title: Reno: regularized non-parametric analysis of protein lysate array data.
abstract: BACKGROUND
  The reverse-phase protein lysate arrays have been used to quantify the relative expression levels of a protein in a number of cellular samples simultaneously. To avoid quantification bias due to mis-specification of commonly used parametric models, a nonparametric approach based on monotone response curves may be used. The existing methods, however, aggregate the protein concentration levels of replicates of each sample, and therefore fail to account for within-sample variability.
  
  
  RESULTS
  We propose a method of regularization on protein concentration estimation at the level of individual dilution series to account for within-sample or within-group variability. We use an efficient algorithm to optimize an approximate objective function, with a data-adaptive approach to choose the level of shrinkage. Simulation results show that the proposed method quantifies protein concentration levels well. We show through the analysis of protein lysate array data from cell lines of different cancer groups that accounting for within-sample variability leads to better statistical analysis.
  
  
  BACKGROUND
  Code written in statistical programming language R is available at: http://odin.mdacc.tmc.edu/~jhhu/Reno
authors.affiliation: Department of Statistics, University of Illinois at Urbana-Champaign, Champaign, IL 61820, USA.
authors.forename: Bin
authors.initials: B
authors.lastname: Li
authors.forename: Feng
authors.initials: F
authors.lastname: Liang
authors.forename: Jianhua
authors.initials: J
authors.lastname: Hu
authors.forename: And Xuming
authors.initials: AX
authors.lastname: He
mesh_terms: D000465:Algorithms; D045744:Cell Line, Tumor; D006801:Humans; D009369:Neoplasms / Q000737:chemistry / Q000235:genetics*; D040081:Protein Array Analysis / Q000379:methods*; D011506:Proteins / Q000032:analysis* / Q000235:genetics; D012044:Regression Analysis
chemical_list: D011506:Proteins