README.md

---
license: cc-by-4.0
language:
- en
size_categories:
- 1M<n<10M
task_categories:
- feature-extraction
tags:
- single-cell
- scRNA-seq
- snRNA-seq
- spatial-transcriptomics
- visium
- cancer
- hepatocellular-carcinoma
- HCC
- iCCA
- liver
- atlas
- cellxgene
- anndata
pretty_name: AIVIN Liver References
---

# AIVIN · Liver References

![AIVIN Liver preview](preview.png)

Harmonized single-cell + single-nucleus + Visium spatial reference set for
**human liver tissue** (cancer + healthy + chronic-liver-disease), curated
under the AIVIN cross-cancer reference project at UCSD. All files are
[CELLxGENE schema 7.0.0](https://github.com/chanzuckerberg/single-cell-curation)
compatible and follow the unified AIVIN naming + provenance convention.

| | |
|---|---|
| **Files** | **41** `.h5ad` (40 valid + 1 known-broken — see Known Issues) |
| **Cells (sc + sn)** | **~1,177,000** |
| **Visium spots** | **~34,900** |
| **Distinct cohorts** | **18** (12 cancer + 5 healthy + 1 CLD) |
| **Source studies** | 15 GEO + CELLxGENE Census + 1 GSM-direct |
| **Disease dimensions** | HCC primary · HCC metastatic · HCC fetal · HCC anti-PD1 · HCC MASH · iCCA · cholangiocarcinoma · NASH · HCV · chronic liver disease · PSC · PBC · healthy |
| **Platforms** | 10x Chromium v2/v3 · Smart-seq2 · CEL-Seq2 · 10x Visium |
| **Total size** | ~14 GB |
| **Snapshot** | AIVIN 2026-Q2 v1.0 |
| **Zenodo DOI** | `[pending — Sat 5/30 snapshot]` |
| **HF DOI** | `[pending — mint after upload]` |
| **License** | CC-BY-4.0 (plus cite original cohort papers) |

---

## What's in this repo

Every `.h5ad` follows the AIVIN naming convention (see `NAMING_INDEX §八` in the AIVIN GitHub for the full grammar):

```
<modality>__<cohort-slug>__<who>__<NcxMg>__<accession>.h5ad
   │           │             │        │           └── GEO GSE / GSM · GSA HRA · CELLxGENE UUID · Zenodo ID
   │           │             │        └────────────── shape: cells × genes  (or spots × genes for visium)
   │           │             └────────────────────── first-author + year + n donors/samples
   │           └─────────────────────────────────── biology tag (disease + sub-type)
   └───────────────────────────────────────────── modality: sc = single-cell · sn = single-nucleus · visium = spatial
```

