Datasets:

KokosDev
/

single-cell-lung-zarr

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README.md ADDED Viewed

	@@ -0,0 +1,279 @@

+---
+license: mit
+tags:
+  - biology
+  - single-cell
+  - scrna-seq
+  - zarr
+  - scanpy
+  - anndata
+---
+## Single-cell lung (CellxGene Census) — Zarr
+This dataset was exported from the **CellxGene Census** as a **chunked + compressed Zarr store** intended for easy streaming access.
+- **Source**: CellxGene Census API
+- **Organism**: Homo sapiens
+- **Filter**: `tissue_general == 'lung' and is_primary_data == True`
+- **Shape**: `100,000 cells × 61,497 genes`
+- **Zarr path**: `lung.zarr`
+### Included labels
+- `obs/cell_type`
+- `obs/disease`
+- `obs/tissue`
+- `obs/sex`
+- `obs/assay`
+### Cell type distribution (top 25)
+| cell_type | n_cells |
+|---|---:|
+| neuron | 8,037 |
+| oligodendrocyte | 4,890 |
+| fibroblast | 4,539 |
+| glutamatergic neuron | 3,367 |
+| macrophage | 2,216 |
+| endothelial cell | 1,941 |
+| astrocyte | 1,808 |
+| natural killer cell | 1,803 |
+| T cell | 1,734 |
+| malignant cell | 1,625 |
+| GABAergic neuron | 1,589 |
+| classical monocyte | 1,562 |
+| basal cell of prostate epithelium | 1,401 |
+| myeloid cell | 1,379 |
+| plasma cell | 1,263 |
+| epithelial cell of proximal tubule | 1,185 |
+| adipocyte of omentum tissue | 1,170 |
+| microglial cell | 1,164 |
+| acinar cell of salivary gland | 1,074 |
+| oligodendrocyte precursor cell | 1,044 |
+| monocyte | 1,036 |
+| blood vessel endothelial cell | 982 |
+| pericyte | 942 |
+| subcutaneous adipocyte | 932 |
+| CD8-positive, alpha-beta T cell | 830 |
+### Disease labels included
+Total unique disease labels: **72**
+```text
+Alzheimer disease
+B-cell acute lymphoblastic leukemia
+Barrett esophagus
+COVID-19
+Crohn disease
+Down syndrome
+HIV infectious disease
+HIV infectious disease || leishmaniasis
+HIV infectious disease || visceral leishmaniasis
+Lewy body dementia
+Parkinson disease
+Wilms tumor
+acute myeloid leukemia
+acute promyelocytic leukemia
+adenocarcinoma
+age related macular degeneration 7
+anencephaly
+arrhythmogenic right ventricular cardiomyopathy
+atrial fibrillation || mitral valve insufficiency
+basal cell carcinoma
+basal laminar drusen
+benign prostatic hyperplasia
+breast cancer
+breast carcinoma
+cataract
+chromophobe renal cell carcinoma
+clear cell renal carcinoma
+colon adenocarcinoma
+colon sessile serrated adenoma/polyp
+colorectal cancer
+common variable immunodeficiency
+cytomegalovirus infection
+dementia
+dilated cardiomyopathy
+enamel caries
+gastric intestinal metaplasia
+gastritis
+glioblastoma
+hyperplastic polyp
+hypertrophic cardiomyopathy
+influenza
+invasive ductal breast carcinoma
+invasive lobular breast carcinoma
+leukoencephalopathy, diffuse hereditary, with spheroids 1
+long COVID-19
+luminal A breast carcinoma
+luminal B breast carcinoma
+macular degeneration
+malignant ovarian serous tumor
+multiple sclerosis
+myocardial infarction
+myocarditis
+neuroblastoma
+neuroendocrine carcinoma
+non-compaction cardiomyopathy
+normal
+obstructive nephropathy
+oral cavity squamous cell carcinoma
+pilocytic astrocytoma
+plasma cell myeloma
+prediabetes syndrome
+prostatic acinar adenocarcinoma
+pulmonary emphysema
+pulpitis
+respiratory failure
+rheumatoid arthritis
+temporal lobe epilepsy
+triple-negative breast carcinoma
+tubular adenoma
+tubulovillous adenoma
+type 2 diabetes mellitus
+ulcerative colitis
+```
+## Data layout
+- **Expression matrix**: `X` (dense 2D array)
+  - **chunks**: `(1000, 1000)`
+  - **dtype**: `float32`
+  - **compression**: Blosc `zstd`
+- **Metadata**:
+  - `obs/_index`, `obs/<col>` (or `obs/<col>_codes` + `obs/<col>_categories` for categoricals)
+  - `var/_index`, `var/<col>` (or `var/<col>_codes` + `var/<col>_categories` for categoricals)
+## Loading (zarr → AnnData)
+```python
+import zarr
+import numpy as np
+import pandas as pd
+from anndata import AnnData
+root = zarr.open_group("lung.zarr", mode="r")
+X = root["X"]  # zarr Array (lazy / on-demand)
+obs = pd.DataFrame(index=root["obs/_index"][:].astype(str))
+for col in ["cell_type", "disease", "tissue", "sex", "assay"]:
+    codes_key = "obs/" + col + "_codes"
+    cats_key = "obs/" + col + "_categories"
+    if codes_key in root and cats_key in root:
+        obs[col] = pd.Categorical.from_codes(root[codes_key][:], root[cats_key][:].astype(str))
+    elif "obs/" + col in root:
+        obs[col] = root["obs/" + col][:].astype(str)
