pyhealth.datasets.ClinVarDataset#

The ClinVar dataset provides information about genomic variants and their clinical significance based on ACMG/AMP guidelines. For more information see ClinVar. This dataset was contributed as part of the Prostate-VarBench benchmarking work (arXiv:2511.09576).

class pyhealth.datasets.ClinVarDataset(root, tables=None, dataset_name=None, config_path=None, **kwargs)[source]#

Bases: BaseDataset

ClinVar dataset for variant classification.

ClinVar is a freely accessible, public archive of reports of the relationships among human variations and phenotypes, with supporting evidence. This dataset enables variant pathogenicity prediction tasks.

Dataset is available at: https://ftp.ncbi.nlm.nih.gov/pub/clinvar/

Parameters:

root (str) – Root directory of the raw data containing the ClinVar files.
tables (List[str]) – Optional list of additional tables to load beyond defaults.
dataset_name (Optional[str]) – Optional name of the dataset. Defaults to “clinvar”.
config_path (Optional[str]) – Optional path to the configuration file. If not provided, uses the default config in the configs directory.

root#: Root directory of the raw data.

dataset_name#: Name of the dataset.

config_path#: Path to the configuration file.

Examples

>>> from pyhealth.datasets import ClinVarDataset
>>> dataset = ClinVarDataset(root="/path/to/clinvar")
>>> dataset.stats()
>>> samples = dataset.set_task()
>>> print(samples[0])

static prepare_metadata(root)[source]#

Prepare metadata for the ClinVar dataset.

Converts raw ClinVar variant_summary.txt to standardized CSV format.

Parameters:: root (str) – Root directory containing the ClinVar files.
Return type:: None

property default_task#

Returns the default task for this dataset.

Returns:: The default classification task.
Return type:: VariantClassificationClinVar

clean_tmpdir()#

Cleans up the temporary directory within the cache.

Return type:: None

create_tmpdir()#

Creates and returns a new temporary directory within the cache.

Returns:: The path to the new temporary directory.
Return type:: Path

get_patient(patient_id)#

Retrieves a Patient object for the given patient ID.

Parameters:: patient_id (str) – The ID of the patient to retrieve.
Returns:: The Patient object for the given ID.
Return type:: Patient
Raises:: AssertionError – If the patient ID is not found in the dataset.

property global_event_df: LazyFrame#

Returns the path to the cached event dataframe.

Returns:: The path to the cached event dataframe.
Return type:: Path

iter_patients(df=None)#

Yields Patient objects for each unique patient in the dataset.

Yields:: Iterator[Patient] – An iterator over Patient objects.
Return type:: Iterator[Patient]

load_data()#

Loads data from the specified tables.

Returns:: A concatenated lazy frame of all tables.
Return type:: dd.DataFrame

load_table(table_name)#

Loads a table and processes joins if specified.

Parameters:

table_name (str) – The name of the table to load.

Returns:

The processed Dask dataframe for the table.

Return type:

dd.DataFrame

Raises:

ValueError – If the table is not found in the config.
FileNotFoundError – If the source file (CSV/TSV or Parquet) for the table or join is not found.

set_task(task=None, num_workers=None, input_processors=None, output_processors=None, split=None)#

Processes the base dataset to generate the task-specific sample dataset. The cache structure is as follows:

{task_name}_{task_uuid}/        # Cached data for specific task based on task name, schema, and args
    task_df.ld/                 # Intermediate task dataframe based on schema
    samples_{proc_uuid}.ld/     # Final processed samples after applying processors
        schema.pkl              # Saved SampleBuilder schema
        split.npz               # Sample indices per split part (with split= only)
        *.bin                   # Processed sample files

Parameters:

task (Optional[BaseTask]) – The task to set. Uses default task if None.
num_workers (int) – Number of workers for multi-threading. Default is self.num_workers.
input_processors (Optional[Dict[str, FeatureProcessor]]) – Pre-fitted input processors. If provided, these will be used instead of creating new ones from task’s input_schema. Defaults to None.
output_processors (Optional[Dict[str, FeatureProcessor]]) – Pre-fitted output processors. If provided, these will be used instead of creating new ones from task’s output_schema. Defaults to None.
split (Optional[Split]) – Split the samples, e.g. by patient with PatientSplit, and fit every processor on the first (training) part only, so the other parts never shape preprocessing. The parts are used as the split returns them. Samples are streamed; nothing is loaded into memory beyond the per-sample index that processing already keeps. Defaults to None: fit on all samples and return one dataset, as before.

Returns:

The generated sample dataset, or, with split, a tuple with one dataset per part, training part first, whose processors were fitted on the training part.

Return type:

SampleDataset

Examples

>>> from pyhealth.datasets import PatientSplit
>>> train, val, test = dataset.set_task(  
...     task, split=PatientSplit(ratios=(0.7, 0.1, 0.2), seed=42)
... )

Raises:: AssertionError – If no default task is found and task is None.

stats()#

Prints statistics about the dataset.

Return type:: None

property unique_patient_ids: List[str]#

Returns a list of unique patient IDs.

Returns:: List of unique patient IDs.
Return type:: List[str]