BrentLab/harbison_2004

Harbison 2004

This Dataset is a parsed version of the data provided by Richard A. Young's lab at their website. DOI and citation are in the dataset card above, or see the ArrayExpress entry

Accessing Data

The examples below require labretriever (pip install labretriever) and/or the HuggingFace Hub client (pip install huggingface_hub).

Accessing Data with labretriever

This repository is part of a collection configured as a unified database using labretriever.VirtualDB. Download the collection config and use it to query the data directly in Python, or with an AI assistant using the labretriever plugin.

from labretriever.virtual_db import VirtualDB
from labretriever.datacard import DataCard

# Citation and metadata
card = DataCard("BrentLab/harbison_2004")
info = card.info()
print(info["doi"])
print(info["citation"])

# path to the downloaded brentlab_yeast_collection.yaml
vdb = VirtualDB("/path/to/brentlab_yeast_collection.yaml")

print(vdb.get_dataset_description("harbison"))
vdb.query("SELECT * FROM harbison LIMIT 5")

Direct parquet access

The repository contains more data than what is exposed through the collection configuration. Use DataCard.info() to inspect available files, then download and query with DuckDB.

Most files in this repository are single parquet files and can be read directly:

from huggingface_hub import snapshot_download
import duckdb

repo_path = snapshot_download(
    repo_id="BrentLab/harbison_2004",
    repo_type="dataset",
    allow_patterns="harbison_2004.parquet",
)
conn = duckdb.connect()
# returns a pandas DataFrame with the first 5 rows
conn.execute(
    "SELECT * FROM read_parquet(?) LIMIT 5",
    [f"{repo_path}/harbison_2004.parquet"],
).df()

Accessing using R

Clone the repository and read parquet files directly with arrow:

# install.packages("arrow")
arrow::read_parquet("harbison_2004.parquet")