+var = pd.DataFrame(index=root["var/_index"][:].astype(str))
+for col in ["feature_name", "feature_id", "gene_symbol"]:
+    key = "var/" + col
+    if key in root:
+        var[col] = root[key][:].astype(str)
+# Convert to in-memory AnnData (loads full matrix):
+adata = AnnData(X=np.asarray(X), obs=obs, var=var)
+```
+## Scanpy workflow example
+```python
+import scanpy as sc
+sc.pp.normalize_total(adata, target_sum=1e4)
+sc.pp.log1p(adata)
+sc.pp.highly_variable_genes(adata, n_top_genes=2000, flavor="seurat_v3")
+adata = adata[:, adata.var["highly_variable"]].copy()
+sc.pp.scale(adata, max_value=10)
+sc.tl.pca(adata, svd_solver="arpack")
+sc.pp.neighbors(adata, n_neighbors=15, n_pcs=50)
+sc.tl.umap(adata)
+sc.tl.leiden(adata, resolution=0.5)
+```
+## Streaming benchmark (single 1000×1000 chunk)
+Measured locally while building this dataset:
+- open Zarr group: **0.0013 s**
+- read one `X[chunk]` (1000×1000): **0.0070 s**
+```json
+{
+  "open_seconds": 0.0013324879109859467,
+  "read_chunk_seconds": 0.007030146196484566,
+  "chunk_shape": [
+    1000,
+    1000
+  ],
+  "chunk_origin": [
+    84212,
+    38534
+  ],
+  "shape": [
+    100000,
+    61497
+  ]
+}
+```
+## Build metadata
+```json
+{
+  "census_version": "2025-11-08",
+  "created_at": "2026-02-03T06:49:50.898169+00:00",
+  "max_cells": 100000,
+  "n_hvg": 0,
+  "obs_value_filter": "tissue_general == 'lung' and is_primary_data == True",
+  "organism": "Homo sapiens",
+  "schema_version": "1.0",
+  "seed": 0,
+  "source": "cellxgene-census",
+  "x_chunks": [
+    1000,
+    1000
+  ],
+  "x_compression": {
+    "clevel": 3,
+    "cname": "zstd",
+    "codec": "blosc",
+    "shuffle": "bitshuffle"
+  },
+  "shape": [
+    100000,
+    61497
+  ],
+  "obs_arrays": [
+    "_index",
+    "assay",
+    "cell_type",
+    "disease",
+    "sex",
+    "tissue"
+  ],
+  "var_arrays": [
+    "_index",
+    "feature_id",
+    "feature_length",
+    "feature_name",
+    "feature_type",
+    "n_measured_obs",
+    "nnz",
+    "soma_joinid"
+  ]
+}
+```
+## Upload to HuggingFace Hub
+This repo is intended for: `KokosDev/single-cell-lung-zarr`.
+If you have a HuggingFace token locally, upload with:
+```bash
+python3.11 -m pip install huggingface_hub
+HF_TOKEN=*** python3.11 build_lung_zarr.py --upload --repo-id "KokosDev/single-cell-lung-zarr"
+```

build_lung_zarr.py ADDED Viewed

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+#!/usr/bin/env python3
+"""
+Build a single-cell RNA-seq lung dataset from the CellxGene Census and export to
+a HuggingFace-friendly Zarr store.
+Design goals (per request):
+  - Expression matrix stored as a Zarr array "X" with chunks=(1000, 1000)
+  - Blosc zstd compression for X (and for metadata arrays)
+  - obs/var metadata stored as separate Zarr arrays
+  - README auto-generated with label stats + load examples + streaming benchmark
+Practical note:
+Storing 100k cells × ~60k genes as a dense float32 matrix can be very large.
+This builder supports optionally selecting HVGs to reduce size, but defaults to
+keeping all genes to match the requested spec.
+"""
+from __future__ import annotations
+import argparse
+import datetime as _dt
+import json
+import os
+import time
+from pathlib import Path
+from typing import Any, Dict, Iterable, Tuple
+import numpy as np
+import pandas as pd
+import scipy.sparse as sp
+import zarr
+from numcodecs import Blosc, VLenUTF8
+import cellxgene_census
+def _utc_now_iso() -> str:
+    return _dt.datetime.now(tz=_dt.timezone.utc).isoformat()
+def _chunk_1d(n: int, target: int = 100_000) -> int:
+    return int(min(max(1, target), max(1, n)))
+def _as_str_object(values: Iterable[Any]) -> np.ndarray:
+    out = []
+    for v in values:
+        if v is None:
+            out.append("")
+        elif isinstance(v, float) and np.isnan(v):
+            out.append("")
+        else:
+            out.append(str(v))
+    return np.asarray(out, dtype=object)
+def _write_1d(group: zarr.hierarchy.Group, name: str, values: np.ndarray, *, compressor: Blosc) -> None:
+    if values.dtype == object:
+        ds = group.create_dataset(
+            name,
+            shape=(len(values),),
+            chunks=(_chunk_1d(len(values)),),
+            dtype=object,
+            object_codec=VLenUTF8(),
+            compressor=compressor,
+            overwrite=True,
+        )
+        # Zarr v2 expects an array-like, not a Python list, for vlen codecs.
