Normalization of metabolomics data using multiple internal standards.

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 pre-processing. 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. 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. The method is demonstrated on mouse liver lipidomic profiles using Ultra Performance Liquid Chromatography
coupled to high resolution mass spectrometry. We compared its performance to two commonly utilized normalization methods: normalization by l2 vector norm and by retention time region specific standard compound profiles. Our approach proved superior in its ability to reduce the effect of systematic error across the full spectrum of metabolite peaks.