BrentLab/hughes_2006

Hughes 2006

This data is parsed from data presented in

G. Chua, Q.D. Morris, R. Sopko, M.D. Robinson, O. Ryan, E.T. Chan, B.J. Frey, B.J. Andrews, C. Boone, & T.R. Hughes, Identifying transcription factor functions and targets by phenotypic activation, Proc. Natl. Acad. Sci. U.S.A. 103 (32) 12045-12050, https://doi.org/10.1073/pnas.0605140103 (2006).

The data is made available by the author and on NCBI with accession GSE5499. I used the data provided by the author.

Details on my parsing can be found in scripts/. The gene features are from BrentLab/yeast_genome_resources.

This repo provides 3 datasets:

• knockout: Deletion/knockout perturbation normalized log2 fold changes.
• metadata: Transcription factor metadata including essentiality and QC status.
• overexpression: Overexpression perturbation normalized log2 fold changes.

Usage

The python package tfbpapi provides an interface to this data which eases examining the datasets, field definitions and other operations. You may also download the parquet datasets directly from hugging face by clicking on "Files and Versions", or by using the huggingface_cli and duckdb directly. In both cases, this provides a method of retrieving dataset and field definitions.

tfbpapi

After installing tfbpapi, you can adapt this tutorial in order to explore the contents of this repository.

huggingface_cli/duckdb

You can retrieves and displays the file paths for each configuration of the "BrentLab/hughes_2006" dataset from Hugging Face Hub.

from huggingface_hub import ModelCard
from pprint import pprint

card = ModelCard.load("BrentLab/hughes_2006", repo_type="dataset")

# cast to dict
card_dict = card.data.to_dict()

# Get partition information
dataset_paths_dict = {d.get("config_name"): d.get("data_files")[0].get("path") for d in card_dict.get("configs")}

pprint(dataset_paths_dict)

If you wish to pull the entire repo, due to its size you may need to use an authentication token. If you do not have one, try omitting the token related code below and see if it works. Else, create a token and provide it like so:

from huggingface_hub import snapshot_download
import duckdb
import os

repo_id = "BrentLab/hughes_2006"

hf_token = os.getenv("HF_TOKEN")

# Download entire repo to local directory
repo_path = snapshot_download(
    repo_id=repo_id,
    repo_type="dataset",
    token=hf_token
)

print(f"\n✓ Repository downloaded to: {repo_path}")

# Construct path to the knockout parquet file
parquet_path = os.path.join(repo_path, "knockout.parquet")
print(f"✓ Parquet file at: {parquet_path}")

Use your favorite method of interacting with parquet files (eg duckDB, but you could use dplyr in R or pandas, too).

# Connect to DuckDB and query the parquet file
conn = duckdb.connect()

query = """
SELECT * 
FROM read_parquet(?)
WHERE regulator_locus_tag = 'CST6'
"""

result = conn.execute(query, [parquet_path]).fetchall()
print(f"Found {len(result)} rows for CST6")