title: Large-scale untargeted LC-MS metabolomics data correction using between-batch feature alignment and cluster-based within-batch signal intensity drift correction.
abstract: BACKGROUND
  Liquid chromatography-mass spectrometry (LC-MS) is a commonly used technique in untargeted metabolomics owing to broad coverage of metabolites, high sensitivity and simple sample preparation. However, data generated from multiple batches are affected by measurement errors inherent to alterations in signal intensity, drift in mass accuracy and retention times between samples both within and between batches. These measurement errors reduce repeatability and reproducibility and may thus decrease the power to detect biological responses and obscure interpretation.
  
  
  OBJECTIVE
  Our aim was to develop procedures to address and correct for within- and between-batch variability in processing multiple-batch untargeted LC-MS metabolomics data to increase their quality.
  
  
  METHODS
  Algorithms were developed for: (i) alignment and merging of features that are systematically misaligned between batches, through aggregating feature presence/missingness on batch level and combining similar features orthogonally present between batches; and (ii) within-batch drift correction using a cluster-based approach that allows multiple drift patterns within batch. Furthermore, a heuristic criterion was developed for the feature-wise choice of reference-based or population-based between-batch normalisation.
  
  
  RESULTS
  In authentic data, between-batch alignment resulted in picking 15 % more features and deconvoluting 15 % of features previously erroneously aligned. Within-batch correction provided a decrease in median quality control feature coefficient of variation from 20.5 to 15.1 %. Algorithms are open source and available as an R package ('batchCorr').
  
  
  CONCLUSIONS
  The developed procedures provide unbiased measures of improved data quality, with implications for improved data analysis. Although developed for LC-MS based metabolomics, these methods are generic and can be applied to other data suffering from similar limitations.
authors.affiliation: Department of Food Science, Uppsala BioCenter, Swedish University of Agricultural Sciences, Box 7051, 750 07 Uppsala, Sweden ; Department of Biology and Biological Engineering, Chalmers University of Technology, 412 96 Göteborg, Sweden.
authors.forename: Carl
authors.identifier: 0000-0003-3957-870X
authors.initials: C
authors.lastname: Brunius
authors.affiliation: Department of Food Science, Uppsala BioCenter, Swedish University of Agricultural Sciences, Box 7051, 750 07 Uppsala, Sweden ; Department of Biology and Biological Engineering, Chalmers University of Technology, 412 96 Göteborg, Sweden.
authors.forename: Lin
authors.initials: L
authors.lastname: Shi
authors.affiliation: Department of Food Science, Uppsala BioCenter, Swedish University of Agricultural Sciences, Box 7051, 750 07 Uppsala, Sweden ; Unit of Nutritional Epidemiology, Institute of Environmental Medicine, Karolinska Insitutet, Box 210, 171 77 Stockholm, Sweden ; Department of Biology and Biological Engineering, Chalmers University of Technology, 412 96 Göteborg, Sweden.
authors.forename: Rikard
authors.initials: R
authors.lastname: Landberg
keywords: Batch alignment; Data correction; Drift correction; LC-MS; Metabolomics