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.

Dataset Details

The data was extract from the Calico website.

I pulled the 'Raw & processed gene expression data' versions and did some minimal parsing to save the data as a partitioned parquet dataset (see scripts/)

Dataset Structure

The data is stored in a single parquet file which has the following fields.

Field	Description
regulator_locus_tag	induced transcriptional regulator systematic ID
regulator_symbol	induced transcriptional regulator common name. If no common name exists, then the regulator_locus_tag is used.
target_locus_tag	Systmatic ID of the feature to which the induced transcriptional regulator's affect is ascribed
target_symbol	Common name of feature to which the induced transcriptional regulator's affect is ascribed. If there is no common name, the systematic ID is used.
time	time point (minutes)
mechanism	induction system (GEV or ZEV)
restriction	nutrient limitation (M, N or P)
date	date performed
strain	strain name
green_median	Median of green (reference) channel fluorescence
red_median	Median of red (experimental) channel fluorescence
log2_ratio	log2(red / green) subtracting value at time zero
log2_cleaned_ratio	Non-specific stress response and prominent outliers removed
log2_noise_model	Estimated noise standard deviation
log2_cleaned_ratio_zth2d	Cleaned timecourses hard-thresholded based on multiple observations (or last observation) passing the noise model
log2_selected_timecourses	Cleaned timecourses hard-thresholded based on single observations passing noise model and impulse evaluation of biological feasibility
log2_shrunken_timecourses	Selected timecourses with observation-level shrinkage based on local FDR (false discovery rate). Most users of the data will want to use this column.

Usage

I recommend using huggingface_hub.snapshot_download to pull the repository. After that, use your favorite method of interacting with parquet files (eg duckDB, but you could use dplyr in R or pandas, too).

from huggingface_hub import snapshot_download
import duckdb

repo_id = "BrentLab/hackett_2020"

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

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

# Construct path to the parquet file
parquet_path = os.path.join(repo_path, "hackett_2020.parquet")
print(f"Parquet file at: {parquet_path}")

# 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}")

Dataset Author and Contact: Chase Mateusiak @cmatKhan