title: Robust estimation of protein expression ratios with lysate microarray technology.
abstract: BACKGROUND
  The protein lysate microarray is a developing proteomic technology for measuring protein expression levels in a large number of biological samples simultaneously. A challenge for accurate quantification is the relatively narrow dynamic range associated with the commonly used chromogenic signal detection system. To facilitate accurate measurement of the relative expression levels, each sample is serially diluted and each diluted version is spotted on a nitrocellulose-coated slide in triplicate. Thus, each sample yields multiple measurements in different dynamic ranges of the detection system. This study aims to develop suitable algorithms that yield accurate representations of the relative expression levels in different samples from multiple data points.
  
  
  RESULTS
  We evaluated two algorithms for estimating relative protein expression in different samples on the lysate microarray by means of a cross-validation procedure. For this purpose as well as for quality control we designed a 1440-spot lysate microarray containing 80 identical samples of purified bovine serum albumin, printed in triplicate with six 2-fold dilutions. Our analysis showed that the algorithm based on a robust least squares estimator provided the most accurate quantification of the protein lysate microarray data. We also demonstrated our methods by estimating relative expression levels of p53 and p21 in either p53(+/+) or p53(-/-) HCT116 colon cancer cells after two drug treatments and their combinations on another lysate microarray.
  
  
  BACKGROUND
  http://www.cs.tut.fi/~mirceanc/lysate_array_bioinformatics.htm
authors.affiliation: Department of Pathology, University of Texas M.D. Anderson Cancer Center, Houston, USA.
authors.forename: Cristian
authors.initials: C
authors.lastname: Mircean
authors.forename: Ilya
authors.initials: I
authors.lastname: Shmulevich
authors.forename: David
authors.initials: D
authors.lastname: Cogdell
authors.forename: Woonyoung
authors.initials: W
authors.lastname: Choi
authors.forename: Yu
authors.initials: Y
authors.lastname: Jia
authors.forename: Ioan
authors.initials: I
authors.lastname: Tabus
authors.forename: Stanley R
authors.initials: SR
authors.lastname: Hamilton
authors.forename: Wei
authors.initials: W
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D014408:Biomarkers, Tumor / Q000378:metabolism*; D045744:Cell Line, Tumor; D003110:Colonic Neoplasms / Q000378:metabolism*; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D006868:Hydrolysis; D007118:Immunoassay / Q000379:methods*; D016018:Least-Squares Analysis; D009363:Neoplasm Proteins / Q000378:metabolism*; D040081:Protein Array Analysis / Q000379:methods*; D012984:Software
chemical_list: D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins