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

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 (NOMIS). We demonstrate the method on mouse liver lipidomic profiles using Ultra Performance Liquid Chromatography coupled to high resolution mass spectrometry, and compare its performance to two commonly utilized no...

• Type: Journal
• Year: 2007
• Where: BMCBI
• Authors: Marko Sysi-Aho, Mikko Katajamaa, Laxman Yetukuri, Matej Oresic
• Keywords: BMCBI 2007, Normalization Method, Standard Compounds, Systematic Error