Normalization method for metabolomics data using optimal selection of multiple internal standards.

ABSTRACT:

Background

Success of metabolomics as the phenotyping platform largely depends on its ability to detect various sources of biological variability. Removal of platform-specific sources of variability such as systematic error is therefore one of the foremost priorities in data preprocessing. However, chemical diversity of molecular species included in typical metabolic profiling experiments leads to different responses to variations in experimental conditions, making normalization a very demanding task.

Results

With the aim to remove unwanted systematic variation, we present an approach that utilizes variability information from multiple internal standard compounds to find optimal normalization factor for each individual molecular species detected by metabolomics approach (NOM

SUBMITTER: Sysi-Aho M 

PROVIDER: S-EPMC1838434 | biostudies-literature | 2007 Mar

REPOSITORIES: biostudies-literature