Add normalized Parquet train/test Human Protein Atlas table

README.md

@@ -1,83 +1,134 @@
 ---
-license: cc-by-sa-4.0
-pretty_name: Human Protein Atlas
-size_categories:
-  - 10K<n<100K
-task_categories:
-  - other
-language:
-  - en
+pretty_name: Human Protein Atlas Gene Annotations
+license: other
 tags:
-  - biology
-  - proteins
-  - annotation
-  - human-protein-atlas
-  - jsonl
+- biology
+- proteins
+- human
+- human-protein-atlas
+- gene-expression
+- subcellular-localization
+- parquet
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*.parquet
+  - split: test
+    path: data/test-*.parquet
 ---
 
-# Human Protein Atlas
+# Human Protein Atlas Gene Annotations
 
-Per-protein annotations from the Human Protein Atlas (proteinatlas.json), normalized to newline-delimited JSON with row-level provenance.
+This dataset contains a viewer-friendly gene-level Parquet table derived from the Human Protein Atlas JSONL source in this repository. Each row is one gene/protein entry. Repeated cancer-prognostic and RNA expression measurements are available as metadata tables.
 
-Processed and uploaded by the [MegaData](https://github.com/) post-download pipeline
-(internal repo). Original source: <https://www.proteinatlas.org/about/download>.
+## Splits
 
-## Statistics
+The split is deterministic by Ensembl ID: `sha256(ensembl_id) % 10`. Bucket `0` is `test`; buckets `1` through `9` are `train`.
 
-| | |
-|---|---|
-| Table files | 1 |
-| Total rows | 20,162 |
-| Total bytes | 148.67 MiB (155,892,614) |
+| Split | Rows |
+|---|---:|
+| train | 18,138 |
+| test | 2,024 |
+| total | 20,162 |
 
-## Tables
+## Dataset Statistics
 
-| Table | Rows | Bytes |
-|---|---:|---:|
-| `annotation_human_protein_atlas_proteinatlas.json.gz.jsonl` | 20,162 | 148.67 MiB |
+| Field | Value |
+|---|---:|
+| Gene rows | 20,162 |
+| Cancer prognostic metadata rows | 442,504 |
+| RNA expression measurement rows | 149,150 |
 
-## Layout
+## Usage
 
-```
-.
-├── _MANIFEST.json                 # aggregate manifest (per-table counts)
-└── tables/<source_slug>.jsonl    # normalized rows (one JSON object per line)
+```bash
+pip install datasets
 ```
 
-Each line in a `tables/*.jsonl` file is a JSON object with at least
-`dataset_id`, `row` (the raw upstream row), `row_index`, and `source_file`
-fields, so every row carries its upstream provenance.
+Load all splits:
 
-## Loading
+```python
+from datasets import load_dataset
 
-```bash
-hf download LiteFold/HumanProteinAtlas --repo-type dataset --local-dir ./human_protein_atlas
+ds = load_dataset("LiteFold/HumanProteinAtlas")
+print(ds)
+print(ds["train"][0]["gene"])
 ```
 
-Programmatic streaming:
+Load one split:
 
 ```python
-import json
-from pathlib import Path
-from huggingface_hub import snapshot_download
-
-local = snapshot_download(repo_id="LiteFold/HumanProteinAtlas", repo_type="dataset")
-for jsonl in sorted(Path(local, "tables").glob("*.jsonl")):
-    with jsonl.open() as f:
-        for line in f:
-            row = json.loads(line)
-            ...  # row["row"] is the upstream record
+from datasets import load_dataset
+
+train = load_dataset("LiteFold/HumanProteinAtlas", split="train")
 ```
 
-## License
+Filter genes with protein-level evidence:
 
-CC BY-SA 4.0 (Human Protein Atlas).
+```python
+from datasets import load_dataset
+
+ds = load_dataset("LiteFold/HumanProteinAtlas", split="train")
+protein_level = ds.filter(lambda row: row["evidence"] == "Evidence at protein level")
+print(protein_level[0])
+```
 
-## Citation
+Load metadata tables directly:
 
