ArticleBMC bioinformatics2019
Evaluation of linear models and missing value imputation for the analysis of peptide-centric proteomics.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Imputation of label-free quantitative mass spectrometry-based proteomics data using self-supervised deep learning.Nature communications · 2024Article
- Small molecule inhibiting microglial nitric oxide release could become a potential treatment for neuroinflammation.PloS one · 2023Article
- Article
- A comparative study of evaluating missing value imputation methods in label-free proteomics.Scientific reports · 2021Article
- Bioinformatics Methods for Mass Spectrometry-Based Proteomics Data Analysis.International journal of molecular sciences · 2020Review
- Adaption of the Aristotle Classifier for Accurately Identifying Highly Similar Bacteria Analyzed by MALDI-TOF MS.Analytical chemistry · 2020Article
- Trans-omics Impact of Thymoproteasome in Cortical Thymic Epithelial Cells.Cell reports · 2019Article
- What is Normalization? The Strategies Employed in Top-Down and Bottom-Up Proteome Analysis Workflows.Proteomes · 2019Review
- Proteome-Wide Analysis of Cysteine Reactivity during Effector-Triggered Immunity.Plant physiology · 2019Article
- Proceedings of the 2018 MidSouth Computational Biology and Bioinformatics Society (MCBIOS) conference.BMC bioinformatics · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
backgroundSeveral methods to handle data generated from bottom-up proteomics via liquid chromatography-mass spectrometry, particularly for peptide-centric quantification dealing with post-translational modification (PTM) analysis like reversible cysteine oxidation are evaluated. The paper proposes a pipeline based on the R programming language to analyze PTMs from peptide-centric label-free quantitative proteomics data.
resultsOur methodology includes variance stabilization, normalization, and missing data imputation to account for the large dynamic range of PTM measurements. It also corrects biases from an enrichment protocol and reduces the random and systematic errors associated with label-free quantification. The performance of the methodology is tested by performing proteome-wide differential PTM quantitation using linear models analysis (limma). We objectively compare two imputation methods along with significance testing when using multiple-imputation for missing data.
conclusionIdentifying PTMs in large-scale datasets is a problem with distinct characteristics that require new methods for handling missing data imputation and differential proteome analysis. Linear models in combination with multiple-imputation could significantly outperform a t-test-based decision method.
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