title: Statistical analysis of an RNA titration series evaluates microarray precision and sensitivity on a whole-array basis.
abstract: BACKGROUND
  Concerns are often raised about the accuracy of microarray technologies and the degree of cross-platform agreement, but there are yet no methods which can unambiguously evaluate precision and sensitivity for these technologies on a whole-array basis.
  
  
  RESULTS
  A methodology is described for evaluating the precision and sensitivity of whole-genome gene expression technologies such as microarrays. The method consists of an easy-to-construct titration series of RNA samples and an associated statistical analysis using non-linear regression. The method evaluates the precision and responsiveness of each microarray platform on a whole-array basis, i.e., using all the probes, without the need to match probes across platforms. An experiment is conducted to assess and compare four widely used microarray platforms. All four platforms are shown to have satisfactory precision but the commercial platforms are superior for resolving differential expression for genes at lower expression levels. The effective precision of the two-color platforms is improved by allowing for probe-specific dye-effects in the statistical model. The methodology is used to compare three data extraction algorithms for the Affymetrix platforms, demonstrating poor performance for the commonly used proprietary algorithm relative to the other algorithms. For probes which can be matched across platforms, the cross-platform variability is decomposed into within-platform and between-platform components, showing that platform disagreement is almost entirely systematic rather than due to measurement variability.
  
  
  CONCLUSIONS
  The results demonstrate good precision and sensitivity for all the platforms, but highlight the need for improved probe annotation. They quantify the extent to which cross-platform measures can be expected to be less accurate than within-platform comparisons for predicting disease progression or outcome.
authors.affiliation: Ian Potter Foundation Centre for Cancer Genomics and Predictive Medicine, Peter MacCallum Cancer Centre, St Andrew's Place, East Melbourne, Victoria 3002, Australia. andrew.holloway@petermac.org
authors.forename: Andrew J
authors.initials: AJ
authors.lastname: Holloway
authors.forename: Alicia
authors.initials: A
authors.lastname: Oshlack
authors.forename: Dileepa S
authors.initials: DS
authors.lastname: Diyagama
authors.forename: David D L
authors.initials: DD
authors.lastname: Bowtell
authors.forename: Gordon K
authors.initials: GK
authors.lastname: Smyth
mesh_terms: D000465:Algorithms; D015982:Bias; D003627:Data Interpretation, Statistical; D005456:Fluorescent Dyes; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D019169:Jurkat Cells; D015233:Models, Statistical; D017711:Nonlinear Dynamics; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D012333:RNA, Messenger / Q000378:metabolism*; D012044:Regression Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
chemical_list: D005456:Fluorescent Dyes; D012333:RNA, Messenger