title: Assessing bias in experiment design for large scale mass spectrometry-based quantitative proteomics.
abstract: 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.
authors.affiliation: Departments of Computer Science and Engineering, University of Washington, Seattle, Washington 98195, USA. amol@cs.washington.edu
authors.forename: Amol
authors.initials: A
authors.lastname: Prakash
authors.forename: Brian
authors.initials: B
authors.lastname: Piening
authors.forename: Jeff
authors.initials: J
authors.lastname: Whiteaker
authors.forename: Heidi
authors.initials: H
authors.lastname: Zhang
authors.forename: Scott A
authors.initials: SA
authors.lastname: Shaffer
authors.forename: Daniel
authors.initials: D
authors.lastname: Martin
authors.forename: Laura
authors.initials: L
authors.lastname: Hohmann
authors.forename: Kelly
authors.initials: K
authors.lastname: Cooke
authors.forename: James M
authors.initials: JM
authors.lastname: Olson
authors.forename: Stacey
authors.initials: S
authors.lastname: Hansen
authors.forename: Mark R
authors.initials: MR
authors.lastname: Flory
authors.forename: Hookeun
authors.initials: H
authors.lastname: Lee
authors.forename: Julian
authors.initials: J
authors.lastname: Watts
authors.forename: David R
authors.initials: DR
authors.lastname: Goodlett
authors.forename: Ruedi
authors.initials: R
authors.lastname: Aebersold
authors.forename: Amanda
authors.initials: A
authors.lastname: Paulovich
authors.forename: Benno
authors.initials: B
authors.lastname: Schwikowski
mesh_terms: D000804:Angiotensin II / Q000494:pharmacology; D000818:Animals; D015982:Bias; D015415:Biomarkers / Q000378:metabolism; D002453:Cell Cycle / Q000187:drug effects; D002853:Chromatography, Liquid; D004195:Disease Models, Animal; D005615:Freezing; D006801:Humans; D006816:Huntington Disease / Q000378:metabolism; D013058:Mass Spectrometry; D051379:Mice; D040901:Proteomics / Q000379:methods*; D015203:Reproducibility of Results; D012107:Research Design; D012441:Saccharomyces cerevisiae / Q000166:cytology / Q000187:drug effects; D013997:Time Factors
chemical_list: D015415:Biomarkers; D000804:Angiotensin II