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.

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.

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/kemmeren_2014")
print([c.config_name for c in card.configs])  # list available datasets
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("kemmeren"))
vdb.query("SELECT * FROM kemmeren 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/kemmeren_2014",
    repo_type="dataset",
    allow_patterns="kemmeren_2014.parquet",
)
conn = duckdb.connect()
# returns a pandas DataFrame with the first 5 rows
conn.execute(
    "SELECT * FROM read_parquet(?) LIMIT 5",
    [f"{repo_path}/kemmeren_2014.parquet"],
).df()

Accessing using R

Clone the repository and read parquet files directly with arrow:

# install.packages("arrow")
arrow::read_parquet("kemmeren_2014.parquet")