dbRUSP

dbRUSP provides analysis of newborn screening blood metabolite data to quantify covariate effects on metabolite levels and support identification of candidate markers for metabolic disorders recommended by the Recommended Uniform Screening Panel (RUSP).

Key Features:

• Comprehensive data analysis: Analyzes 41 NBS metabolites and six covariates (gestational age, birth weight, age at blood collection, sex, parent-reported ethnicity, and parenteral nutrition status) that influence metabolite concentrations.
• Modular covariate effect analysis: Implements modules to evaluate individual and joint effects of covariates on metabolite concentrations.
• Personalized reference ranges: Generates individualized reference ranges for metabolite levels conditioned on specified covariates.
• Candidate marker identification: Supports selection of new candidate metabolic markers for detecting RUSP-recommended conditions.

Scientific Applications:

• Interpretation of newborn screening results: Quantifies covariate-driven variability in metabolite levels to refine NBS result interpretation.
• Reduction of false positives: Accounts for covariate influences on metabolite measurements to reduce false positive NBS results.
• Discovery and validation of diagnostic markers: Enables identification and evaluation of candidate metabolic markers for genetic disease detection within the RUSP framework.

Methodology:

Implemented in R using the Shiny package; analyzes 41 NBS metabolites alongside six covariates and provides modules to model individual and joint covariate effects and to generate personalized reference ranges.