DEqMS : A Method for Accurate Variance Estimation in Differential Protein Expression Analysis
| Autor: | Simon Anders, Janne Lehtiö, Georgios Mermelekas, Henrik J. Johansson, Alina Malyutina, Lukas M. Orre, Yafeng Zhu, Yan Zhou Tran |
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| Přispěvatelé: | Research Program in Systems Oncology, Institute for Molecular Medicine Finland, University of Helsinki |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Proteomics
Proteome differential analysis Quantitative proteomics STATISTICAL-MODEL Computational biology Biochemistry label-free quantification FDR Cell Line Analytical Chemistry Bioconductor 03 medical and health sciences Tandem Mass Spectrometry Escherichia coli Humans Molecular Biology mass spectrometry 030304 developmental biology 0303 health sciences quality control and metrics 030302 biochemistry & molecular biology Technological Innovation and Resources Linear model Reproducibility of Results data evaluation Gefitinib Statistical model QUANTIFICATION ErbB Receptors Label-free quantification statistics TMT MCF-7 Cells Protein Expression Analysis 1182 Biochemistry cell and molecular biology Isoelectric Focusing Peptides Algorithms Biomarkers Chromatography Liquid |
| Zdroj: | Molecular & Cellular Proteomics : MCP |
| Popis: | Differential analysis of MS-data to identify biomarkers or to understand biology is a cornerstone in proteomics. DEqMS is a robust method for analysis of both labelled and label-free MS-data. The method takes into account the inherent dependence of protein variance on the number of PSMs or peptides used for quantification, thereby providing a more accurate variance estimation. Compared to current methods, DEqMS achieves better accuracy and statistical power in quantitative proteomics. The tool is available as user-friendly R package. Graphical Abstract Highlights DEqMS is a method for statistical analysis of quantitative MS-data. Variance estimates based on the actual MS-data structure. Improved statistical power and accuracy in protein differential analysis. DEqMS is available as a user-friendly R package in Bioconductor. Quantitative proteomics by mass spectrometry is widely used in biomarker research and basic biology research for investigation of phenotype level cellular events. Despite the wide application, the methodology for statistical analysis of differentially expressed proteins has not been unified. Various methods such as t test, linear model and mixed effect models are used to define changes in proteomics experiments. However, none of these methods consider the specific structure of MS-data. Choices between methods, often originally developed for other types of data, are based on compromises between features such as statistical power, general applicability and user friendliness. Furthermore, whether to include proteins identified with one peptide in statistical analysis of differential protein expression varies between studies. Here we present DEqMS, a robust statistical method developed specifically for differential protein expression analysis in mass spectrometry data. In all data sets investigated there is a clear dependence of variance on the number of PSMs or peptides used for protein quantification. DEqMS takes this feature into account when assessing differential protein expression. This allows for a more accurate data-dependent estimation of protein variance and inclusion of single peptide identifications without increasing false discoveries. The method was tested in several data sets including E. coli proteome spike-in data, using both label-free and TMT-labeled quantification. Compared with previous statistical methods used in quantitative proteomics, DEqMS showed consistently better accuracy in detecting altered protein levels compared with other statistical methods in both label-free and labeled quantitative proteomics data. DEqMS is available as an R package in Bioconductor. |
| Databáze: | OpenAIRE |
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