BrentLab/hackett_2020

Hackett 2020

This Dataset is a parsed version of the data provided by Calicolabs under the heading "Raw & processed gene expression data". See scripts/ for more details on the parsing from the data provided by Calico to this Dataset.

Hackett SR, Baltz EA, Coram M, Wranik BJ, Kim G, Baker A, Fan M, Hendrickson DG, Berndl M, McIsaac RS. Learning causal networks using inducible transcription factors and transcriptome-wide time series. Mol Syst Biol. 2020 Mar;16(3):e9174. doi: 10.15252/msb.20199174. PMID: 32181581; PMCID: PMC7076914.

This repo provides 1 dataset:

• hackett_2020: TF overexpression data from Hackett 2020.

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/hackett_2020" dataset from Hugging Face Hub.

from huggingface_hub import ModelCard
from pprint import pprint

card = ModelCard.load("BrentLab/hackett_2020", 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/hackett_2020"

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 hackett_2020 parquet file
parquet_path = os.path.join(repo_path, "hackett_2020.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 DISTINCT time, mechanism, restriction, date
FROM read_parquet(?)
WHERE regulator_symbol = 'ACA1'
"""
result = conn.execute(query, [parquet_path]).df()
print(f"Found {result}")