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AIVIN-UCSD
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liver-references

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single-cell
scRNA-seq
snRNA-seq
spatial-transcriptomics
visium
cancer
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AIVIN · Liver References

AIVIN Liver preview

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 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) 

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) 

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) 

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):

    @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
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