Seongho Kim1, Nicholas Carruthers2, Joohyoung Lee3, Sreenivasa Chinni4, Paul Stemmer2. 1. Biostatistics Core, Karmanos Cancer Institute, Wayne State University, Detroit, MI 48201, USA; Department of Oncology, Wayne State University, Detroit, MI 48201, USA. Electronic address: kimse@karmanos.org. 2. Proteomics Core, Karmanos Cancer Institute, Wayne State University, Detroit, MI 48201, USA; Institute of Environmental Health Sciences, Wayne State University, Detroit, MI 48201, USA. 3. Biostatistics Core, Karmanos Cancer Institute, Wayne State University, Detroit, MI 48201, USA; Department of Family Medicine and Public Health Sciences, Wayne State University, Detroit, MI 48201, USA. 4. Department of Urology, School of Medicine, Wayne State University, Detroit, MI 48201, USA.
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.
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.
Authors: Eric W Deutsch; Luis Mendoza; David Shteynberg; Terry Farrah; Henry Lam; Natalie Tasman; Zhi Sun; Erik Nilsson; Brian Pratt; Bryan Prazen; Jimmy K Eng; Daniel B Martin; Alexey I Nesvizhskii; Ruedi Aebersold Journal: Proteomics Date: 2010-03 Impact factor: 3.984
Authors: Adam A Margolin; Shao-En Ong; Monica Schenone; Robert Gould; Stuart L Schreiber; Steven A Carr; Todd R Golub Journal: PLoS One Date: 2009-10-14 Impact factor: 3.240
Authors: Guiping Kong; Luming Zhou; Elisabeth Serger; Ilaria Palmisano; Francesco De Virgiliis; Thomas H Hutson; Eilidh Mclachlan; Anja Freiwald; Paolo La Montanara; Kirill Shkura; Radhika Puttagunta; Simone Di Giovanni Journal: Nat Metab Date: 2020-08-10