title: High-resolution spatial normalization for microarrays containing embedded technical replicates.
abstract: BACKGROUND
  Microarray data are susceptible to a wide-range of artifacts, many of which occur on physical scales comparable to the spatial dimensions of the array. These artifacts introduce biases that are spatially correlated. The ability of current methodologies to detect and correct such biases is limited.
  
  
  RESULTS
  We introduce a new approach for analyzing spatial artifacts, termed 'conditional residual analysis for microarrays' (CRAM). CRAM requires a microarray design that contains technical replicates of representative features and a limited number of negative controls, but is free of the assumptions that constrain existing analytical procedures. The key idea is to extract residuals from sets of matched replicates to generate residual images. The residual images reveal spatial artifacts with single-feature resolution. Surprisingly, spatial artifacts were found to coexist independently as additive and multiplicative errors. Efficient procedures for bias estimation were devised to correct the spatial artifacts on both intensity scales. In a survey of 484 published single-channel datasets, variance fell 4- to 12-fold in 5% of the datasets after bias correction. Thus, inclusion of technical replicates in a microarray design affords benefits far beyond what one might expect with a conventional 'n = 5' averaging, and should be considered when designing any microarray for which randomization is feasible.
  
  
  BACKGROUND
  CRAM is implemented as version 2 of the hoptag software package for R, which is included in the Supplementary information.
authors.affiliation: Department of Molecular Biology and Genetics, Johns Hopkins University, Baltimore, MD 21205, USA. dyuan@jhmi.edu
authors.forename: Daniel S
authors.initials: DS
authors.lastname: Yuan
authors.forename: Rafael A
authors.initials: RA
authors.lastname: Irizarry
mesh_terms: D000465:Algorithms; D016477:Artifacts; D003627:Data Interpretation, Statistical; D020869:Gene Expression Profiling / Q000379:methods* / Q000592:standards; D017404:In Situ Hybridization, Fluorescence / Q000379:methods*; D008856:Microscopy, Fluorescence / Q000379:methods* / Q000592:standards; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods* / Q000592:standards; D011786:Quality Control; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity