Common components and specific weights analysis: A tool for metabolomic data pre-processing

The metabolomic approach using LC-MS analyses suffers from substantial intensity variability which must be corrected before extracting useful biological information. In this paper, Common Components and Specific Weights Analysis (CCSWA) is proposed as a novel method for the correction of this analytical bias. This method was compared to LOESS normalisation for within-batch correction and to the median of the quality controls for between-batch correction. In the first case, the correction of a non-continuous effect in the batch was investigated using both LOESS signal correction and CCSWA on fish samples. In the second case, four batches were analysed and combined to create a larger cohort of honey samples. CCSWA was successfully applied to correct both within- and between-batch effects observed in the LC-MS signals. (C) 2015 Elsevier B.V. All rights reserved.