title: Classification-based quantitative analysis of stable isotope labeling by amino acids in cell culture (SILAC) data.
abstract: 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.affiliation: 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.
authors.forename: Seongho
authors.initials: S
authors.lastname: Kim
authors.affiliation: Proteomics Core, Karmanos Cancer Institute, Wayne State University, Detroit, MI 48201, USA; Institute of Environmental Health Sciences, Wayne State University, Detroit, MI 48201, USA.
authors.forename: Nicholas
authors.initials: N
authors.lastname: Carruthers
authors.affiliation: 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.
authors.forename: Joohyoung
authors.initials: J
authors.lastname: Lee
authors.affiliation: Department of Urology, School of Medicine, Wayne State University, Detroit, MI 48201, USA.
authors.forename: Sreenivasa
authors.initials: S
authors.lastname: Chinni
authors.affiliation: Proteomics Core, Karmanos Cancer Institute, Wayne State University, Detroit, MI 48201, USA; Institute of Environmental Health Sciences, Wayne State University, Detroit, MI 48201, USA.
authors.forename: Paul
authors.initials: P
authors.lastname: Stemmer
mesh_terms: D000465:Algorithms; D000596:Amino Acids / Q000737:chemistry*; D018929:Cell Culture Techniques; D006801:Humans; D007553:Isotope Labeling
chemical_list: D000596:Amino Acids
keywords: Classification; Mass spectrometry; Particle swarm optimization; Proteomics; SILAC