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Molecular & Cellular Proteomics 6:1741-1748, 2007.
© 2007 by The American Society for Biochemistry and Molecular Biology, Inc.

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Research

Assessing Bias in Experiment Design for Large Scale Mass Spectrometry-based Quantitative Proteomics^*,S

Amol Prakash^,,¶,||, Brian Piening^,**, Jeff Whiteaker, Heidi Zhang, Scott A. Shaffer, Daniel Martin^,, Laura Hohmann, Kelly Cooke, James M. Olson, Stacey Hansen, Mark R. Flory^¶¶, Hookeun Lee^||||, Julian Watts, David R. Goodlett, Ruedi Aebersold^,||||, Amanda Paulovich and Benno Schwikowski^,¶

From the Departments of Computer Science and Engineering and Medicinal Chemistry and ^** Molecular and Cellular Biology Program, University of Washington, Seattle, Washington 98195, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, Institute for Systems Biology, Seattle, Washington 98103, ^¶ Systems Biology Group, Institut Pasteur, 75015 Paris Cedex 15, France, ^|||| Institute of Molecular Systems Biology, 8093 Zurich, Switzerland, and ^¶¶ Department of Molecular Biology and Biochemistry, Wesleyan University, Middletown, Connecticut 06459

Mass spectrometry-based proteomics holds great promise as a discovery tool for biomarker candidates in the early detection of diseases. Recently much emphasis has been placed upon producing highly reliable data for quantitative profiling for which highly reproducible methodologies are indispensable. The main problems that affect experimental reproducibility stem from variations introduced by sample collection, preparation, and storage protocols and LC-MS settings and conditions. On the basis of a formally precise and quantitative definition of similarity between LC-MS experiments, we have developed Chaorder, a fully automatic software tool that can assess experimental reproducibility of sets of large scale LC-MS experiments. By visualizing the similarity relationships within a set of experiments, this tool can form the basis of systematic quality control and thus help assess the comparability of mass spectrometry data over time, across different laboratories, and between instruments. Applying Chaorder to data from multiple laboratories and a range of instruments, experimental protocols, and sample complexities revealed biases introduced by the sample processing steps, experimental protocols, and instrument choices. Moreover we show that reducing bias by correcting for just a few steps, for example randomizing the run order, does not provide much gain in statistical power for biomarker discovery.

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