Classification-based quantitative analysis of stable isotope labeling by amino acids in cell culture (SILAC) data. (December 2016)

• Record Type: Journal Article
• Authors: Kim, Seongho; Carruthers, Nicholas; Lee, Joohyoung; Chinni, Sreenivasa; Stemmer, Paul
• Is Part Of: Computer methods and programs in biomedicine. Volume 137(2016)
• Page Start: 137
• Page End: 148
• Publication Date: 2016-12
• Subjects: Classification -- Mass spectrometry -- Particle swarm optimization -- Proteomics -- SILAC; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2016.09.017
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: A novel PSO classification-based approach to dealing with SILAC data is introduced. It depends mainly on the protein ratio summary not restricted only to the proteins with two or more peptide hits. No addition correction for multiple comparisons is necessary. The developed methods are implemented in the R package cSILAC. Abstract: Background and objective: Stable isotope labeling by amino acids in cell culture (SILAC) is a practical and powerful approach for quantitative proteomic analysis. A key advantage of SILAC is the ability to simultaneously detect the isotopically labeled peptides in a single instrument run and so guarantee relative quantitation for a large number of peptides without introducing any variation caused by separate experiment. However, there are a few approaches available to assessing protein ratios and none of the existing algorithms pays considerable attention to the proteins having only one peptide hit. Methods: We introduce new quantitative approaches to dealing with SILAC protein-level summary using classification-based methodologies, such as Gaussian mixture models with EM algorithms and its Bayesian approach as well as K-means clustering. In addition, a new approach is developed using Gaussian mixture model and a stochastic, metaheuristic global optimization algorithm, particle swarm optimization (PSO), to avoid either a premature convergence or being stuck in a local optimum. Results: Our simulation studies show that the newly developed PSO-based method performs the best among others in terms of F1 score and the proposed methods further demonstrate the ability of detecting potential markers through real SILAC experimental data. Conclusions: No matter how many peptide hits the protein has, the developed approach can be applicable, rescuing many proteins doomed to removal. Furthermore, no additional correction for multiple comparisons is necessary for the developed methods, enabling direct interpretation of the analysis outcomes.