Systematic ratio normalization of gas chromatography signals for biological sample discrimination and biomarker discovery

The present paper introduces a new gas chromatography data processing procedure dubbed systematic ratio normalization (SRN) enabling to improve both sample set discrimination and biomarker identification. SRN consists in (1) calculating, for each sample, all the log-ratios between abundances of chromatography-analyzed compounds, then (2) selecting the log-ratio(s) that best maximize the discrimination between sample-sets. The relevance of SRN was evaluated on two data sets acquired through gas chromatography–mass spectrometry as part of separate studies designed (i) to discriminate source-origins between vegetable oils analyzed via an analytical system exposed to instrument drift (data set 1) and (ii) to discriminate animal feed between meat samples aged for different durations (data set 2). Applying SRN to raw data made it possible to obtain robust discrimination models for the two data sets by enhancing the contribution to the data variance of the factor-of-interest while stabilizing the contribution of the disturbance factor. The most discriminant log-ratios were shown to employ the most relevant biomarkers presenting relative independence of the factor-of-interest as well as co-behavior of the disturbance effects potentially biasing the discrimination, such as instrument drift or sample biochemical changes. SRN can be run a posteriori on any data set, and might be generalizable to most of separating methods.

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► We developed a novel processing procedure of GC data dubbed systematic ratio normalization (SRN).
► SRN consists in selecting the most discriminative inter-compound log-ratios.
► SRN enables to improve the discrimination of groups of biological samples.
► SRN uncovers the most biologically relevant biomarkers.
► SRN emphasizes the factor-of-interest while diminishing the weight of disturbance factors.