 Zone_2a test 8.24356 9.31284 1 1 | |
| c_1_99_996 Hep.5 Zone_2b test 8.28724 9.31428 1 0 | |
| c_1_99_997 Hep.4 Zone_2a test 8.41696 9.31512 0 1 | |
| c_1_99_998 Hep.4 Zone_2b test 8.57044 9.31524 0 0 | |
| c_1_99_999 Inflammatory.macrophages Zone_2a test 8.25976 9.31596 9 1 | |
| [34573 rows x 7 columns] | |
| ``` | |
| ## cell density | |
| The task is to predict neighbor cell number of a target cell given the expression profiles. Neighbor cell number is counted within a specific radius. Basically, cell density distribution shows the pattern to distinguish different cell states/conditions. For instance, tumor tissue is much more aggregated than healthy tissue, thus cancer samples are more densed than normal samples. | |
| The raw dataset is from [human liver sample](https://nanostring.com/products/cosmx-spatial-molecular-imager/ffpe-dataset/human-liver-rna-ffpe-dataset), including a healthy and tumor sample slide. We collected the data and curated ground truth neighbor number as in [Schaar et al.](https://www.biorxiv.org/content/10.1101/2024.04.15.589472v2). Then we randomly split the total set into train, valid and split. | |
| Each `.h5ad` file contains spatial coordinate information (`x`, `y`) and density value (`density`). | |
| ```bash | |
| >>> file = 'cell_density/xenium_lung_for_density.test.h5ad' | |
| >>> adata = ad.read_h5ad(file) | |
| >>> adata | |
| AnnData object with n_obs × n_vars = 124058 × 19264 | |
| obs: 'density', 'split', 'x', 'y' | |
| >>> adata.obs | |
| density split x y | |
| cell_id | |
| aaaaaahk-1-0 7.0 test 497.832559 855.178702 | |
| aaaandcd-1-0 21.0 test 1732.162622 856.926639 | |
| aaabfmfn-1-0 21.0 test 1718.817560 853.211935 | |
| aaabmojc-1-0 24.0 test 1724.924030 860.927252 | |
| aaadaeog-1-0 19.0 test 2248.489685 862.145029 | |
| ... ... ... ... ... | |
| oilcikef-1-1 4.0 test 10815.658984 8476.788525 | |
| oilfbafk-1-1 5.0 test 11116.310791 8524.649170 | |
| oilfpbmk-1-1 6.0 test 11128.255615 8538.087305 | |
| oilgfkkb-1-1 2.0 test 11051.862695 8556.525830 | |
| oilgkofb-1-1 4.0 test 11085.344727 8522.173047 | |
| [124058 rows x 4 columns] | |
| ``` | |
| ## other files | |
| `scRNA_genename_and_index.tsv`: gene name and index corresponds to .h5ad file | |
| `processed_fetal_lung_visium_xenium.xenium.convert.h5ad`: test for dump cell embedding |