title: mapDIA: Preprocessing and statistical analysis of quantitative proteomics data from data independent acquisition mass spectrometry.
abstract: Data independent acquisition (DIA) mass spectrometry is an emerging technique that offers more complete detection and quantification of peptides and proteins across multiple samples. DIA allows fragment-level quantification, which can be considered as repeated measurements of the abundance of the corresponding peptides and proteins in the downstream statistical analysis. However, few statistical approaches are available for aggregating these complex fragment-level data into peptide- or protein-level statistical summaries. In this work, we describe a software package, mapDIA, for statistical analysis of differential protein expression using DIA fragment-level intensities. The workflow consists of three major steps: intensity normalization, peptide/fragment selection, and statistical analysis. First, mapDIA offers normalization of fragment-level intensities by total intensity sums as well as a novel alternative normalization by local intensity sums in retention time space. Second, mapDIA removes outlier observations and selects peptides/fragments that preserve the major quantitative patterns across all samples for each protein. Last, using the selected fragments and peptides, mapDIA performs model-based statistical significance analysis of protein-level differential expression between specified groups of samples. Using a comprehensive set of simulation datasets, we show that mapDIA detects differentially expressed proteins with accurate control of the false discovery rates. We also describe the analysis procedure in detail using two recently published DIA datasets generated for 14-3-3β dynamic interaction network and prostate cancer glycoproteome.
  
  
  BACKGROUND
  The software was written in C++ language and the source code is available for free through SourceForge website http://sourceforge.net/projects/mapdia/.This article is part of a Special Issue entitled: Computational Proteomics.
authors.affiliation: Department of Applied Probability and Statistics, National University of Singapore, Singapore; Saw Swee Hock School of Public Health, National University of Singapore, Singapore.
authors.forename: Guoshou
authors.initials: G
authors.lastname: Teo
authors.affiliation: Department of Biostatistics, School of Public Health, Rutgers University, Piscataway, NJ, USA.
authors.forename: Sinae
authors.initials: S
authors.lastname: Kim
authors.affiliation: Department of Pathology, University of Michigan, Ann Arbor, MI, USA.
authors.forename: Chih-Chiang
authors.initials: CC
authors.lastname: Tsou
authors.affiliation: Department of Biology, Institute of Molecular Systems Biology, ETH Zürich, Zürich, Switzerland.
authors.forename: Ben
authors.initials: B
authors.lastname: Collins
authors.affiliation: Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada.
authors.forename: Anne-Claude
authors.initials: AC
authors.lastname: Gingras
authors.affiliation: Department of Pathology, University of Michigan, Ann Arbor, MI, USA; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
authors.forename: Alexey I
authors.initials: AI
authors.lastname: Nesvizhskii
authors.affiliation: Saw Swee Hock School of Public Health, National University of Singapore, Singapore. Electronic address: hyung_won_choi@nuhs.edu.sg.
authors.forename: Hyungwon
authors.initials: H
authors.lastname: Choi
mesh_terms: D000595:Amino Acid Sequence; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D020869:Gene Expression Profiling / Q000379:methods*; D013058:Mass Spectrometry / Q000379:methods*; D015233:Models, Statistical; D025941:Protein Interaction Mapping / Q000379:methods*; D020543:Proteome / Q000737:chemistry* / Q000378:metabolism*; D040901:Proteomics / Q000379:methods; D020539:Sequence Analysis, Protein / Q000379:methods*
chemical_list: D020543:Proteome
keywords: Data independent acquisition; Data preprocessing; Differential expression; Normalization