README.md

metadata

language:
  - en

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 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 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).