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. 2009 Aug 1;25(15):1930-6.
doi: 10.1093/bioinformatics/btp291. Epub 2009 May 4.

apLCMS--adaptive processing of high-resolution LC/MS data

Tianwei Yu  1 , Youngja Park, Jennifer M Johnson, Dean P Jones
Affiliations

apLCMS--adaptive processing of high-resolution LC/MS data

Tianwei Yu et al. Bioinformatics. .

Abstract

Motivation: Liquid chromatography-mass spectrometry (LC/MS) profiling is a promising approach for the quantification of metabolites from complex biological samples. Significant challenges exist in the analysis of LC/MS data, including noise reduction, feature identification/ quantification, feature alignment and computation efficiency.

Result: Here we present a set of algorithms for the processing of high-resolution LC/MS data. The major technical improvements include the adaptive tolerance level searching rather than hard cutoff or binning, the use of non-parametric methods to fine-tune intensity grouping, the use of run filter to better preserve weak signals and the model-based estimation of peak intensities for absolute quantification. The algorithms are implemented in an R package apLCMS, which can efficiently process large LC/ MS datasets.

Availability: The R package apLCMS is available at www.sph.emory.edu/apLCMS.

Supplementary information: Supplementary data are available at Bioinformatics online.

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Figures

Fig. 1.
Fig. 1.
The general workflow of LC/MS data processing.
Fig. 2.
Fig. 2.
A fraction of a representative LC/FT-MS profile.
Fig. 3.
Fig. 3.
Illustration of the algorithm for finding the m/z tolerance level. (a) Schematic illustration of the mixture model; (b) estimating the rate parameter from a segment of the estimated density (between the vertical lines).
Fig. 4.
Fig. 4.
Workflow of the apLCMS package. Box A, steps for noise removal and feature identification from a single profile; box B, steps for retention time alignment across profiles; box C, steps for feature alignment across profiles.
Fig. 5.
Fig. 5.
Sample plots by the R package apLCMS. (a) The full profile after square-root transformation of the intensities. (b) A fraction of the profile with cube-root transformation of the intensities showing more details. Relative scale is used for the intensity (z) axis. (c) Plot of the EIC of one feature in eight profiles. (d) Plot of EIC of the same feature in a subset of profiles.
Fig. 6.
Fig. 6.
Illustration of the feature detection. (a) Features detected in a representative LC/MS profile; (b) Illustration of the steps of data point grouping, noise removal and feature detection.
See this image and copyright information in PMC

References

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