hRUV

hRUV normalizes LC-MS metabolomics data by hierarchically removing intra- and inter-batch unwanted variation to preserve biological signal.

Key Features:

• Hierarchical strategy: Normalizes multiple batches in a hierarchical manner to mitigate batch effects in large-scale LC-MS metabolomics studies.
• Utilization of replicates: Leverages intra-batch and inter-batch replicates to estimate and separate within- and between-batch unwanted variation.
• RUV-III methodology: Implements the RUV-III framework using replicate samples to model and remove technical noise.

Scientific Applications:

• Large population cohort metabolomics: Corrects unwanted variation in LC-MS studies spanning weeks to years while preserving true biological signals.
• Longitudinal human plasma studies: Applicable to longitudinal analyses and demonstrated on over 1,000 human plasma samples to quantify and remove temporal technical variance.

Methodology:

Applies a hierarchical normalization workflow that integrates intra- and inter-batch replicates with the RUV-III method to estimate and remove unwanted variation.