-> Uhlen M, et al. Tissue-based map of the human proteome. Science, 347(6220):1260419, 2015.
+```python
+import pandas as pd
+from huggingface_hub import hf_hub_download
+
+prognostics_path = hf_hub_download(
+    repo_id="LiteFold/HumanProteinAtlas",
+    repo_type="dataset",
+    filename="metadata/cancer_prognostics.parquet",
+)
+prognostics = pd.read_parquet(prognostics_path)
+print(prognostics.head())
+
+expression_path = hf_hub_download(
+    repo_id="LiteFold/HumanProteinAtlas",
+    repo_type="dataset",
+    filename="metadata/rna_expression_measurements.parquet",
+)
+expression = pd.read_parquet(expression_path)
+print(expression.head())
+```
 
-## Provenance
+## Key Columns
 
-Built from the local manifest entry `human_protein_atlas` of `manifests/atlas_download_plan.json`.
-Pipeline source: `megadata-post normalize --dataset human_protein_atlas --tables-only`.
+| Column | Description |
+|---|---|
+| `ensembl_id` | Ensembl gene ID. |
+| `gene` | Gene symbol. |
+| `gene_description` | Gene/protein description. |
+| `uniprot` | UniProt accession, when available. |
+| `chromosome` | Chromosome. |
+| `position` | Genomic position string. |
+| `evidence` | Main HPA evidence level. |
+| `hpa_evidence` | HPA-specific evidence level. |
+| `uniprot_evidence` | UniProt evidence level. |
+| `nextprot_evidence` | neXtProt evidence level. |
+| `antibodies` | HPA antibody IDs. |
+| `gene_synonyms` | Gene synonyms. |
+| `protein_classes` | Protein class labels. |
+| `biological_processes` | Biological process annotations. |
+| `molecular_functions` | Molecular function annotations. |
+| `disease_involvement` | Disease involvement labels. |
+| `subcellular_main_locations` | Main subcellular locations. |
+| `subcellular_additional_locations` | Additional subcellular locations. |
+| `secretome_locations` | Secretome location annotations. |
+| `secretome_functions` | Secretome function annotations. |
+| `rna_tissue_specificity` | RNA tissue specificity category. |
+| `rna_tissue_distribution` | RNA tissue distribution category. |
+| `prognostic_cancer_count` | Number of cancers where the gene is prognostic. |
+| `validated_prognostic_cancer_count` | Number of validated prognostic cancer entries. |
+| `potential_prognostic_cancer_count` | Number of potential prognostic cancer entries. |
+| `prognostic_cancers` | Cancer names with prognostic entries. |
+| `split_bucket` | Deterministic split bucket from `sha256(ensembl_id) % 10`. |
+
+## Preparation
+
+The normalization script used to create the Parquet files is included at `scripts/prepare_hpa_dataset.py`.

data/test-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:94e1aa6381607bf895a4c4d0493485e44bde2a9b93e9689c11daeef0d134e1e1
+size 234848

data/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2269abf21bc9f3f570bfb81647703955161296da985941a88e7e8c291dfd5a0c
+size 1646557

