Assessing bias in experiment design for large scale mass spectrometry-based quantitative proteomics

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, Benno Schwikowski

Research output: Contribution to journalArticlepeer-review

44 Scopus citations


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 Cha-order, 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.

Original languageEnglish (US)
Pages (from-to)1741-1748
Number of pages8
JournalMolecular and Cellular Proteomics
Issue number10
StatePublished - Oct 2007
Externally publishedYes

ASJC Scopus subject areas

  • Analytical Chemistry
  • Biochemistry
  • Molecular Biology


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