Calibration plot for proteomics: A graphical tool to visually check the assumptions underlying FDR control in quantitative experiments. Issue 1 (5th January 2016)

• Record Type: Journal Article
• Authors: Giai Gianetto, Quentin; Combes, Florence; Ramus, Claire; Bruley, Christophe; Couté, Yohann; Burger, Thomas
• Is Part Of: Proteomics. Volume 16:Issue 1(2016)
• Page Start: 29
• Page End: 32
• Publication Date: 2016-01-05
• Subjects: False discovery rate -- Relative quantification experiments -- Statistical significance; Proteins -- Separation -- Periodicals; Bioinformatics -- Periodicals; Proteomics -- Periodicals; Genomes -- Periodicals; Molecular genetics -- Periodicals; 572.605
• DOI: 10.1002/pmic.201500189
• Languages: English
• ISSNs: 1615-9853

Abstract

Abstract : In MS‐based quantitative proteomics, the FDR control (i.e. the limitation of the number of proteins that are wrongly claimed as differentially abundant between several conditions) is a major postanalysis step. It is classically achieved thanks to a specific statistical procedure that computes the adjusted p ‐values of the putative differentially abundant proteins. Unfortunately, such adjustment is conservative only if the p ‐values are well‐calibrated; the false discovery control being spuriously underestimated otherwise. However, well‐calibration is a property that can be violated in some practical cases. To overcome this limitation, we propose a graphical method to straightforwardly and visually assess the p ‐value well‐calibration, as well as theR codes to embed it in any pipeline. All MS data have been deposited in the ProteomeXchange with identifier PXD002370 (http://proteomecentral.proteomexchange.org/dataset/PXD002370 ).