dataset_summary.json

@@ -0,0 +1,127 @@
+{
+  "source": "LiteFold/HumanProteinAtlas",
+  "entry_rows": 20162,
+  "cancer_prognostic_rows": 442504,
+  "rna_expression_measurement_rows": 149150,
+  "splits": {
+    "train": 18138,
+    "test": 2024
+  },
+  "split_strategy": "deterministic sha256(ensembl_id) % 10; bucket 0 is test, buckets 1-9 are train",
+  "evidence_counts": {
+    "Evidence at protein level": 18564,
+    "Evidence at transcript level": 1344,
+    "No human protein/transcript evidence": 254
+  },
+  "rna_tissue_specificity_counts": {
+    "Low tissue specificity": 8096,
+    "Tissue enhanced": 6356,
+    "Tissue enriched": 3132,
+    "Group enriched": 1547,
+    "Not detected": 1031
+  },
+  "top_subcellular_main_locations": {
+    "Nucleoplasm": 4897,
+    "Cytosol": 3087,
+    "Vesicles": 1661,
+    "Plasma membrane": 1648,
+    "Mitochondria": 974,
+    "Golgi apparatus": 886,
+    "Nucleoli": 638,
+    "Endoplasmic reticulum": 486,
+    "Nuclear speckles": 412,
+    "Nuclear bodies": 326,
+    "Centrosome": 248,
+    "Microtubules": 237,
+    "Nucleoli fibrillar center": 193,
+    "Nuclear membrane": 193,
+    "Cell Junctions": 192,
+    "Mid piece": 151,
+    "Principal piece": 140,
+    "Primary cilium": 140,
+    "Basal body": 139,
+    "Actin filaments": 138
+  },
+  "top_protein_classes": {
+    "Predicted intracellular proteins": 15915,
+    "Predicted membrane proteins": 5573,
+    "Disease related genes": 4906,
+    "Human disease related genes": 4555,
+    "Enzymes": 3777,
+    "Plasma proteins": 3750,
+    "Metabolic proteins": 2882,
+    "Transporters": 2138,
+    "Essential proteins": 2023,
+    "Predicted secreted proteins": 1902,
+    "Potential drug targets": 1757,
+    "Cancer-related genes": 1672,
+    "Transcription factors": 1485,
+    "FDA approved drug targets": 854,
+    "G-protein coupled receptors": 743,
+    "CD markers": 384,
+    "RAS pathway related proteins": 235,
+    "Immunoglobulin genes": 214,
+    "T-cell receptor genes": 196,
+    "Ribosomal proteins": 180
+  },
+  "columns": [
+    "ensembl_id",
+    "gene",
+    "gene_description",
+    "uniprot",
+    "chromosome",
+    "position",
+    "evidence",
+    "hpa_evidence",
+    "uniprot_evidence",
+    "nextprot_evidence",
+    "antibodies",
+    "gene_synonyms",
+    "protein_classes",
+    "biological_processes",
+    "molecular_functions",
+    "disease_involvement",
+    "subcellular_main_locations",
+    "subcellular_additional_locations",
+    "secretome_locations",
+    "secretome_functions",
+    "reliability_if",
+    "reliability_ih",
+    "reliability_mouse_brain",
+    "interactions",
+    "ccd_protein",
+    "ccd_transcript",
+    "blood_expression_cluster",
+    "brain_expression_cluster",
+    "cell_line_expression_cluster",
+    "single_cell_expression_cluster",
+    "tissue_expression_cluster",
+    "rna_tissue_specificity",
+    "rna_tissue_distribution",
+    "rna_tissue_specificity_score",
+    "rna_cell_line_specificity",
+    "rna_cell_line_distribution",
+    "rna_cell_line_specificity_score",
+    "rna_cancer_specificity",
+    "rna_cancer_distribution",
+    "rna_cancer_specificity_score",
+    "rna_single_cell_type_specificity",
+    "rna_single_cell_type_distribution",
+    "rna_single_cell_type_specificity_score",
+    "rna_blood_cell_specificity",
+    "rna_blood_cell_distribution",
+    "rna_blood_cell_specificity_score",
+    "prognostic_cancer_count",
+    "validated_prognostic_cancer_count",
+    "potential_prognostic_cancer_count",
+    "favorable_prognostic_cancers",
+    "unfavorable_prognostic_cancers",
+    "prognostic_cancers",
+    "source_row_index",
+    "split_bucket"
+  ],
+  "metadata_tables": [
+    "metadata/cancer_prognostics.parquet",
+    "metadata/rna_expression_measurements.parquet"
+  ]
+}

metadata/cancer_prognostics.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:eb2be087d1c8e9f2af45770e988c5f1caa75f4ef4f87496b229977a913447aad
+size 1232620

metadata/rna_expression_measurements.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0aaae1c210442b91464ae97102e9efd10b21c68bd9d70c220562107ecc771f8d
+size 872017

