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Selection of Features with Consistent Profiles Improves Relative Protein Quantification in Mass Spectrometry Experiments

Institution:	3. Khoury College of Computer Sciences, Northeastern University, Boston, Massachusetts;4. Roche Pharmaceutical Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, Basel, Switzerland;5. Department of Pharmacology, Yale Cancer Biology Institute, Yale University School of Medicine, West Haven, Connecticut;6. Department of Genome Sciences, University of Washington, Seattle, Washington

Abstract:	Download : Download high-res image (187KB) Download : Download full-size image Highlights ?Automated statistical approach for detecting uninformative features and outliers. ?Improved performance on relative protein quantification. ?An option in the open-source R-based software MSstats.

Keywords:	Statistics biostatistics mass spectrometry targeted mass spectrometry computational biology label-free quantification multiple reaction monitoring quantification selected reaction monitoring bioinformatics
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