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

被引：19

作者：

Dubin, Elodie ^{[1
]}

Spiteri, Marc ^{[1
]}

Dumas, Anne-Sophie ^{[1
]}

Ginet, Jerome ^{[1
]}

Lees, Michele ^{[1
]}

Rutledge, Douglas N. ^{[2
]}

机构：

[1] Eurofins Analyt France, F-44323 Nantes 3, France

[2] Univ Paris Saclay, AgroParisTech, INRA, UMR Ingn Proc Aliments, F-91300 Massy, France

来源：

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS | 2016年 / 150卷

关键词：

LC-TOF-MS; Metabolomics; Pre-processing; Intensity normalisation; Common Components and Specific Weights; Analysis; LARGE-SCALE; NORMALIZATION;

D O I：

10.1016/j.chemolab.2015.11.005

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：41 / 50

页数：10

共 19 条

[1]

AOAC International, 2010, 99812C4 AOAC INT, P33

[2] Probabilistic quotient normalization as robust method to account for dilution of complex biological mixtures.: Application in 1H NMR metabonomics [J].