Introduction

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.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.

Publications

  1. mapDIA: Preprocessing and statistical analysis of quantitative proteomics data from data independent acquisition mass spectrometry.
    Cite this
    Teo G, Kim S, Tsou CC, Collins B, Gingras AC, Nesvizhskii AI, Choi H, 2015-11-01 - Journal of proteomics

Credits

  1. Guoshou Teo
    Developer

    Department of Applied Probability and Statistics, National University of Singapore, Singapore

  2. Sinae Kim
    Developer

    Department of Biostatistics, School of Public Health, United States of America

  3. Chih-Chiang Tsou
    Developer

    Department of Pathology, University of Michigan, United States of America

  4. Ben Collins
    Developer

    Department of Biology, Institute of Molecular Systems Biology, Switzerland

  5. Anne-Claude Gingras
    Developer

    Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Canada

  6. Alexey I Nesvizhskii
    Developer

    Department of Pathology, University of Michigan, United States of America

  7. Hyungwon Choi
    Investigator

    Saw Swee Hock School of Public Health, National University of Singapore

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Summary
AccessionBT001977
Tool TypeApplication
Category
PlatformsLinux/Unix
TechnologiesC++
User InterfaceTerminal Command Line
Download Count0
Submitted ByHyungwon Choi