README.md

---
license: other
language:
- en
pretty_name: scCAFM Tutorial Data
size_categories:
- 100K<n<1M
tags:
- single-cell
- scRNA-seq
- gene-regulatory-network
- perturb-seq
- chip-seq
- genomics
- biology
---

# scCAFM Tutorial Data

This repository contains the prepared datasets used by the executable tutorials for **scCAFM**, a causality-aware single-cell RNA-seq foundation model. The collection supports three workflows:

1. inference of cell-specific gene regulatory networks (GRNs) in developing mouse pancreas;
2. pooled-GRN inference and ChIP-seq-based benchmarking in human and mouse embryonic stem cells;
3. validation of predicted regulatory edges with CRISPRi Perturb-seq in K562 cells.

These files are tutorial-ready derivatives of public research datasets. They are not replacements for the primary archives. Users should cite the relevant original study and repository record listed below.

Related resources:

- [scCAFM model](https://huggingface.co/kaichenxu/scCAFM)
- [scCAFM source and tutorials](https://github.com/Catchxu/scCAFM)
- [scCAFM Space](https://huggingface.co/spaces/kaichenxu/scCAFM)

## Repository structure

```text
scCAFM-data/
├── cell_specific_grns/
│   └── mPancreas.h5ad
├── chipseq_grn_recovery/
│   ├── hESC.h5ad
│   ├── mESC.h5ad
│   ├── hESC-ChIP-seq.csv
│   └── mESC-ChIP-seq.csv
├── perturbseq_edge_validation/
│   └── K562.h5ad
├── LICENSES.md
└── MANIFEST.tsv
```

## Dataset inventory

| File | Contents | Dimensions |
|---|---|---:|
| `cell_specific_grns/mPancreas.h5ad` | Processed E15.5 mouse pancreatic endocrinogenesis data with cell-population annotations, spliced/unspliced layers, PCA and UMAP coordinates | 3,696 cells × 27,998 genes |
| `chipseq_grn_recovery/hESC.h5ad` | BEELINE-derived human embryonic stem-cell expression matrix | 758 cells × 17,735 genes |
| `chipseq_grn_recovery/mESC.h5ad` | BEELINE-derived mouse embryonic stem-cell expression matrix | 421 cells × 18,385 genes |
| `chipseq_grn_recovery/hESC-ChIP-seq.csv` | Directed human ChIP-seq reference edges (`Gene1` regulator, `Gene2` target) | 441,991 edges |
| `chipseq_grn_recovery/mESC-ChIP-seq.csv` | Directed mouse ChIP-seq reference edges (`Gene1` regulator, `Gene2` target) | 985,654 edges |
| `perturbseq_edge_validation/K562.h5ad` | QC-filtered raw-count K562 essential-scale CRISPRi Perturb-seq data | 162,751 cells × 8,563 genes |

SHA-256 checksums and exact byte sizes are provided in [`MANIFEST.tsv`](MANIFEST.tsv).

## Data formats

### AnnData files

The `.h5ad` files follow the [AnnData](https://anndata.readthedocs.io/) convention:

- rows (`obs`) are cells;
- columns (`var`) are measured genes;
- `X` stores the expression matrix;
- additional annotations, layers and embeddings are retained where available.

Important fields include:

- `mPancreas.h5ad`
  - `obs["clusters"]`: eight detailed cell populations;
  - `obs["clusters_coarse"]`: five broader populations;
  - `obs["species"]`: `mouse`;
  - `layers["spliced"]` and `layers["unspliced"]`;
  - `obsm["X_pca"]` and `obsm["X_umap"]`.
- `hESC.h5ad` and `mESC.h5ad`
  - `obs["species"]`;
  - `obs["disease"]`, set to `normal`.
- `K562.h5ad`
  - `X`: raw counts after the recorded filtering and perturbation-QC steps;
  - `obs["gene"]`: perturbed gene or `non-targeting`;
  - `obs["gene_id"]`: Ensembl gene identifier for the perturbation;
  - `obs["sgID_AB"]`: paired guide identifiers;
  - `obs["species"]`, `obs["disease"]`, `obs["cell_line"]`, `obs["cell_type"]` and `obs["tissue"]`;
  - `var["gene_name"]`: gene symbol;
  - `uns["basic_filter"]` and `uns["perturbation_qc"]`: recorded preparation parameters and cell counts.

