ArticleJournal of proteomics2015
mapDIA: Preprocessing and statistical analysis of quantitative proteomics data from data independent acquisition mass spectrometry.
Article in Journal of proteomics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 82 papers.
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Who cites it
82 citing papers in PubMed.
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- Takotsubo Syndrome: The First Non-Acute Proteomic Analysis by Remote Dried Blood Microsampling.Journal of cardiovascular translational research · 2026Article
- Human iPSC-Derived Mononuclear Phagocytes Improve Cognition and Neural Health across Multiple Mouse Models of Aging and Alzheimer's Disease.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Cross-Run Hybrid Features Improve the Identification of Data-Independent Acquisition Proteomics.ACS omega · 2024Article
- An Equine Protein Atlas Highlights Synovial Fluid Proteome Dynamics during Experimentally LPS-Induced Arthritis.Journal of proteome research · 2024Article
- Analysis and Visualization of Quantitative Proteomics Data Using FragPipe-Analyst.Journal of proteome research · 2024Article
- Patient subtyping analysis of baseline multi-omic data reveals distinct pre-immune states associated with antibody response to seasonal influenza vaccination.Clinical immunology (Orlando, Fla.) · 2024Article
- Myocardial ultrastructure of human heart failure with preserved ejection fraction.Nature cardiovascular research · 2024Article
- Topical and oral peroxisome proliferator-activated receptor-α agonist ameliorates diabetic corneal neuropathy.Scientific reports · 2024Article
- Targeting low levels of MIF expression as a potential therapeutic strategy for ALS.Cell reports. Medicine · 2024Article
- A uniquely efficacious type of CFTR corrector with complementary mode of action.Science advances · 2024Article
- Complex changes in serum protein levels in COVID-19 convalescents.Scientific reports · 2024Article
- The Molecular Twin artificial-intelligence platform integrates multi-omic data to predict outcomes for pancreatic adenocarcinoma patients.Nature cancer · 2024Article
- Functional and spatial proteomics profiling reveals intra- and intercellular signaling crosstalk in colorectal cancer.iScience · 2023Article
- Article
- Analysis of DIA proteomics data using MSFragger-DIA and FragPipe computational platform.Nature communications · 2023Article
- Accurate Label-Free Quantification by directLFQ to Compare Unlimited Numbers of Proteomes.Molecular & cellular proteomics : MCP · 2023Article
- Oral Peroxisome Proliferator-Activated Receptor-α Agonist Enhances Corneal Nerve Regeneration in Patients With Type 2 Diabetes.Diabetes · 2023Article
- Re-mining serum proteomics data reveals extensive post-translational modifications upon Zika and dengue infection.Molecular omics · 2023Article
- Phosphorylation-linked complex profiling identifies assemblies required for Hippo signal integration.Molecular systems biology · 2023Article
22 more citing papers are in PubMed but not listed here.
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7 authors.
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Abstract
unlabelledData 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. AVAILABILITY: 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.
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