Publish scCAFM tutorial datasets

.gitattributes

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+*.csv filter=lfs diff=lfs merge=lfs -text
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 *.h5 filter=lfs diff=lfs merge=lfs -text
+*.h5ad filter=lfs diff=lfs merge=lfs -text
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LICENSES.md

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+# Rights, licences and attribution
+
+This repository aggregates tutorial-ready derivatives from multiple public sources. It does not apply the scCAFM software licence to third-party data. Each component retains the terms and attribution requirements of its source.
+
+## Component-specific terms
+
+| Repository files | Source | Source terms |
+|---|---|---|
+| `chipseq_grn_recovery/hESC.h5ad`, `chipseq_grn_recovery/mESC.h5ad`, `chipseq_grn_recovery/hESC-ChIP-seq.csv`, `chipseq_grn_recovery/mESC-ChIP-seq.csv` | BEELINE data record, [10.5281/zenodo.3378975](https://doi.org/10.5281/zenodo.3378975) | [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) as stated by the Zenodo record |
+| `perturbseq_edge_validation/K562.h5ad` | Replogle and Weissman processed Perturb-seq record, [10.25452/figshare.plus.20029387.v1](https://doi.org/10.25452/figshare.plus.20029387.v1) | [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) as stated by the Figshare record |
+| `cell_specific_grns/mPancreas.h5ad` | Bastidas-Ponce *et al.* data, [GSE132188](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE132188), distributed in processed form through scVelo | No new licence is asserted by this aggregation; consult the original GEO record, publication and distribution source for applicable terms |
+| `README.md`, `LICENSES.md`, `MANIFEST.tsv` | scCAFM dataset packaging metadata | Provided for attribution and reproducibility with this repository |
+
+## Attribution
+
+Users must cite the original data generators and repository records relevant to the files they use. The required source links and study identifiers are provided in the root [`README.md`](README.md).
+
+## No additional grant
+
+Except for the repository-authored metadata, this repository does not grant rights beyond those provided by the original data owners and repositories. If the terms displayed here conflict with an original source record, the original source record controls.

MANIFEST.tsv

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+path	size_bytes	sha256	format	records	variables	source_identifier
+cell_specific_grns/mPancreas.h5ad	52500160	6f5a0dc98295e7c909573fec8058c17a30502882562f258021f04df0a26334d9	H5AD	3696 cells	27998 genes	GEO:GSE132188
+chipseq_grn_recovery/hESC.h5ad	45939135	b4913b26e73326a0d8d226edb6dbca9a53c803e82d9d2c85decf1e9271a40427	H5AD	758 cells	17735 genes	GEO:GSE75748; DOI:10.5281/zenodo.3378975
+chipseq_grn_recovery/mESC.h5ad	47678592	e8e312befc7c5c7fc4aa23144792a0e3917820fe2020e23f733163047c1e5d11	H5AD	421 cells	18385 genes	GEO:GSE98664; DOI:10.5281/zenodo.3378975
+chipseq_grn_recovery/hESC-ChIP-seq.csv	5699096	23fa58c407cb654cd820ff6286e728bf0646b387fcc1c7c7f5d65ac2ee26fa61	CSV	441991 edges	2 columns	DOI:10.5281/zenodo.3378975
+chipseq_grn_recovery/mESC-ChIP-seq.csv	13176251	a2ffed3ad8cb33410662e57b49a8300ecdd8e523a0e14cd370c55bc761c28ace	CSV	985654 edges	2 columns	DOI:10.5281/zenodo.3378975
+perturbseq_edge_validation/K562.h5ad	792811426	f3d20e979e1454dc68381f782d5756b336fdba3dbb642b59cf9cb866ccefa128	H5AD	162751 cells	8563 genes	DOI:10.25452/figshare.plus.20029387.v1; GEO:GSE146194

README.md

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 ---
-license: gpl-3.0
+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

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