ArticleJournal of proteome research2019
pmartR: Quality Control and Statistics for Mass Spectrometry-Based Biological Data.
Article in Journal of proteome research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 1 of them a synthesis that pooled it.
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Who cites it
43 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Elucidating regulatory processes of intense physical activity by multi-omics analysis.Military Medical Research · 2023Pooled it
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- Blood pro-thrombotic analytes and platelet activation are associated with post-acute sequelae of COVID-19.BMC infectious diseases · 2025Observational
- Multi-Omic Profiling Identifies Conserved Metabolic Pathways Critical for SARS-CoV-2 Variants Infection.bioRxiv : the preprint server for biology · 2025Article
- Lipidomics Reveals Cell Specific Changes During Pluripotent Differentiation to Neural and Mesodermal Lineages.bioRxiv : the preprint server for biology · 2025Article
- Data from a multi-year targeted proteomics study of a longitudinal birth cohort of type 1 diabetes.Scientific data · 2025Article
- Conditional knockout of C/EBPβ in epidermis results in dysregulated lipid biosynthesis and a defect in skin barrier function.PloS one · 2025Article
- Calculating and Reporting Coefficients of Variation for DIA-Based Proteomics.Journal of proteome research · 2024Review
- The human plasma lipidome response to exertional heat tolerance testing.Lipids in health and disease · 2024Article
- A Framework for Quality Control in Quantitative Proteomics.Journal of proteome research · 2024Article
- Quality Control in the Mass Spectrometry Proteomics Core: A Practical Primer.Journal of biomolecular techniques : JBT · 2024Article
- Exploring the Synergistic Secretome: Insights from Co-Cultivation ofJournal of fungi (Basel, Switzerland) · 2024Article
- LC-HRMS Lipidomic Fingerprints in Serbian Cohort of Schizophrenia Patients.International journal of molecular sciences · 2024Article
- A framework for quality control in quantitative proteomics.bioRxiv : the preprint server for biology · 2024Article
- Communicating Mass Spectrometry Quality Information in mzQC with Python, R, and Java.Journal of the American Society for Mass Spectrometry · 2024Article
- Deciphering ApoE Genotype-Driven Proteomic and Lipidomic Alterations in Alzheimer's Disease Across Distinct Brain Regions.Journal of proteome research · 2024Article
- The impact of heat stress on the human plasma lipidome.Research square · 2024Article
- A compendium of multi-omics data illuminating host responses to lethal human virus infections.Scientific data · 2024Article
- Do Nanodisc Assembly Conditions Affect Natural Lipid Uptake?Journal of the American Society for Mass Spectrometry · 2023Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
Prior to statistical analysis of mass spectrometry (MS) data, quality control (QC) of the identified biomolecule peak intensities is imperative for reducing process-based sources of variation and extreme biological outliers. Without this step, statistical results can be biased. Additionally, liquid chromatography-MS proteomics data present inherent challenges due to large amounts of missing data that require special consideration during statistical analysis. While a number of R packages exist to address these challenges individually, there is no single R package that addresses all of them. We present pmartR, an open-source R package, for QC (filtering and normalization), exploratory data analysis (EDA), visualization, and statistical analysis robust to missing data. Example analysis using proteomics data from a mouse study comparing smoke exposure to control demonstrates the core functionality of the package and highlights the capabilities for handling missing data. In particular, using a combined quantitative and qualitative statistical test, 19 proteins whose statistical significance would have been missed by a quantitative test alone were identified. The pmartR package provides a single software tool for QC, EDA, and statistical comparisons of MS data that is robust to missing data and includes numerous visualization capabilities.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.