Data independent acquisition (DIA) is a mode of mass spectrometry (MS) analysis that can generate MS/MS data for an unbiased selection of peptides, offering new opportunities to achieve more complete detection of peptides/proteins. DIA also enables fragment-level quantification, which has shown improvements in the detection of quantitative changes in proteins. However, data extraction methods for DIA data are still in early development stage and few statistical approaches are available for this emerging platform of quantitative proteomics data. In this work we describe a software package MAP-DIA for statistical analysis of differential expression using MS/MS fragment-level quantitative data. MAP-DIA offers a series of tools for essential data preprocessing, including a novel retention time-based normalization method and multiple peptide/fragment selection steps. Using the preprocessed data, MAP-DIA provides hierarchical model-based statistical significance analysis for multi-group comparisons under representative experimental designs. This advanced modelling technique also allows the user to incorporate relational information such as protein-protein interaction data and gene ontology annotations, enabling module-oriented analysis of differential expression that pools statistical information within modules. Using a comprehensive set of simulation datasets, we show that MAP-DIA provides reliable classification of differentially expressed proteins with accurate control of the false discovery rates. We also illustrate MAP-DIA using two recently published DIA datasets on 14-3-3-beta dynamic interaction network and prostate cancer glycoproteome, with detailed description of the data preprocessing steps and statistical analysis.

Download
The software and vignette can be downloaded from
http://sourceforge.net/projects/mapdia/files/

Compilation with GNU Scientific Library (GSL)
GSL is free from the web. Simply run the bash script ‘compile’ to install the program. Directory containing the executable should also be added to the PATH variable. Data preparation steps are described in the vignette.

References
[1] G. Teo, S. Kim, C-.C. Tsou, B. Collins, A-.C. Gingras, A.I. Nesvizhskii, H. Choi (2015) MAP-DIA: Preprocessing and Statistical Analysis of Quantitative Proteomics Data from Data Independent Acquisition Mass Spectrometry, Submitted