# 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