BrentLab/barkai_compendium

Barkai Compendium

This collects the ChEC-seq data from the following GEO series:

• GSE179430
• GSE209631
• GSE222268

The metadata for each is parsed out from the SraRunTable, or in the case of GSE222268, the NCBI series matrix file (the genotype isn't in the SraRunTable)

The Barkai lab refers to this set as their binding compendium.

The genotypes for GSE222268 are not clear enough to me currently to parse well.

This repo provides 4 datasets:

• GSE178430_metadata: Metadata for GSE178430.
• GSE209631_metadata: ChEC-seq experiment metadata for transcription factor variant studies.
• GSE222268_metadata: General experiment metadata for genomic studies.
• genome_map: Genomic coverage data with pileup counts at specific positions.

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/barkai_compendium" dataset from Hugging Face Hub.

from huggingface_hub import ModelCard
from pprint import pprint

card = ModelCard.load("BrentLab/barkai_compendium", 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)

The entire repository is large. It may be preferrable to only retrieve specific files or partitions. You canuse the metadata files to choose which files to pull.

from huggingface_hub import snapshot_download
import duckdb
import os

# Download only the partitioned dataset directory
repo_path = snapshot_download(
    repo_id="BrentLab/barkai_compendium",
    repo_type="dataset",
    allow_patterns="*metadata.parquet"
)

dataset_path = os.path.join(repo_path, "GSE178430_metadata.parquet")
conn = duckdb.connect()
meta_res = conn.execute("SELECT * FROM read_parquet(?) LIMIT 10", [dataset_path]).df()

print(meta_res)

We might choose to take a look at the file with accession GSM5417602

# Download only the partitioned dataset directory
repo_path = snapshot_download(
    repo_id="BrentLab/barkai_compendium",
    repo_type="dataset",
    allow_patterns="genome_map/series=GSE179430/accession=GSM5417602/*parquet"  # Only the parquet data
)

# Query the specific partition
dataset_path = os.path.join(repo_path, "genome_map")
result = conn.execute("SELECT * FROM read_parquet(?) LIMIT 10", 
                     [f"{dataset_path}/**/*.parquet"]).df()

print(result)

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:

repo_id = "BrentLab/barkai_compendium"

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 genome_map parquet file
parquet_path = os.path.join(repo_path, "genome_map.parquet")
print(f"✓ Parquet file at: {parquet_path}")