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

• Published in: Chemometrics and intelligent laboratory systems Vol. 150; pp. 41 - 50
• Main Authors: Dubin, Elodie, Spiteri, Marc, Dumas, Anne-Sophie, Ginet, Jérôme, Lees, Michèle, Rutledge, Douglas N.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 15.01.2016; Elsevier
• Subjects: Common Components and Specific Weights Analysis; Intensity normalisation; LC-TOF-MS; Life Sciences; Metabolomics; Pre-processing; CC; SVD; Metabolomics; RT; Common Components and Specific Weights Analysis; Pre-processing; PQN; PCA; VSN; LOESS; CCSWA; LC-MS; NMR; QC; PC; UPLC; LC-TOF-MS; FWHM; QuEChERS; TOF-MS; ESI; Intensity normalisation; OSC; pre-processing; analysis; metabolomics; common components and specific weights
• ISSN: 0169-7439; 1873-3239
• DOI: 10.1016/j.chemolab.2015.11.005

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. •CCSWA was proposed to correct within- and between-batch bias of LC-MS analyses.•CCSWA was compared to LOESS and QC normalisation.•Method was successfully applied on honey and fish samples.