Assessing bias in experiment design for large scale mass spectrometry-based quantitative proteomics.
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.
Prakash
Amol
A
Departments of Computer Science and Engineering, University of Washington, Seattle, Washington 98195, USA. amol@cs.washington.edu
Piening
Brian
B
Whiteaker
Jeff
J
Zhang
Heidi
H
Shaffer
Scott A
SA
Martin
Daniel
D
Hohmann
Laura
L
Cooke
Kelly
K
Olson
James M
JM
Hansen
Stacey
S
Flory
Mark R
MR
Lee
Hookeun
H
Watts
Julian
J
Goodlett
David R
DR
Aebersold
Ruedi
R
Paulovich
Amanda
A
Schwikowski
Benno
B
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
D015415:Biomarkers; D000804:Angiotensin II