scripts/prepare_hpa_dataset.py

@@ -0,0 +1,313 @@
+#!/usr/bin/env python3
+"""Build viewer-friendly Parquet splits for LiteFold/HumanProteinAtlas."""
+
+from __future__ import annotations
+
+import argparse
+import hashlib
+import json
+import re
+import shutil
+from collections import Counter
+from pathlib import Path
+from typing import Any
+
+import pandas as pd
+
+
+ENTRY_COLUMNS = [
+    "ensembl_id",
+    "gene",
+    "gene_description",
+    "uniprot",
+    "chromosome",
+    "position",
+    "evidence",
+    "hpa_evidence",
+    "uniprot_evidence",
+    "nextprot_evidence",
+    "antibodies",
+    "gene_synonyms",
+    "protein_classes",
+    "biological_processes",
+    "molecular_functions",
+    "disease_involvement",
+    "subcellular_main_locations",
+    "subcellular_additional_locations",
+    "secretome_locations",
+    "secretome_functions",
+    "reliability_if",
+    "reliability_ih",
+    "reliability_mouse_brain",
+    "interactions",
+    "ccd_protein",
+    "ccd_transcript",
+    "blood_expression_cluster",
+    "brain_expression_cluster",
+    "cell_line_expression_cluster",
+    "single_cell_expression_cluster",
+    "tissue_expression_cluster",
+    "rna_tissue_specificity",
+    "rna_tissue_distribution",
+    "rna_tissue_specificity_score",
+    "rna_cell_line_specificity",
+    "rna_cell_line_distribution",
+    "rna_cell_line_specificity_score",
+    "rna_cancer_specificity",
+    "rna_cancer_distribution",
+    "rna_cancer_specificity_score",
+    "rna_single_cell_type_specificity",
+    "rna_single_cell_type_distribution",
+    "rna_single_cell_type_specificity_score",
+    "rna_blood_cell_specificity",
+    "rna_blood_cell_distribution",
+    "rna_blood_cell_specificity_score",
+    "prognostic_cancer_count",
+    "validated_prognostic_cancer_count",
+    "potential_prognostic_cancer_count",
+    "favorable_prognostic_cancers",
+    "unfavorable_prognostic_cancers",
+    "prognostic_cancers",
+    "source_row_index",
+    "split_bucket",
+]
+
+
+PROGNOSIS_COLUMNS = [
+    "ensembl_id",
+    "gene",
+    "cancer",
+    "source",
+    "is_prognostic",
+    "p_value",
+    "prognostic",
+    "prognostic_type",
+]
+
+
+EXPRESSION_COLUMNS = [
+    "ensembl_id",
+    "gene",
+    "measurement",
+    "sample",
+    "value",
+]
+
+
+def stable_bucket(value: str, buckets: int = 10) -> int:
+    digest = hashlib.sha256(value.encode("utf-8")).hexdigest()[:16]
+    return int(digest, 16) % buckets
+
+
+def as_list(value: Any) -> list[str]:
+    if value is None or value == "NA":
+        return []
+    if isinstance(value, list):
+        return [str(item) for item in value if item is not None and item != ""]
+    return [str(value)] if value != "" else []
+
+
+def clean_scalar(value: Any) -> Any:
+    if value in {"NA", ""}:
+        return None
+    return value
+
+
+def parse_float(value: Any) -> float | None:
+    if value in {None, "", "NA"}:
+        return None
+    try:
+        return float(value)
+    except (TypeError, ValueError):
+        return None
+
+
+def score(value: Any) -> float | None:
+    return parse_float(value)
+
+
+def normalize_cancer_name(column: str) -> tuple[str, str]:
+    label = column.removeprefix("Cancer prognostics - ")
+    source = "validation" if "(validation)" in label else "TCGA" if "(TCGA)" in label else ""
+    cancer = re.sub(r"\s*\((TCGA|validation)\)\s*$", "", label).strip()
+    return cancer, source
+
+
+def entry_row(wrapper: dict[str, Any]) -> tuple[dict[str, Any], list[dict[str, Any]], list[dict[str, Any]]]:
+    row = wrapper["row"]
+    ensembl = row.get("Ensembl")
+    gene = row.get("Gene")
+    prognosis_rows = []
+    prognostic_cancers = []
+    validated = 0
+    potential = 0
+    favorable = []
+    unfavorable = []
+
+    expression_rows = []
+    for key, value in row.items():
+        if key.startswith("Cancer prognostics -") and isinstance(value, dict):
+            cancer, source = normalize_cancer_name(key)
+            is_prognostic = bool(value.get("is_prognostic"))
+            prognostic = value.get("prognostic") or None
+            prognostic_type = value.get("prognostic type") or None
+            if is_prognostic:
+                prognostic_cancers.append(cancer)
+                if prognostic == "validated prognostic":
+                    validated += 1
+                elif prognostic == "potential prognostic":
