MSstatsTMT: Statistical Detection of Differentially Abundant Proteins in Experiments with Isobaric Labeling and Multiple Mixtures期刊界 All Journals 搜尽天下杂志传播学术成果专业期刊搜索期刊信息化学术搜索

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MSstatsTMT: Statistical Detection of Differentially Abundant Proteins in Experiments with Isobaric Labeling and Multiple Mixtures

Authors:	Ting Huang Meena Choi Manuel Tzouros Sabrina Golling Nikhil Janak Pandya Balazs Banfai Tom Dunkley Olga Vitek

Institution:	1. Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA;2. Roche Pharma Research and Early Development, Pharmaceutical Sciences-BiOmics and Pathology, Roche Innovation Center Basel, Hoffmann-La Roche Ltd, Basel, Switzerland

Abstract:	Download : Download high-res image (246KB) Download : Download full-size image Highlights •Statistical approach for differential abundance analysis for proteomic experiments with TMT labeling. •Applicable to large-scale experiments with complex or unbalanced design. •An open-source R/Bioconductor package compatible with popular data processing tools.

Keywords:	Mass spectrometry statistics quantification bioinformatics software mathematical modeling hypothesis testing multiple mixtures protein quantification TMT
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