|
|
--- |
|
|
language: |
|
|
- en |
|
|
--- |
|
|
|
|
|
# AIDO.Cell Dataset Collection |
|
|
|
|
|
## Cell Type Classification |
|
|
|
|
|
| Dataset Name | Location | # Classes | Citation | Notes | |
|
|
| --- | --- | --- | --- | --- | |
|
|
| Zheng | `zheng` | 11 | [Zheng et al. 2017](https://pubmed.ncbi.nlm.nih.gov/28091601/) | Human PBMCs. Same splits as [Ho et al. 2024](https://www.biorxiv.org/content/10.1101/2024.11.28.625303v1). | |
|
|
| Segerstolpe | `Segerstolpe` | 13 | [Segerstople et al. 2016](https://pubmed.ncbi.nlm.nih.gov/27667667/) | Same splits as [Ho et al. 2024](https://www.biorxiv.org/content/10.1101/2024.11.28.625303v1). | |
|
|
| scTab | `sctab` | 164 | [Fischer et al. 2024](https://www.nature.com/articles/s41467-024-51059-5) | TileDB version of the `minimal` dataset from [scTab's GitHub](https://github.com/theislab/scTab). | |
|
|
|
|
|
## Perturbation Datasets |
|
|
|
|
|
### Tahoe-100M |
|
|
For demonstration purposes, we include data for one plate in `tahoe100m/h5ad`. Instructions for accessing the full dataset can be found on [GitHub](https://github.com/ArcInstitute/arc-virtual-cell-atlas/tree/main). |
|
|
|
|
|
## Transcriptomic Clock Dataset |
|
|
GenBio AI has curated a large dataset for transcriptomic clock modeling, derived from [CELLxGENE](https://cellxgene.cziscience.com/). The data can be found in `clocks`. |
|
|
|
|
|
### Cell filtering |
|
|
|
|
|
The dataset is derived from the `2023-07-25` version of the CELLxGENE census. |
|
|
|
|
|
We then restrict to cells that meet the following criteria: |
|
|
* Cells must be human |
|
|
* Cells must be primary cells |
|
|
* Cells must be derived from subjects with no disease labels (i.e. nominally "healthy" subjects) |
|
|
* Cells must be sequenced with a 10x technology |
|
|
|
|
|
### `cell+tissue` type filtering |
|
|
|
|
|
Let's call the combination of tissue type (`tissue_general`) and cell type (`cell_type`) a `cell+tissue` type. |
|
|
|
|
|
We discard all cells for a `cell+tissue` type if: |
|
|
* Fewer than 50 donors are represented |
|
|
* Fewer than 2 ages are represented |
|
|
|
|
|
### Splits |
|
|
|
|
|
For each donor, all cells were randomly assigned to exactly one split: train (70%), validation (15%), or test (15%). |
|
|
|
|
|
### Mapping `development_stage` values to numeric ages |
|
|
|
|
|
Age information in CELLxGENE is derived from the `development_stage` field. |
|
|
|
|
|
* Some values of `development stage` give a precise age in years. |
|
|
* Example: `80 year-old human`. In this case, we assign a numerical value of `80`. |
|
|
* Other values of `development_stage` are broader. |
|
|
* Example: `child_stage`. [It turns out](https://ontology.archive.data.humancellatlas.org/ontologies/hcao/terms?iri=http%3A%2F%2Fpurl.obolibrary.org%2Fobo%2FHsapDv_0000081) that this is synonymous with the age range of 2-12 years. In this case, we assign a numerical value of `7`, corresponding to the midpoint of the range. |
|
|
|
|
|
This means that some of our numerical age values are more precise than others. This is reflected in the `age_precision` variable, which gives the maximum error in the assigned value of `age`. For instance, for `child_stage` we have a value of `5` for `age_precision`, since the assigned age (`7`) could be 5 years too low (i.e. age `12`) or too high (i.e. age `2`). |
|
|
|
