BrentLab/callingcards

Calling Cards

This is data produced in both the Brent Lab and Mitra Lab at Washington University.

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/callingcards")
print([c.config_name for c in card.configs])  # list available datasets

# print citation
info = card.info()
print(info["citation"])

# path to the downloaded brentlab_yeast_collection.yaml
vdb = VirtualDB("/path/to/brentlab_yeast_collection.yaml")

print(vdb.get_dataset_description("callingcards"))
vdb.query("SELECT * FROM callingcards 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.

Some files are single parquet files (e.g. metadata files); others are partitioned datasets. Download a metadata file first to identify relevant partitions before fetching the full data.

Single parquet file example:

from huggingface_hub import snapshot_download
import duckdb

repo_path = snapshot_download(
    repo_id="BrentLab/callingcards",
    repo_type="dataset",
    allow_patterns="annotated_feature_meta.parquet",
)
conn = duckdb.connect()
# returns a pandas DataFrame with the first 5 rows
conn.execute(
    "SELECT * FROM read_parquet(?) LIMIT 5",
    [f"{repo_path}/annotated_feature_meta.parquet"],
).df()

Partitioned dataset example (the annotated_feature directory):

repo_path = snapshot_download(
    repo_id="BrentLab/callingcards",
    repo_type="dataset",
    allow_patterns="annotated_feature/**",
)
conn.execute(
    "SELECT * FROM read_parquet(?) LIMIT 5",
    [f"{repo_path}/annotated_feature/**/*.parquet"],
).df()

Accessing using R

Clone the repository and read parquet files directly with arrow:

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