### Cohort manifest

#### Cancer cohorts (GEO source · 24 files · ~874k cells)

| Cohort slug | Source | Cells | Disease | Platform | Citation |
|---|---|---:|---|---|---|
| `hcc-cd45` | GSE235863 | 191,435 | HCC CD45+ enriched | 10x | Guo et al., 2025 |
| `hcc-fetal` | GSE156625 | 109,238 | HCC onco-fetal | 10x | Sharma et al., *Cell* 2020 |
| `hcc-cd8tcell` | GSE235863 | 95,408 | HCC CD8 T cells | 10x | Guo et al., 2025 |
| `hcc-tumor-normal` (Sharma) | GSE156625 | 73,589 | HCC tumor + adjacent | 10x | Sharma et al., *Cell* 2020 |
| `hcc-multisite` | GSE149614 | 71,915 | HCC primary + metastatic + PVTT + LN | 10x | Lu et al., *Nat Commun* 2022 |
| `hcc-iccA-cd45` | GSE140228-droplet | 66,187 | HCC + iCCA, CD45+ | 10x | Sharma et al., *Cell* 2020 |
| `hcc-iccA-treated` | GSE151530 | 56,721 | HCC + iCCA, post-treatment | 10x | Ma et al., *J Hepatol* 2021 |
| `hcc-trm` | GSE281110 | 41,848 | HCC tumor-associated TRM T | 10x | Park et al., 2025 |
| `hcc-tumor-normal-3pt` | GSE189175 | 39,995 | HCC tumor + normal | sn-10x | Alvarez et al., 2022 |
| `hcc-tumor-normal-1pt` | GSE189175 | 39,995 | (duplicate — see Known Issues) | sn-10x | Alvarez et al., 2022 |
| `hcc-mash-spectrum` | GSE282630 | 34,396 | HCC + MASH spectrum | 10x | Huang et al., 2025 |
| `hcc-cd45-ss2` | GSE140228-ss2 | 7,074 | HCC CD45+ Smart-seq2 | SS2 | Sharma et al., *Cell* 2020 |
| `hcc-iccA-mixed-set1` | GSE125449-set1 | 5,115 | HCC + iCCA, mixed | 10x | Ma et al., *Cancer Cell* 2019 |
| `hcc-tcell` | GSE98638 | 5,063 | HCC infiltrating T cells | SMART-seq2 | Zheng et al., *Cell* 2017 |
| `hcc-iccA-mixed-set2` | GSE125449-set2 | 4,831 | HCC + iCCA, mixed | 10x | Ma et al., *Cancer Cell* 2019 |
| `hcc-antiPD1` (R1) | GSE238264-HCC1R | 3,006 | HCC anti-PD1 responder | 10x | Liu et al., 2025 |
| `hcc-antiPD1` (R4) | GSE238264-HCC4R | 3,002 | HCC anti-PD1 responder | 10x | Liu et al., 2025 |
| `hcc-antiPD1` (R2) | GSE238264-HCC2R | 2,766 | HCC anti-PD1 responder | 10x | Liu et al., 2025 |
| `hcc-antiPD1` (NR6) | GSE238264-HCC6NR | 2,575 | HCC anti-PD1 non-responder | 10x | Liu et al., 2025 |
| `hcc-antiPD1` (NR7) | GSE238264-HCC7NR | 2,453 | HCC anti-PD1 non-responder | 10x | Liu et al., 2025 |
| `hcc-antiPD1` (R3) | GSE238264-HCC3R | 2,170 | HCC anti-PD1 responder | 10x | Liu et al., 2025 |
| `hcc-antiPD1` (NR5) | GSE238264-HCC5NR | 1,320 | HCC anti-PD1 non-responder | 10x | Liu et al., 2025 |
| `cld-lyec` | GSE129933 | 901 | Chronic liver disease lymphatic EC | SMART-seq2 | Tamburini et al., *Front Immunol* 2019 |
| `healthy-nat` | GSM4648565 | 13,083 | healthy liver | 10x | (Nat Commun 2020) |

#### Healthy + autoimmune baselines (CELLxGENE Census · 11 sc/sn files · ~303k cells)

| Cohort slug | Cells | Cell type / disease | Modality |
|---|---:|---|---|
| `psc-pbc-healthy` (sn) | 105,780 | PSC + PBC + healthy, all cells | sn |
| `psc-pbc-healthy` (sc) | 89,637 | PSC + PBC + healthy, all cells | sc |
| `healthy hepatocyte-v1` | 53,015 | hepatocytes | sc |
| `healthy lymphoid` | 16,665 | lymphoid lineage | sc |
| `healthy hepatocyte-v2` | 13,635 | hepatocytes (alt curation) | sc |
| `healthy macrophage` | 11,127 | macrophages | sc |
| `healthy endothelial` | 9,422 | endothelial cells | sc |
| `healthy stellate` | 1,417 | hepatic stellate cells | sc |
| `healthy b-cell` | 1,250 | B cells | sc |
| `healthy cholangiocyte` | 1,011 | cholangiocytes | sc |