+        ds[:] = np.asarray(values, dtype=object)
+        return
+    ds = group.create_dataset(
+        name,
+        shape=(len(values),),
+        chunks=(_chunk_1d(len(values)),),
+        dtype=values.dtype,
+        compressor=compressor,
+        overwrite=True,
+    )
+    ds[:] = values
+def _write_table_columns(
+    group: zarr.hierarchy.Group,
+    df: pd.DataFrame,
+    *,
+    compressor: Blosc,
+    prefix: str,
+) -> None:
+    # Always write index explicitly.
+    _write_1d(group, f"{prefix}/_index", _as_str_object(df.index), compressor=compressor)
+    for col in df.columns:
+        s = df[col]
+        base = f"{prefix}/{col}"
+        # Normalize object/string to explicit strings.
+        if pd.api.types.is_object_dtype(s) or pd.api.types.is_string_dtype(s):
+            _write_1d(group, base, _as_str_object(s), compressor=compressor)
+            continue
+        if pd.api.types.is_categorical_dtype(s):
+            codes = s.cat.codes.to_numpy(dtype=np.int32, copy=False)
+            cats = _as_str_object(s.cat.categories)
+            _write_1d(group, f"{base}_codes", codes, compressor=compressor)
+            _write_1d(group, f"{base}_categories", cats, compressor=compressor)
+            continue
+        if pd.api.types.is_bool_dtype(s):
+            _write_1d(group, base, s.to_numpy(dtype=np.bool_, copy=False), compressor=compressor)
+            continue
+        if pd.api.types.is_integer_dtype(s):
+            _write_1d(group, base, s.to_numpy(copy=False), compressor=compressor)
+            continue
+        if pd.api.types.is_float_dtype(s):
+            _write_1d(group, base, s.to_numpy(copy=False), compressor=compressor)
+            continue
+        # Fallback: stringify
+        _write_1d(group, base, _as_str_object(s), compressor=compressor)
+def _write_x_dense_chunked(
+    root: zarr.hierarchy.Group,
+    x: Any,
+    *,
+    shape: Tuple[int, int],
+    chunks: Tuple[int, int],
+    compressor: Blosc,
+    dtype: np.dtype = np.float32,
+) -> None:
+    n_obs, n_var = shape
+    X = root.create_dataset(
+        "X",
+        shape=(n_obs, n_var),
+        chunks=chunks,
+        dtype=dtype,
+        compressor=compressor,
+        fill_value=0,
+        overwrite=True,
+    )
+    row_chunk, col_chunk = chunks
+    for i0 in range(0, n_obs, row_chunk):
+        i1 = min(n_obs, i0 + row_chunk)
+        if i0 == 0 or i0 % (row_chunk * 10) == 0:
+            print(f"[zarr] writing X rows {i0}:{i1} / {n_obs}", flush=True)
+        for j0 in range(0, n_var, col_chunk):
+            j1 = min(n_var, j0 + col_chunk)
+            block = x[i0:i1, j0:j1]
+            if sp.issparse(block):
+                block = block.toarray()
+            X[i0:i1, j0:j1] = np.asarray(block, dtype=dtype)
+def _streaming_benchmark(zarr_path: Path, *, seed: int = 0) -> Dict[str, Any]:
+    t0 = time.perf_counter()
+    root = zarr.open_group(str(zarr_path), mode="r")
+    t_open = time.perf_counter() - t0
+    X = root["X"]
+    n_obs, n_var = X.shape
+    rng = np.random.default_rng(seed)
+    i0 = int(rng.integers(0, max(1, n_obs - 1000 + 1)))
+    j0 = int(rng.integers(0, max(1, n_var - 1000 + 1)))
+    t1 = time.perf_counter()
+    _ = np.asarray(X[i0 : i0 + 1000, j0 : j0 + 1000])
+    t_read = time.perf_counter() - t1
+    return {
+        "open_seconds": t_open,
+        "read_chunk_seconds": t_read,
+        "chunk_shape": [1000, 1000],
+        "chunk_origin": [i0, j0],
+        "shape": [int(n_obs), int(n_var)],
+    }
+def _render_readme(
+    *,
+    repo_id: str,
+    zarr_relpath: str,
+    adata_shape: Tuple[int, int],
+    cell_type_counts: Dict[str, int],
+    disease_labels: Iterable[str],
+    benchmark: Dict[str, Any],
+    meta: Dict[str, Any],
+) -> str:
+    disease_sorted = sorted(list(disease_labels))
+    top_cell_types = sorted(cell_type_counts.items(), key=lambda kv: kv[1], reverse=True)[:25]
+    def fmt_secs(x: Any) -> str:
+        try:
+            return f"{float(x):.4f}"
+        except Exception:
+            return str(x)
+    table_rows = "\n".join([f"| {ct} | {n:,} |" for ct, n in top_cell_types]) if top_cell_types else "| (missing) | 0 |"
+    return f"""---
+license: mit
+tags:
+  - biology
+  - single-cell
+  - scrna-seq
+  - zarr
+  - scanpy
+  - anndata
+---
+## Single-cell lung (CellxGene Census) — Zarr
+This dataset was exported from the **CellxGene Census** as a **chunked + compressed Zarr store** intended for easy streaming access.
