language:
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AIDO.Cell Dataset Collection
Cell Type Classification
| Dataset Name | Location | # Classes | Citation | Notes |
|---|---|---|---|---|
| Zheng | zheng |
11 | Zheng et al. 2017 | Human PBMCs. Same splits as Ho et al. 2024. |
| Segerstolpe | Segerstolpe |
13 | Segerstople et al. 2016 | Same splits as Ho et al. 2024. |
| scTab | sctab |
164 | Fischer et al. 2024 | TileDB version of the minimal dataset from scTab's GitHub. |
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.
Transcriptomic Clock Dataset
GenBio AI has curated a large dataset for transcriptomic clock modeling, derived from CELLxGENE. 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 stagegive a precise age in years.- Example:
80 year-old human. In this case, we assign a numerical value of80.
- Example:
- Other values of
development_stageare broader.- Example:
child_stage. It turns out that this is synonymous with the age range of 2-12 years. In this case, we assign a numerical value of7, corresponding to the midpoint of the range.
- Example:
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).