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Biostatistics Advance Access originally published online on July 31, 2006
Biostatistics 2007 8(2):357-367; doi:10.1093/biostatistics/kxl015
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© 2006 The Authors
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

A statistical method for chromatographic alignment of LC-MS data

Pei Wang*,{dagger}, Hua Tang, Matthew P. Fitzgibbon and Martin Mcintosh

Fred Hutchinson Cancer Research Center, 1100 Fairveiw Avenue N, M2-B500, PO Box 19204, Seattle, WA, USA pwang{at}fhcrc.org

Marc Coram{dagger}

Department of Statistics, University of Chicago, Chicago, IL, USA

Hui Zhang, Eugene Yi and Ruedi Aebersold

Institute for System Biology, Seattle, WA, USA

* To whom correspondence should be addressed.

Integrated liquid-chromatography mass-spectrometry (LC-MS) is becoming a widely used approach for quantifying the protein composition of complex samples. The output of the LC-MS system measures the intensity of a peptide with a specific mass-charge ratio and retention time. In the last few years, this technology has been used to compare complex biological samples across multiple conditions. One challenge for comparative proteomic profiling with LC-MS is to match corresponding peptide features from different experiments. In this paper, we propose a new method—Peptide Element Alignment (PETAL) that uses raw spectrum data and detected peak to simultaneously align features from multiple LC-MS experiments. PETAL creates spectrum elements, each of which represents the mass spectrum of a single peptide in a single scan. Peptides detected in different LC-MS data are aligned if they can be represented by the same elements. By considering each peptide separately, PETAL enjoys greater flexibility than time warping methods. While most existing methods process multiple data sets by sequentially aligning each data set to an arbitrarily chosen template data set, PETAL treats all experiments symmetrically and can analyze all experiments simultaneously. We illustrate the performance of PETAL on example data sets.

Keywords: Alignment; LC-MS; Regression; Retention time

{dagger} Equal contributors.

Received December 20, 2005; revised May 26, 2006; revised July 11, 2006; accepted for publication July 13, 2006.


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