+- **Source**: CellxGene Census API
+- **Organism**: Homo sapiens
+- **Filter**: `tissue_general == 'lung' and is_primary_data == True`
+- **Shape**: `{adata_shape[0]:,} cells × {adata_shape[1]:,} genes`
+- **Zarr path**: `{zarr_relpath}`
+### Included labels
+- `obs/cell_type`
+- `obs/disease`
+- `obs/tissue`
+- `obs/sex`
+- `obs/assay`
+### Cell type distribution (top 25)
+| cell_type | n_cells |
+|---|---:|
+{table_rows}
+### Disease labels included
+Total unique disease labels: **{len(disease_sorted)}**
+```text
+{chr(10).join(disease_sorted)}
+```
+## Data layout
+- **Expression matrix**: `X` (dense 2D array)
+  - **chunks**: `(1000, 1000)`
+  - **dtype**: `float32`
+  - **compression**: Blosc `zstd`
+- **Metadata**:
+  - `obs/_index`, `obs/<col>` (or `obs/<col>_codes` + `obs/<col>_categories` for categoricals)
+  - `var/_index`, `var/<col>` (or `var/<col>_codes` + `var/<col>_categories` for categoricals)
+## Loading (zarr → AnnData)
+```python
+import zarr
+import numpy as np
+import pandas as pd
+from anndata import AnnData
+root = zarr.open_group("{zarr_relpath}", mode="r")
+X = root["X"]  # zarr Array (lazy / on-demand)
+obs = pd.DataFrame(index=root["obs/_index"][:].astype(str))
+for col in ["cell_type", "disease", "tissue", "sex", "assay"]:
+    codes_key = "obs/" + col + "_codes"
+    cats_key = "obs/" + col + "_categories"
+    if codes_key in root and cats_key in root:
+        obs[col] = pd.Categorical.from_codes(root[codes_key][:], root[cats_key][:].astype(str))
+    elif "obs/" + col in root:
+        obs[col] = root["obs/" + col][:].astype(str)
+var = pd.DataFrame(index=root["var/_index"][:].astype(str))
+for col in ["feature_name", "feature_id", "gene_symbol"]:
+    key = "var/" + col
+    if key in root:
+        var[col] = root[key][:].astype(str)
+# Convert to in-memory AnnData (loads full matrix):
+adata = AnnData(X=np.asarray(X), obs=obs, var=var)
+```
+## Scanpy workflow example
+```python
+import scanpy as sc
+sc.pp.normalize_total(adata, target_sum=1e4)
+sc.pp.log1p(adata)
+sc.pp.highly_variable_genes(adata, n_top_genes=2000, flavor="seurat_v3")
+adata = adata[:, adata.var["highly_variable"]].copy()
+sc.pp.scale(adata, max_value=10)
+sc.tl.pca(adata, svd_solver="arpack")
+sc.pp.neighbors(adata, n_neighbors=15, n_pcs=50)
+sc.tl.umap(adata)
+sc.tl.leiden(adata, resolution=0.5)
+```
+## Streaming benchmark (single 1000×1000 chunk)
+Measured locally while building this dataset:
+- open Zarr group: **{fmt_secs(benchmark.get("open_seconds"))} s**
+- read one `X[chunk]` (1000×1000): **{fmt_secs(benchmark.get("read_chunk_seconds"))} s**
+```json
+{json.dumps(benchmark, indent=2)}
+```
+## Build metadata
+```json
+{json.dumps(meta, indent=2)}
+```
+## Upload to HuggingFace Hub
+This repo is intended for: `{repo_id}`.
+If you have a HuggingFace token locally, upload with:
+```bash
+python3.11 -m pip install huggingface_hub
+HF_TOKEN=*** python3.11 build_lung_zarr.py --upload --repo-id "{repo_id}"
+```
+"""
+def main() -> None:
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--out-dir", type=str, default=".", help="Output folder (repo root).")
+    ap.add_argument("--zarr-name", type=str, default="lung.zarr", help="Zarr directory name inside out-dir.")
+    ap.add_argument("--repo-id", type=str, default="KokosDev/single-cell-lung-zarr", help="HF repo id to upload to.")
+    ap.add_argument("--max-cells", type=int, default=100_000, help="Subsample to at most this many cells.")
+    ap.add_argument("--seed", type=int, default=0, help="Random seed for subsampling.")
+    ap.add_argument("--n-hvg", type=int, default=0, help="Select this many HVGs (0 = keep all genes).")
+    ap.add_argument("--only-readme", action="store_true", help="Only (re)generate README+summary from an existing zarr store.")
