title: QPROT: Statistical method for testing differential expression using protein-level intensity data in label-free quantitative proteomics.
abstract: We introduce QPROT, a statistical framework and computational tool for differential protein expression analysis using protein intensity data. QPROT is an extension of the QSPEC suite, originally developed for spectral count data, adapted for the analysis using continuously measured protein-level intensity data. QPROT offers a new intensity normalization procedure and model-based differential expression analysis, both of which account for missing data. Determination of differential expression of each protein is based on the standardized Z-statistic based on the posterior distribution of the log fold change parameter, guided by the false discovery rate estimated by a well-known Empirical Bayes method. We evaluated the classification performance of QPROT using the quantification calibration data from the clinical proteomic technology assessment for cancer (CPTAC) study and a recently published Escherichia coli benchmark dataset, with evaluation of FDR accuracy in the latter.
  
  
  CONCLUSIONS
  QPROT is a statistical framework with computational software tool for comparative quantitative proteomics analysis. It features various extensions of QSPEC method originally built for spectral count data analysis, including probabilistic treatment of missing values in protein intensity data. With the increasing popularity of label-free quantitative proteomics data, the proposed method and accompanying software suite will be immediately useful for many proteomics laboratories. This article is part of a Special Issue entitled: Computational Proteomics.
authors.affiliation: Saw Swee Hock School of Public Health, National University of Singapore.
authors.forename: Hyungwon
authors.initials: H
authors.lastname: Choi
authors.affiliation: Department of Biostatistics, Rutgers University.
authors.forename: Sinae
authors.initials: S
authors.lastname: Kim
authors.affiliation: Department of Pathology, Yale University.
authors.forename: Damian
authors.initials: D
authors.lastname: Fermin
authors.affiliation: Department of Pathology, University of Michigan Medical School.
authors.forename: Chih-Chiang
authors.initials: CC
authors.lastname: Tsou
authors.affiliation: Departments of Pathology and Computational Medicine and Bioinformatics, University of Michigan Medical School.
authors.forename: Alexey I
authors.initials: AI
authors.lastname: Nesvizhskii
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D020869:Gene Expression Profiling / Q000379:methods*; D015233:Models, Statistical; D008969:Molecular Sequence Data; D020543:Proteome / Q000737:chemistry* / Q000378:metabolism*; D020539:Sequence Analysis, Protein / Q000379:methods*; D013194:Staining and Labeling
chemical_list: D020543:Proteome
keywords: Continuously normalized spectral counts; Differential expression; Intensity; Missing data