+                    potential += 1
+                if prognostic_type == "favorable":
+                    favorable.append(cancer)
+                elif prognostic_type == "unfavorable":
+                    unfavorable.append(cancer)
+            prognosis_rows.append(
+                {
+                    "ensembl_id": ensembl,
+                    "gene": gene,
+                    "cancer": cancer,
+                    "source": source,
+                    "is_prognostic": is_prognostic,
+                    "p_value": parse_float(value.get("p_val")),
+                    "prognostic": prognostic,
+                    "prognostic_type": prognostic_type,
+                }
+            )
+        elif key.startswith("RNA ") and isinstance(value, dict):
+            for sample, measurement in value.items():
+                expression_rows.append(
+                    {
+                        "ensembl_id": ensembl,
+                        "gene": gene,
+                        "measurement": key,
+                        "sample": sample,
+                        "value": parse_float(measurement),
+                    }
+                )
+
+    entry = {
+        "ensembl_id": ensembl,
+        "gene": gene,
+        "gene_description": row.get("Gene description"),
+        "uniprot": row.get("Uniprot"),
+        "chromosome": row.get("Chromosome"),
+        "position": row.get("Position"),
+        "evidence": row.get("Evidence"),
+        "hpa_evidence": row.get("HPA evidence"),
+        "uniprot_evidence": row.get("UniProt evidence"),
+        "nextprot_evidence": row.get("NeXtProt evidence"),
+        "antibodies": as_list(row.get("Antibody")),
+        "gene_synonyms": as_list(row.get("Gene synonym")),
+        "protein_classes": as_list(row.get("Protein class")),
+        "biological_processes": as_list(row.get("Biological process")),
+        "molecular_functions": as_list(row.get("Molecular function")),
+        "disease_involvement": as_list(row.get("Disease involvement")),
+        "subcellular_main_locations": as_list(row.get("Subcellular main location")),
+        "subcellular_additional_locations": as_list(row.get("Subcellular additional location")),
+        "secretome_locations": as_list(row.get("Secretome location")),
+        "secretome_functions": as_list(row.get("Secretome function")),
+        "reliability_if": clean_scalar(row.get("Reliability (IF)")),
+        "reliability_ih": clean_scalar(row.get("Reliability (IH)")),
+        "reliability_mouse_brain": clean_scalar(row.get("Reliability (Mouse Brain)")),
+        "interactions": row.get("Interactions"),
+        "ccd_protein": clean_scalar(row.get("CCD Protein")),
+        "ccd_transcript": clean_scalar(row.get("CCD Transcript")),
+        "blood_expression_cluster": clean_scalar(row.get("Blood expression cluster")),
+        "brain_expression_cluster": clean_scalar(row.get("Brain expression cluster")),
+        "cell_line_expression_cluster": clean_scalar(row.get("Cell line expression cluster")),
+        "single_cell_expression_cluster": clean_scalar(row.get("Single cell expression cluster")),
+        "tissue_expression_cluster": clean_scalar(row.get("Tissue expression cluster")),
+        "rna_tissue_specificity": clean_scalar(row.get("RNA tissue specificity")),
+        "rna_tissue_distribution": clean_scalar(row.get("RNA tissue distribution")),
+        "rna_tissue_specificity_score": score(row.get("RNA tissue specificity score")),
+        "rna_cell_line_specificity": clean_scalar(row.get("RNA cell line specificity")),
+        "rna_cell_line_distribution": clean_scalar(row.get("RNA cell line distribution")),
+        "rna_cell_line_specificity_score": score(row.get("RNA cell line specificity score")),
+        "rna_cancer_specificity": clean_scalar(row.get("RNA cancer specificity")),
+        "rna_cancer_distribution": clean_scalar(row.get("RNA cancer distribution")),
+        "rna_cancer_specificity_score": score(row.get("RNA cancer specificity score")),
+        "rna_single_cell_type_specificity": clean_scalar(row.get("RNA single cell type specificity")),
+        "rna_single_cell_type_distribution": clean_scalar(row.get("RNA single cell type distribution")),
+        "rna_single_cell_type_specificity_score": score(row.get("RNA single cell type specificity score")),