+    ap.add_argument("--upload", action="store_true", help="Upload folder to HuggingFace Hub (requires HF_TOKEN env var).")
+    args = ap.parse_args()
+    out_dir = Path(args.out_dir).resolve()
+    zarr_path = out_dir / args.zarr_name
+    readme_path = out_dir / "README.md"
+    summary_path = out_dir / "dataset_summary.json"
+    compressor = Blosc(cname="zstd", clevel=3, shuffle=Blosc.BITSHUFFLE)
+    if args.only_readme:
+        if not zarr_path.exists():
+            raise SystemExit(f"--only-readme set but zarr store not found: {zarr_path}")
+        root = zarr.open_group(str(zarr_path), mode="r")
+        shape = tuple(map(int, root["X"].shape))
+        obs_group = root["obs"]
+        var_group = root["var"]
+        # cell_type counts from either categorical encoding or plain strings
+        cell_type_counts: Dict[str, int] = {}
+        if "cell_type_codes" in obs_group and "cell_type_categories" in obs_group:
+            codes = obs_group["cell_type_codes"][:].astype(np.int64, copy=False)
+            cats = obs_group["cell_type_categories"][:].astype(str)
+            valid = codes >= 0
+            if valid.any():
+                bc = np.bincount(codes[valid], minlength=len(cats))
+                cell_type_counts = {str(cats[i]): int(bc[i]) for i in range(len(cats)) if bc[i] > 0}
+        elif "cell_type" in obs_group:
+            vals = obs_group["cell_type"][:].astype(str)
+            uniq, cnt = np.unique(vals, return_counts=True)
+            cell_type_counts = {str(u): int(c) for u, c in zip(uniq, cnt)}
+        disease_labels: Iterable[str] = []
+        if "disease_categories" in obs_group:
+            disease_labels = obs_group["disease_categories"][:].astype(str).tolist()
+        elif "disease" in obs_group:
+            disease_labels = np.unique(obs_group["disease"][:].astype(str)).tolist()
+        benchmark = _streaming_benchmark(zarr_path, seed=args.seed)
+        meta = dict(root.attrs)
+        try:
+            meta.update(
+                {
+                    "shape": [shape[0], shape[1]],
+                    "obs_arrays": sorted(list(obs_group.array_keys())),
+                    "var_arrays": sorted(list(var_group.array_keys())),
+                }
+            )
+        except Exception:
+            pass
+        readme = _render_readme(
+            repo_id=args.repo_id,
+            zarr_relpath=args.zarr_name,
+            adata_shape=(shape[0], shape[1]),
+            cell_type_counts=cell_type_counts,
+            disease_labels=disease_labels,
+            benchmark=benchmark,
+            meta=meta,
+        )
+        readme_path.write_text(readme, encoding="utf-8")
+        summary_path.write_text(
+            json.dumps(
+                {
+                    "shape": [shape[0], shape[1]],
+                    "cell_type_counts": cell_type_counts,
+                    "disease_labels": list(disease_labels),
+                    "benchmark": benchmark,
+                    "meta": meta,
+                },
+                indent=2,
+            ),
+            encoding="utf-8",
+        )
+        if args.upload:
+            token = os.environ.get("HF_TOKEN", "")
+            if not token:
+                raise SystemExit("HF_TOKEN env var is required for --upload.")
+            from huggingface_hub import create_repo, upload_folder
+            print(f"[upload] ensuring dataset repo exists: {args.repo_id}", flush=True)
+            create_repo(repo_id=args.repo_id, repo_type="dataset", exist_ok=True, token=token)
+            print(f"[upload] uploading folder: {out_dir}", flush=True)
+            upload_folder(
+                repo_id=args.repo_id,
+                folder_path=str(out_dir),
+                repo_type="dataset",
+                token=token,
+                commit_message="Add CellxGene Census lung Zarr dataset",
+            )
+            print("[upload] done", flush=True)
+        return
+    # 1) Memory-safe fetch:
+    #    - read only obs joinids for lung primary cells
+    #    - subsample joinids to max_cells
+    #    - fetch only that slice with get_anndata(obs_coords=...)
+    #
+    # Also pin to the current stable Census release for reproducibility.
