README.md

metadata

pretty_name: Human Protein Atlas Gene Annotations
license: other
tags:
  - 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 Gene Annotations

The Human Protein Atlas is an open resource mapping human gene and protein expression across tissues, cells, organs, pathology, and subcellular locations using transcriptomics and antibody-based proteomics.

Splits

The split is deterministic by Ensembl ID: sha256(ensembl_id) % 10. Bucket 0 is test; buckets 1 through 9 are train.

Split	Rows
train	18,138
test	2,024
total	20,162

Dataset Statistics

Field	Value
Gene rows	20,162
Cancer prognostic metadata rows	442,504
RNA expression measurement rows	149,150

Usage

pip install datasets

Load all splits:

from datasets import load_dataset

ds = load_dataset("LiteFold/HumanProteinAtlas")
print(ds)
print(ds["train"][0]["gene"])

Load one split:

from datasets import load_dataset

train = load_dataset("LiteFold/HumanProteinAtlas", split="train")

Filter genes with protein-level evidence:

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])

Load metadata tables directly:

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())

Key Columns

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.

Citation

@article{uhlen2015hpa,
  title   = {Tissue-based map of the human proteome},
  author  = {Uhl{\'e}n, Mathias and Fagerberg, Linn and Hallstr{\"o}m, Bj{\"o}rn M. and others},
  journal = {Science},
  volume  = {347},
  number  = {6220},
  pages   = {1260419},
  year    = {2015},
  doi     = {10.1126/science.1260419}
}