#### Spatial transcriptomics (CELLxGENE Census · 6 Visium files · ~35k spots)

| Cohort slug | Spots | Tissue block | Disease |
|---|---:|---|---|
| `visium healthy-C73 / blockA1` | 4,992 | block A1 | healthy donor C73 |
| `visium healthy-C73 / blockC1` | 4,992 | block C1 | healthy donor C73 |
| `visium healthy-C73 / blockD1` | 4,992 | block D1 | healthy donor C73 |
| `visium psc-PSC011 / blockA1` | 4,992 | block A1 | PSC patient 011 |
| `visium psc-PSC011 / blockB1` | 4,992 | block B1 | PSC patient 011 |
| `visium psc-PSC011 / blockC1` | 4,992 | block C1 | PSC patient 011 |
| `visium psc-PSC011 / blockD1` | 4,992 | block D1 | PSC patient 011 |

---

## Schema

All `.h5ad` conform to **CELLxGENE schema 7.0.0** plus AIVIN extensions:

**`obs` (cells) — required columns**

- `cell_id` (index)
- `donor_id` (when known)
- `tissue_site` — unified vocab: `PT` (primary tumor) · `NTL` (normal liver) · `JTL` (juxta-tumor liver) · `MLN` (lymph node metastasis) · `PVTT` (portal vein tumor thrombus) · `PBMC` (peripheral blood) · `LIL` (liver intra-lesional)
- `disease` — values within the **Disease dimensions** list above
- `cell_type` (when annotated by original author)
- `assay` — platform / chemistry

**`var` (genes) — convention**

- Ensembl ID as `var.index` (when available, esp. CELLxGENE-sourced)
- Some GEO-sourced cohorts use HGNC `gene_symbol` as index + `entrez_id` column
- Heterogeneity across cohorts: 18 distinct gene-space sizes (2,384 – 58,100 genes) — see `aivin_obs_field_notes` per file for caveats; downstream concat use `ad.concat(..., join='outer')`

**`uns` (provenance, AIVIN-specific)**

- `citation` — full APA reference
- `doi` — primary paper DOI
- `source_accession` — GEO GSE / GSM / GSA HRA / CELLxGENE UUID / Zenodo ID
- `source_url`
- `aivin_ingest_date`
- `aivin_cohort_slug`
- `aivin_source_files` — original raw filename list
- `aivin_obs_field_notes` — any value-mapping done in ingest

---

## Usage

### Load one cohort (lazy / single file)

```python
from huggingface_hub import hf_hub_download
import anndata as ad

path = hf_hub_download(
    repo_id='AIVIN-UCSD/liver-references',
    filename='sc__hcc-multisite__lu2022-10pts__71915cx25712g__GSE149614.h5ad',
    repo_type='dataset',
)
a = ad.read_h5ad(path)
print(a)
# Inspect AIVIN provenance
print(a.uns['citation'])
print(a.uns['aivin_obs_field_notes'])
```

### Load all cancer cohorts + concat (gene union)

```python
from huggingface_hub import snapshot_download
from pathlib import Path
import anndata as ad

local = snapshot_download(
    repo_id='AIVIN-UCSD/liver-references',
    repo_type='dataset',
    allow_patterns='sc__hcc-*.h5ad',   # cancer only
    ignore_patterns='*macparland2019-0donors*',   # skip known-broken file
)
adatas = {f.stem: ad.read_h5ad(f) for f in Path(local).glob('sc__hcc-*.h5ad')}
merged = ad.concat(adatas, axis=0, join='outer', label='cohort', fill_value=0)
print(merged)
# ~750k cells × union of genes across cohorts
```

### Pipe into scvi-tools (foundation model training)

```python
import scvi
scvi.model.SCVI.setup_anndata(merged, batch_key='cohort')
model = scvi.model.SCVI(merged, n_layers=2, n_latent=30)
model.train(accelerator='mps')   # Apple Silicon MPS acceleration
```

---

## Citation

If you use this dataset in a publication, please cite:

1. **AIVIN as a collection** (this dataset card):
   ```bibtex
   @dataset{aivin_liver_2026Q2,
     author    = {AIVIN Project, UCSD},
     title     = {{AIVIN Liver References (2026-Q2 v1.0)}},
     year      = {2026},
     publisher = {Hugging Face},
     doi       = {[pending HF DOI mint]},
     url       = {https://huggingface.co/datasets/AIVIN-UCSD/liver-references}
   }
   ```