+    census_version = "2025-11-08"
+    print(f"[census] opening soma (version={census_version})", flush=True)
+    census = cellxgene_census.open_soma(census_version=census_version)
+    try:
+        obs_filter = "tissue_general == 'lung' and is_primary_data == True"
+        # get_obs returns a DataFrame indexed by soma_joinid (joinid is the coord)
+        print("[census] fetching lung obs joinids", flush=True)
+        obs_df = cellxgene_census.get_obs(
+            census,
+            organism="Homo sapiens",
+            value_filter=obs_filter,
+            column_names=["cell_type", "disease", "tissue", "sex", "assay"],
+        )
+        joinids = obs_df.index.to_numpy()
+        print(f"[census] lung primary cells: {joinids.shape[0]:,}", flush=True)
+        rng = np.random.default_rng(args.seed)
+        if joinids.shape[0] > args.max_cells:
+            joinids = rng.choice(joinids, args.max_cells, replace=False)
+        print(f"[census] sampling cells: {joinids.shape[0]:,}", flush=True)
+        print("[census] fetching anndata slice (this may take a while)", flush=True)
+        adata = cellxgene_census.get_anndata(
+            census,
+            organism="Homo sapiens",
+            obs_coords=joinids,
+            obs_column_names=["cell_type", "disease", "tissue", "sex", "assay"],
+        )
+    finally:
+        census.close()
+    print(f"[census] fetched anndata: {adata.n_obs:,} x {adata.n_vars:,}", flush=True)
+    # Ensure sparse CSR for efficient slicing
+    if not sp.issparse(adata.X):
+        adata.X = sp.csr_matrix(adata.X)
+    else:
+        adata.X = adata.X.tocsr()
+    # Ensure expected obs dtypes
+    for col in ["cell_type", "disease", "tissue", "sex", "assay"]:
+        if col in adata.obs.columns:
+            adata.obs[col] = adata.obs[col].astype("category")
+    # Keep common var identifiers if present; ensure strings
+    for vcol in ["feature_name", "feature_id", "gene_symbol"]:
+        if vcol in adata.var.columns:
+            adata.var[vcol] = adata.var[vcol].astype(str)
+    # 3) Optionally select HVGs to keep dense X manageable
+    if args.n_hvg and args.n_hvg > 0:
+        import scanpy as sc
+        print(f"[hvg] selecting top {args.n_hvg} HVGs", flush=True)
+        sc.pp.highly_variable_genes(adata, n_top_genes=args.n_hvg, flavor="seurat_v3", subset=True)
+        # Ensure CSR after subsetting
+        adata.X = adata.X.tocsr()
+        print(f"[hvg] after HVG subset: {adata.n_obs:,} x {adata.n_vars:,}", flush=True)
+    # 4) Write Zarr (remove existing)
+    if zarr_path.exists():
+        import shutil
+        shutil.rmtree(zarr_path)
+    root = zarr.open_group(str(zarr_path), mode="w")
+    root.attrs.update(
+        {
+            "created_at": _utc_now_iso(),
+            "schema_version": "1.0",
+            "source": "cellxgene-census",
+            "organism": "Homo sapiens",
+            "census_version": "2025-11-08",
+            "obs_value_filter": "tissue_general == 'lung' and is_primary_data == True",
+            "max_cells": int(args.max_cells),
+            "seed": int(args.seed),
+            "n_hvg": int(args.n_hvg),
+            "x_chunks": [1000, 1000],
+            "x_compression": {"codec": "blosc", "cname": "zstd", "clevel": 3, "shuffle": "bitshuffle"},
+        }
+    )
+    _write_x_dense_chunked(root, adata.X, shape=(int(adata.n_obs), int(adata.n_vars)), chunks=(1000, 1000), compressor=compressor)
+    _write_table_columns(root, adata.obs, compressor=compressor, prefix="obs")
+    _write_table_columns(root, adata.var, compressor=compressor, prefix="var")
+    # Consolidate metadata for faster remote opening
+    print("[zarr] consolidating metadata", flush=True)
+    zarr.consolidate_metadata(str(zarr_path))
+    print("[zarr] done", flush=True)
+    # 5) Compute README stats
+    cell_type_counts = (
+        {str(k): int(v) for k, v in adata.obs["cell_type"].value_counts(dropna=False).to_dict().items()}
+        if "cell_type" in adata.obs.columns
+        else {}
+    )
+    disease_labels = (
+        list(adata.obs["disease"].cat.categories.astype(str))
+        if "disease" in adata.obs.columns and pd.api.types.is_categorical_dtype(adata.obs["disease"])
+        else []
+    )
+    benchmark = _streaming_benchmark(zarr_path, seed=args.seed)
+    meta = dict(root.attrs)
+    meta.update(
+        {
+            "shape": [int(adata.n_obs), int(adata.n_vars)],
+            "obs_columns": list(map(str, adata.obs.columns)),
+            "var_columns": list(map(str, adata.var.columns)),
+        }
+    )
+    readme = _render_readme(
+        repo_id=args.repo_id,
+        zarr_relpath=args.zarr_name,
+        adata_shape=(int(adata.n_obs), int(adata.n_vars)),
+        cell_type_counts=cell_type_counts,
+        disease_labels=disease_labels,
+        benchmark=benchmark,
+        meta=meta,
+    )
+    readme_path.write_text(readme, encoding="utf-8")
+    summary_path.write_text(
+        json.dumps(
+            {
+                "shape": [int(adata.n_obs), int(adata.n_vars)],
+                "cell_type_counts": cell_type_counts,
+                "disease_labels": disease_labels,
+                "benchmark": benchmark,
+                "meta": meta,
+            },
+            indent=2,
+        ),
+        encoding="utf-8",
+    )
+    # 6) Upload (optional, safe: token must come from env)
+    if args.upload:
+        token = os.environ.get("HF_TOKEN", "")
+        if not token:
+            raise SystemExit("HF_TOKEN env var is required for --upload.")
