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liranmao/meowcat-predictions

MeowCat cell-type predictions on TCGA-LUAD and CPTAC-CCRCC Per-pixel cell-type predictions generated by MeowCat on H&E whole-slide images from two public cohorts: Cohort Tissue Samples h5ad payload TCGA-LUAD Lung adenocarcinoma 531 ~60 GB CPTAC-CCRCC Clear-cell renal cell carcinoma 831 ~93 GB File layout composition.parquet # long format: sample × cell_type → count, fraction metadata.parquet # sample_id, cohort, patient_id, n_pixels… See the full description on the dataset page: https://huggingface.co/datasets/liranmao/meowcat-predictions.

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Dataset Card

MeowCat cell-type predictions on TCGA-LUAD and CPTAC-CCRCC

Per-pixel cell-type predictions generated by MeowCat on H&E whole-slide images from two public cohorts:

Cohort	Tissue	Samples	h5ad payload
TCGA-LUAD	Lung adenocarcinoma	531	~60 GB
CPTAC-CCRCC	Clear-cell renal cell carcinoma	831	~93 GB

File layout

composition.parquet      # long format: sample × cell_type → count, fraction
metadata.parquet         # sample_id, cohort, patient_id, n_pixels, original h5ad path
cell_type_vocab.json     # per-cohort cell-type list (same 8 types, different order)
tcga_luad/<sample>.h5ad  # 531 files
cptac_ccrcc/<sample>.h5ad # 831 files

Each .h5ad (AnnData) contains:

field	content
`obs['meowcat_label']`	argmax cell-type label per pixel (categorical)
`obsm['spatial']`	(x, y) pixel coordinates in the downsampled grid, float32 (N, 2)
`obsm['meowcat_probs']`	predicted probabilities, float32 (N, 8)
`uns['meowcat_ctypes']`	cell-type names — use this to align probability columns
`X`	empty (no expression data — predictions only)

Cell-type vocabulary

Both cohorts share the same 8 classes (different column order in each file — always use uns['meowcat_ctypes'] to align):

NonTumor_Epi, Tumor_Epi, B, Plasma, T, NK, Myeloid, Stromal

Quickstart

python

import pandas as pd
import anndata as ad
from huggingface_hub import hf_hub_download

# Cohort-level composition (small, fast)
comp = pd.read_parquet(hf_hub_download(
    "liranmao/meowcat-predictions", "composition.parquet", repo_type="dataset"))
print(comp.groupby(["cohort", "cell_type"]).fraction.mean().unstack())

# One sample's full pixel-level prediction
h5 = hf_hub_download(
    "liranmao/meowcat-predictions", "tcga_luad/S100.h5ad", repo_type="dataset")
a = ad.read_h5ad(h5)
print(a.obs["meowcat_label"].value_counts())
print(a.obsm["spatial"][:3], a.obsm["meowcat_probs"][:3])

Provenance

Predictions produced by the MeowCat multi-resolution model (3-phase training: masked reconstruction → Visium MSE → Xenium CE). See the MeowCat repository and accompanying paper for model details, training data, and limitations.

Notes & limitations

—Predictions are at the downsampled pixel-grid level used during inference, not at single-cell resolution. obsm['spatial'] gives integer-valued pixel coordinates within that grid.
—CPTAC-CCRCC: 308 additional samples have raw full-grid prediction pickles but are not included here because cell-bin h5ad outputs were not generated for them. They can be added in a future release on request.
—TCGA-LUAD sample IDs are internal short codes (S100 …). Mapping to TCGA patient barcodes is not included in this release.
—No clinical metadata (stage, survival) is shipped here yet. To join, pull from GDC (TCGA) / PDC (CPTAC) using patient_id. A future release may include this.

License

Released under CC-BY-4.0. If you use this dataset, please cite the MeowCat paper and acknowledge TCGA (NIH) and CPTAC (NCI) as the original imaging sources.

Contact

Issues / questions: open an issue at the MeowCat repository.