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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:
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  • 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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