+        from huggingface_hub import create_repo, upload_folder
+        print(f"[upload] ensuring dataset repo exists: {args.repo_id}", flush=True)
+        create_repo(repo_id=args.repo_id, repo_type="dataset", exist_ok=True, token=token)
+        print(f"[upload] uploading folder: {out_dir}", flush=True)
+        upload_folder(
+            repo_id=args.repo_id,
+            folder_path=str(out_dir),
+            repo_type="dataset",
+            token=token,
+            commit_message="Add CellxGene Census lung Zarr dataset",
+        )
+        print("[upload] done", flush=True)
+if __name__ == "__main__":
+    main()

dataset_summary.json ADDED Viewed

	@@ -0,0 +1,651 @@

+{
+  "shape": [
+    100000,
+    61497
+  ],
+  "cell_type_counts": {
+    "A2 amacrine cell": 20,
+    "B cell": 669,
+    "B-1a B cell": 4,
+    "B-1b B cell": 5,
+    "B-2 B cell": 9,
+    "BEST4+ colonocyte": 21,
+    "BEST4+ enterocyte": 6,
+    "Bergmann glial cell": 161,
+    "CD14-low, CD16-positive monocyte": 16,
+    "CD14-positive monocyte": 344,
+    "CD14-positive, CD16-low monocyte": 36,
+    "CD14-positive, CD16-positive monocyte": 87,
+    "CD141-positive myeloid dendritic cell": 21,
+    "CD16-negative, CD56-bright natural killer cell, human": 46,
+    "CD16-positive, CD56-dim natural killer cell, human": 285,
+    "CD1c-positive myeloid dendritic cell": 63,
+    "CD34-positive, CD56-positive, CD117-positive common innate lymphoid precursor, human": 2,
+    "CD38-negative naive B cell": 199,
+    "CD4-negative, CD8-negative, alpha-beta intraepithelial T cell": 3,
+    "CD4-positive helper T cell": 301,
+    "CD4-positive, CD25-positive, alpha-beta regulatory T cell": 2,
+    "CD4-positive, alpha-beta T cell": 576,
+    "CD4-positive, alpha-beta cytotoxic T cell": 75,
+    "CD4-positive, alpha-beta memory T cell": 386,
+    "CD4-positive, alpha-beta thymocyte": 23,
+    "CD56-positive, CD161-positive immature natural killer cell, human": 20,
+    "CD8-alpha-beta-positive, alpha-beta intraepithelial T cell": 19,
+    "CD8-positive, alpha-beta T cell": 830,
+    "CD8-positive, alpha-beta cytotoxic T cell": 275,
+    "CD8-positive, alpha-beta memory T cell": 428,
+    "CD8-positive, alpha-beta thymocyte": 6,
+    "Cajal-Retzius cell": 3,
+    "GABAergic amacrine cell": 121,
+    "GABAergic interneuron": 72,
+    "GABAergic neuron": 1589,
+    "IgA plasma cell": 724,
+    "IgA plasmablast": 43,
+    "IgG memory B cell": 24,
+    "IgG plasma cell": 323,
+    "IgG plasmablast": 53,
+    "IgG-negative class switched memory B cell": 69,
+    "Kupffer cell": 147,
+    "L5 extratelencephalic projecting glutamatergic cortical neuron": 112,
+    "Langerhans cell": 10,
+    "M cell of gut": 5,
+    "Merkel cell": 3,
+    "Mueller cell": 760,
+    "OFF retinal ganglion cell": 23,
+    "OFF-bipolar cell": 354,
+    "ON retinal ganglion cell": 10,
+    "ON-bipolar cell": 435,
+    "Purkinje cell": 235,
+    "S cone cell": 1,
+    "Schwann cell": 153,
+    "Schwann cell precursor": 44,
+    "T cell": 1734,
+    "T follicular helper cell": 157,
+    "T follicular regulatory cell": 18,
+    "T-helper 1 cell": 48,
+    "T-helper 17 cell": 53,
+    "TAC3-positive striatal interneuron": 13,
+    "Tc1 cell": 39,
+    "VIP GABAergic cortical interneuron": 11,
+    "abnormal cell": 177,
+    "acinar cell": 8,
+    "acinar cell of salivary gland": 1074,
+    "activated CD4-positive, alpha-beta T cell": 83,
+    "activated CD4-positive, alpha-beta T cell, human": 24,
+    "activated CD8-positive, alpha-beta T cell": 157,
+    "adipocyte": 44,
+    "adipocyte of omentum tissue": 1170,
+    "adult skeletal muscle myoblast": 1,
+    "adventitial cell": 188,
+    "afferent neuron": 28,
+    "alpha-beta T cell": 296,
+    "alveolar macrophage": 1,
+    "amacrine cell": 133,
+    "anterior lens cell": 100,
+    "astrocyte": 1808,
+    "basal cell": 181,
+    "basal cell of epidermis": 28,
+    "basal cell of epithelium of bronchus": 14,
+    "basal cell of prostate epithelium": 1401,
+    "basal cell of urothelium": 157,
+    "basal-myoepithelial cell of mammary gland": 74,
+    "basophil": 1,
+    "basophil mast progenitor cell": 15,
+    "blood cell": 24,
+    "blood vessel endothelial cell": 982,
+    "blood vessel smooth muscle cell": 48,
+    "brain vascular cell": 1,
+    "bronchial goblet cell": 2,