+        "rna_blood_cell_specificity": clean_scalar(row.get("RNA blood cell specificity")),
+        "rna_blood_cell_distribution": clean_scalar(row.get("RNA blood cell distribution")),
+        "rna_blood_cell_specificity_score": score(row.get("RNA blood cell specificity score")),
+        "prognostic_cancer_count": len(set(prognostic_cancers)),
+        "validated_prognostic_cancer_count": validated,
+        "potential_prognostic_cancer_count": potential,
+        "favorable_prognostic_cancers": sorted(set(favorable)),
+        "unfavorable_prognostic_cancers": sorted(set(unfavorable)),
+        "prognostic_cancers": sorted(set(prognostic_cancers)),
+        "source_row_index": wrapper.get("row_index"),
+        "split_bucket": stable_bucket(str(ensembl or gene or wrapper.get("row_index"))),
+    }
+    return entry, prognosis_rows, expression_rows
+
+
+def build_dataset(raw_dir: Path, out_dir: Path) -> dict[str, Any]:
+    source = raw_dir / "tables/annotation_human_protein_atlas_proteinatlas.json.gz.jsonl"
+    rows = []
+    prognosis_rows = []
+    expression_rows = []
+    with source.open("r", encoding="utf-8") as handle:
+        for line in handle:
+            wrapper = json.loads(line)
+            entry, prognosis, expression = entry_row(wrapper)
+            rows.append(entry)
+            prognosis_rows.extend(prognosis)
+            expression_rows.extend(expression)
+
+    if out_dir.exists():
+        shutil.rmtree(out_dir)
+    data_dir = out_dir / "data"
+    metadata_dir = out_dir / "metadata"
+    data_dir.mkdir(parents=True, exist_ok=True)
+    metadata_dir.mkdir(parents=True, exist_ok=True)
+
+    df = pd.DataFrame.from_records(rows, columns=ENTRY_COLUMNS)
+    train = df[df["split_bucket"].ne(0)].sort_values("ensembl_id", kind="mergesort")
+    test = df[df["split_bucket"].eq(0)].sort_values("ensembl_id", kind="mergesort")
+    train.to_parquet(data_dir / "train-00000-of-00001.parquet", index=False, compression="zstd")
+    test.to_parquet(data_dir / "test-00000-of-00001.parquet", index=False, compression="zstd")
+
+    prognosis_df = pd.DataFrame.from_records(prognosis_rows, columns=PROGNOSIS_COLUMNS)
+    prognosis_df.to_parquet(metadata_dir / "cancer_prognostics.parquet", index=False, compression="zstd")
+    expression_df = pd.DataFrame.from_records(expression_rows, columns=EXPRESSION_COLUMNS)
+    expression_df.to_parquet(metadata_dir / "rna_expression_measurements.parquet", index=False, compression="zstd")
+
+    evidence_counts = df["evidence"].fillna("missing").value_counts().to_dict()
+    tissue_specificity_counts = df["rna_tissue_specificity"].fillna("missing").value_counts().to_dict()
+    location_counts = Counter(location for values in df["subcellular_main_locations"] for location in values)
+    protein_class_counts = Counter(item for values in df["protein_classes"] for item in values)
+    summary = {
+        "source": "LiteFold/HumanProteinAtlas",
+        "entry_rows": int(len(df)),
+        "cancer_prognostic_rows": int(len(prognosis_df)),
+        "rna_expression_measurement_rows": int(len(expression_df)),
+        "splits": {"train": int(len(train)), "test": int(len(test))},
+        "split_strategy": "deterministic sha256(ensembl_id) % 10; bucket 0 is test, buckets 1-9 are train",
+        "evidence_counts": {str(k): int(v) for k, v in evidence_counts.items()},
+        "rna_tissue_specificity_counts": {str(k): int(v) for k, v in tissue_specificity_counts.items()},
+        "top_subcellular_main_locations": dict(location_counts.most_common(20)),
+        "top_protein_classes": dict(protein_class_counts.most_common(20)),
+        "columns": ENTRY_COLUMNS,
+        "metadata_tables": [
+            "metadata/cancer_prognostics.parquet",
+            "metadata/rna_expression_measurements.parquet",
+        ],
+    }
+    (out_dir / "dataset_summary.json").write_text(json.dumps(summary, indent=2) + "\n", encoding="utf-8")
+    return summary
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--raw-dir", type=Path, default=Path("LiteFold_HumanProteinAtlas_raw"))
+    parser.add_argument("--out-dir", type=Path, default=Path("LiteFold_HumanProteinAtlas_processed"))
+    args = parser.parse_args()
+    summary = build_dataset(args.raw_dir, args.out_dir)
+    print(json.dumps(summary, indent=2))
+
+
+if __name__ == "__main__":
+    main()