dbGaPCheckup

dbGaPCheckup validates and prepares phenotype datasets and corresponding data dictionaries for submission to the National Center for Biotechnology Information's Database of Genotypes and Phenotypes (dbGaP) by checking formatting and data integrity.

Key Features:

• Data Integrity Checks: Implements checks required by dbGaP guidelines to verify presence of required fields in the data dictionary, ensure consistency between dataset and dictionary, match variable numbers and names, and detect duplicated entries.
• Logical Value Verification: Validates observed data values against logical minimum and maximum thresholds defined in the data dictionary.
• Automated Error Correction: Provides utility functions to perform minor and scalable corrections to discrepancies, including reordering variables in the data dictionary to align with the dataset.
• Descriptive Reporting: Generates textual and graphical descriptive reports of datasets to summarize content and highlight potential issues prior to submission.

Scientific Applications:

• dbGaP Submission Preparation: Prepares genotype and phenotype study datasets for compliance with dbGaP submission requirements.
• Large-scale Study Data Curation: Supports data curation and quality control for large-scale genotype and phenotype studies by automating integrity checks.
• Reproducibility and Data Sharing: Ensures datasets are accurately formatted and complete to support reproducibility, collaboration, and validation.

Methodology:

The package executes automated checks that align with dbGaP submission instructions by comparing datasets to their data dictionaries, validating values against dictionary-defined logical minima and maxima, detecting duplicated or mismatched variables, and applying utility functions (e.g., variable reordering) to correct discrepancies; these processes are implemented in R.