+    "bronchial smooth muscle cell": 4,
+    "bronchus fibroblast of lung": 13,
+    "capillary endothelial cell": 735,
+    "cardiac endothelial cell": 89,
+    "cardiac muscle cell": 18,
+    "cardiac muscle myoblast": 36,
+    "cardiac neuron": 132,
+    "caudal ganglionic eminence derived cortical interneuron": 174,
+    "cell": 8,
+    "cell of skeletal muscle": 29,
+    "central memory CD4-positive, alpha-beta T cell": 518,
+    "central memory CD8-positive, alpha-beta T cell": 73,
+    "central nervous system macrophage": 491,
+    "centrilobular region hepatocyte": 56,
+    "centroblast": 68,
+    "centrocyte": 25,
+    "cerebellar granule cell": 68,
+    "cerebral cortex endothelial cell": 90,
+    "chandelier pvalb GABAergic cortical interneuron": 206,
+    "cholangiocyte": 21,
+    "choroid plexus epithelial cell": 262,
+    "choroid plexus macrophage": 6,
+    "ciliated cell": 224,
+    "ciliated epithelial cell": 52,
+    "class switched memory B cell": 155,
+    "classical monocyte": 1562,
+    "club cell": 90,
+    "club-like cell of the urethral epithelium": 72,
+    "colon endothelial cell": 33,
+    "colon goblet cell": 39,
+    "colon macrophage": 2,
+    "colonocyte": 133,
+    "columnar/cuboidal epithelial cell": 2,
+    "committed oligodendrocyte precursor": 12,
+    "common dendritic progenitor": 10,
+    "common lymphoid progenitor": 6,
+    "common myeloid progenitor": 18,
+    "cone retinal bipolar cell": 182,
+    "conventional dendritic cell": 173,
+    "cortical thymic epithelial cell": 319,
+    "corticomedullary thymic epithelial cell": 118,
+    "cycling T cell": 4,
+    "cycling macrophage": 4,
+    "cycling pulmonary alveolar type 2 cell": 32,
+    "cytotoxic T cell": 105,
+    "dendritic cell": 383,
+    "dendritic cell, human": 5,
+    "deuterosomal cell": 1,
+    "direct pathway medium spiny neuron": 96,
+    "dopaminergic neuron": 361,
+    "double negative thymocyte": 19,
+    "double-positive, alpha-beta thymocyte": 5,
+    "early colonocyte": 24,
+    "early lymphoid progenitor": 2,
+    "early promyelocyte": 94,
+    "effector CD4-positive, alpha-beta T cell": 180,
+    "effector CD8-positive, alpha-beta T cell": 162,
+    "effector T cell": 9,
+    "effector memory CD4-positive, alpha-beta T cell": 206,
+    "effector memory CD8-positive, alpha-beta T cell": 628,
+    "effector memory CD8-positive, alpha-beta T cell, terminally differentiated": 37,
+    "embryonic blood vessel endothelial progenitor cell": 18,
+    "endothelial cell": 1941,
+    "endothelial cell of arteriole": 53,
+    "endothelial cell of artery": 288,
+    "endothelial cell of coronary artery": 38,
+    "endothelial cell of hepatic sinusoid": 54,
+    "endothelial cell of lymphatic vessel": 350,
+    "endothelial cell of pericentral hepatic sinusoid": 163,
+    "endothelial cell of periportal hepatic sinusoid": 52,
+    "endothelial cell of uterus": 49,
+    "endothelial cell of vascular tree": 45,
+    "endothelial cell of venule": 436,
+    "endothelial tip cell": 7,
+    "enteric neuron": 66,
+    "enteric smooth muscle cell": 34,
+    "enterocyte": 626,
+    "enterocyte of epithelium proper of ileum": 15,
+    "enteroendocrine cell": 15,
+    "enteroendocrine cell of colon": 2,
+    "enteroglial cell": 173,
+    "eosinophil": 2,
+    "ependymal cell": 144,
+    "epicardial adipocyte": 60,
+    "epithelial cell": 335,
+    "epithelial cell of early distal convoluted tubule": 54,
+    "epithelial cell of glomerular capsule": 2,
+    "epithelial cell of lower respiratory tract": 7,
+    "epithelial cell of lung": 210,
+    "epithelial cell of nephron": 31,
+    "epithelial cell of prostate": 50,
+    "epithelial cell of proximal tubule": 1185,
+    "epithelial cell of proximal tubule segment 3": 42,
+    "epithelial cell of thymus": 3,
+    "epithelial cell of urethra": 122,
+    "erythroblast": 244,
+    "erythrocyte": 499,
+    "erythroid lineage cell": 31,
+    "erythroid progenitor cell": 172,
+    "exhausted T cell": 8,
+    "eye photoreceptor cell": 22,
+    "fallopian tube secretory epithelial cell": 6,
+    "fast muscle cell": 30,
+    "fenestrated endothelial cell": 30,
+    "fetal pre-type II pulmonary alveolar epithelial cell": 39,
+    "fibroblast": 4539,
+    "fibroblast of breast": 143,
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