BrentLab/kemmeren_2014

Kemmeren 2014

This Dataset is a parsed version of the data provided by the Holstege lab. Both the original mutant/wt effect (M), and the effect after removal of an inferred cell cycle signal (Madj), are provided in the data. The paper describing the entire data set is:

Kemmeren P, Sameith K, van de Pasch LA, Benschop JJ, Lenstra TL, Margaritis T, O'Duibhir E, Apweiler E, van Wageningen S, Ko CW, van Heesch S, Kashani MM, Ampatziadis-Michailidis G, Brok MO, Brabers NA, Miles AJ, Bouwmeester D, van Hooff SR, van Bakel H, Sluiters E, Bakker LV, Snel B, Lijnzaad P, van Leenen D, Groot Koerkamp MJ, Holstege FC. Large-scale genetic perturbations reveal regulatory networks and an abundance of gene-specific repressors. Cell. 2014 Apr 24;157(3):740-52. doi: 10.1016/j.cell.2014.02.054. PMID: 24766815.

And the paper describing the removal of the cell cycle effect is:

O'Duibhir E, Lijnzaad P, Benschop JJ, Lenstra TL, van Leenen D, Groot Koerkamp MJ, Margaritis T, Brok MO, Kemmeren P, Holstege FC. Cell cycle population effects in perturbation studies. Mol Syst Biol. 2014 Jun 21;10(6):732. doi: 10.15252/msb.20145172. PMID: 24952590; PMCID: PMC4265054.

This repo provides 1 dataset:

• kemmeren_2014: Transcriptional regulator overexpression perturbation data with differential expression measurements.

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/kemmeren_2014" dataset from Hugging Face Hub.

from huggingface_hub import ModelCard
from pprint import pprint

card = ModelCard.load("BrentLab/kemmeren_2014", 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/kemmeren_2014"

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 kemmeren_2014 parquet file
parquet_path = os.path.join(repo_path, "kemmeren_2014.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 = 'YJR060W'
"""

result = conn.execute(query, [parquet_path]).fetchall()
print(f"Found {len(result)} rows for YJR060W")

NOTE: There are some loci with multiple probes (multiple_probes == TRUE). One strategy to deduplicate these is to take the max M, Madj and min pval.