2. **Each individual cohort** — see the `uns.citation` field of every `.h5ad`,
   or the **Cohort manifest** table above. Particularly for landmark papers:
   - Lu et al., *Nat Commun* 13:4594 (2022) — `doi:10.1038/s41467-022-32283-3`
   - Sharma et al., *Cell* 183:377 (2020) — `doi:10.1016/j.cell.2020.08.040`
   - Ma et al., *J Hepatol* 75:1418 (2021) — `doi:10.1016/j.jhep.2021.06.028`
   - Ma et al., *Cancer Cell* 36:418 (2019) — `doi:10.1016/j.ccell.2019.08.007`
   - Zheng et al., *Cell* 169:1342 (2017) — `doi:10.1016/j.cell.2017.05.035`

3. **(Optional) the Zenodo permanent snapshot** for byte-frozen reproducibility:
   `doi: [pending Sat 5/30]`

---

## License

This collection is released under **CC-BY-4.0**. The license applies to AIVIN's
harmonization, schema mapping, and provenance metadata. **You must still cite
the original cohort papers** when using their data — see the per-cohort
manifest above. Cohorts derived from controlled-access sources (e.g., GSA-Human
HRA001748 Xue 2022) are NOT included in this public repo; see the cross-tissue
meta-repo for access pointers.

---

## Pipeline & reproducibility

- **Ingest scripts**: `github.com/AIVIN-UCSD/aivin/tree/main/scripts`
  (per-cohort `<Cn>_<author><year>_ingest.py` + `W3_backlog_ingest.py` dispatcher)
- **Methods extracts**: per-paper structured methods at
  `github.com/AIVIN-UCSD/aivin/tree/main/literature/A_cancer_TME/methods_extracts`
- **Structure report**: full per-file schema audit at
  `github.com/AIVIN-UCSD/aivin/blob/main/database_unified/Liver_References/STRUCTURE_REPORT.md`
- **Backlog inventory**: candidates for v3 (3-month) expansion at
  `github.com/AIVIN-UCSD/aivin/blob/main/database_unified/_staging/BACKLOG_INVENTORY.md`

---

## Known issues (v1.0)

| Issue | Affected file | Fix planned |
|---|---|---|
| **MacParland v1 ingest broken** (shape `0 × 3,958,008`) — the multi-plate CEL-Seq2 concat in `ingest_GSE124395()` produced a degenerate output | `sc__healthy-hlca__macparland2019-0donors__0cx3958008g__GSE124395.h5ad` | Will re-ingest in v1.1 with proper plate-level dedup; **filter out via `ignore_patterns='*macparland2019-0donors*'`** in `snapshot_download` |
| **GSE189175 Alvarez duplicate** — same 39,995 cells appear twice with different `who` slugs (`alvarez2022-1pts` and `alvarez2022-3pts`) | both files identical | Will dedup to single file in v1.1 |
| **Gene-space heterogeneity** — 18 distinct gene-space sizes across cohorts (Smart-seq2 ~54k vs 10x v3 ~36k vs reduced curation ~2-3k) | all multi-cohort concat operations | Use `ad.concat(..., join='outer', fill_value=0)`; foundation model fine-tune should project to common Ensembl space |
| **Some cohorts use HGNC symbol as var.index, others use Ensembl ID** | mixed across GEO vs CELLxGENE | Documented per-file in `uns.aivin_obs_field_notes`; v2 will unify to Ensembl ID |

---

## Contact

- 🤗 **HF discussions tab** on this repo (preferred for technical questions)
- 💬 **scverse Discourse**: https://discourse.scverse.org/ — `#show-and-tell` thread
- 📧 **z4fu@ucsd.edu** (project lead)
- 🐛 **Issues / PRs**: `github.com/AIVIN-UCSD/aivin`

---

*Last updated: 2026-05-25 · AIVIN v2.0 snapshot 2026-Q2 · 41 .h5ad (40 valid) · 1.17M cells + 35k spots · ~14 GB*