### ChIP-seq edge tables

Both CSV files contain:

| Column | Definition |
|---|---|
| `Gene1` | Regulator/transcription factor |
| `Gene2` | Putative target gene supported by the BEELINE ChIP-seq reference |

The ChIP-seq networks are experimental reference networks, not absolute biological ground truth.

## Download and load

Download the complete snapshot:

```python
from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="kaichenxu/scCAFM-data",
    repo_type="dataset",
    local_dir="tutorial_data",
)
```

Load the files:

```python
import anndata as ad
import pandas as pd

pancreas = ad.read_h5ad(
    "tutorial_data/cell_specific_grns/mPancreas.h5ad"
)

hesc_chip = pd.read_csv(
    "tutorial_data/chipseq_grn_recovery/hESC-ChIP-seq.csv"
)
```

For reproducible analyses, pin the dataset repository to a commit revision when calling `snapshot_download`.

## Provenance and preparation

### Mouse pancreatic endocrinogenesis

`mPancreas.h5ad` is derived from the processed E15.5 pancreatic endocrinogenesis dataset distributed through scVelo. The underlying experiment is available from GEO under [GSE132188](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE132188) and was described by Bastidas-Ponce *et al.* The publication copy corrects an erroneous `obs["species"]` value from `human` to `mouse`; the expression matrix, layers, annotations and embeddings are otherwise unchanged during this packaging step.

### Embryonic stem-cell expression and ChIP-seq references

The hESC and mESC materials are reformatted from the experimental expression data and ChIP-seq reference networks distributed with BEELINE:

- BEELINE data record: [10.5281/zenodo.3378975](https://doi.org/10.5281/zenodo.3378975)
- BEELINE study: [10.1038/s41592-019-0690-6](https://doi.org/10.1038/s41592-019-0690-6)
- hESC source experiment: [GSE75748](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE75748)
- mESC source experiment: [GSE98664](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE98664)

The expression matrices were placed into cell-by-gene AnnData files and supplied with the species and condition fields required by the scCAFM tutorials. The directed ChIP-seq edge tables retain the BEELINE regulator-target representation.

### K562 Perturb-seq

`K562.h5ad` is derived from the K562 essential-scale CRISPRi Perturb-seq experiment sampled at day 6 in Replogle *et al.*:

- processed-data record: [10.25452/figshare.plus.20029387.v1](https://doi.org/10.25452/figshare.plus.20029387.v1)
- study: [10.1016/j.cell.2022.05.013](https://doi.org/10.1016/j.cell.2022.05.013)
- raw sequencing archive: [GSE146194](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE146194)

The preparation started from the raw single-cell count object. The stored provenance records 310,385 original cells, 192,648 cells after perturbation-level QC and 162,751 retained cells after cell-level QC. Basic filters require at least 200 detected genes per cell and at least three cells per gene. The stored perturbation-QC metadata records a minimum of more than 25 filtered cells, a maximum knockdown percentage of −0.3 (inclusive), more than 50 differentially expressed genes, and a 10th-percentile control threshold for cell-effect filtering.

## Intended use

The collection is intended for:

- running the curated scCAFM tutorials;
- testing scCAFM data loading and preprocessing;
- reproducing the tutorial-level GRN inference and validation examples;
- educational exploration of single-cell GRN workflows.

It is not intended to serve as a new primary archive, a clinical resource or a comprehensive reprocessing of the source studies.

## Limitations

- The files are processed tutorial derivatives; consult the primary repositories for raw data and full experimental metadata.
- ChIP-seq binding provides population-level regulatory evidence but does not prove that every edge is active in every cell.
- A Perturb-seq expression shift can be indirect or off-target and does not by itself prove a direct TF-target interaction.
- The three workflows use different organisms, assays and processing histories; matrices should not be concatenated or compared without an explicit harmonization strategy.
- The hESC and mESC AnnData files contain only the metadata needed by the tutorial and do not reproduce every field from their primary archives.

## Access, rights and attribution

This is a mixed-source collection, so no single software license applies to every data file. The repository therefore uses the Hugging Face `other` license designation. Per-source licenses and attribution requirements are documented in [`LICENSES.md`](LICENSES.md). Redistribution through this repository does not replace the original terms, and users must follow the terms associated with each source dataset.

## Citation

When using a file from this repository, cite the corresponding original dataset and study listed in **Provenance and preparation**, as well as the scCAFM model or paper associated with the analysis.

Suggested repository reference:

> Xu, K. (2026). *scCAFM Tutorial Data*. Hugging Face Datasets. https://huggingface.co/datasets/kaichenxu/scCAFM-data