ArticleMolecular & cellular proteomics : MCP2011
A bayesian mixture model for comparative spectral count data in shotgun proteomics.
Article in Molecular & cellular proteomics : MCP, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- MS1 ion current-based quantitative proteomics: A promising solution for reliable analysis of large biological cohorts.Mass spectrometry reviews · 2019Review
- A Tailored Multivariate Mixture Model for Detecting Proteins of Concordant Change Among Virulent Strains ofJournal of the American Statistical Association · 2018Article
- A multi-model statistical approach for proteomic spectral count quantitation.Journal of proteomics · 2016Article
- Reproducibility of Differential Proteomic Technologies in CPTAC Fractionated Xenografts.Journal of proteome research · 2016Article
- QPROT: Statistical method for testing differential expression using protein-level intensity data in label-free quantitative proteomics.Journal of proteomics · 2015Article
- Expression Profiling and Proteomic Analysis of JIN Chinese Herbal Formula in Lung Carcinoma H460 Xenografts.Evidence-based complementary and alternative medicine : eCAM · 2013Article
- Proteomic analysis of chloroplast-to-chromoplast transition in tomato reveals metabolic shifts coupled with disrupted thylakoid biogenesis machinery and elevated energy-production components.Plant physiology · 2012Article
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Authors and funding
4 authors.
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Abstract
Recent developments in mass-spectrometry-based shotgun proteomics, especially methods using spectral counting, have enabled large-scale identification and differential profiling of complex proteomes. Most such proteomic studies are interested in identifying proteins, the abundance of which is different under various conditions. Several quantitative methods have recently been proposed and implemented for this purpose. Building on some techniques that are now widely accepted in the microarray literature, we developed and implemented a new method using a Bayesian model to calculate posterior probabilities of differential abundance for thousands of proteins in a given experiment simultaneously. Our Bayesian model is shown to deliver uniformly superior performance when compared with several existing methods.
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