Normalization of microarray data using a spatial mixed model analysis which includes splines.
BACKGROUND
Microarray experiments with thousands of genes on a slide and multiple slides used in any experimental set represent a large body of data with many sources of variation. The identification of such sources of variation within microarray experimental sets is critical for correct deciphering of desired gene expression differences.


RESULTS
We describe new methods for the normalization using spatial mixed models which include splines and analysis of two-colour spotted microarrays for within slide variation and for a series of slides. The model typically explains 45-85% of the variation on a slide with only approximately 1% of the total degrees of freedom. The results from our methods compare favourably with those from intensity dependent normalization loess methods where we accounted for twice as much uncontrolled and unwanted variation on the slides. We have also developed an index for each EST that combines the various measures of the differential response into a single value that researchers can use to rapidly assess the genes of interest.
Baird
David
D
AgResearch, Lincoln, New Zealand. david.baird@agresearch.co.nz
Johnstone
Peter
P
Wilson
Theresa
T
D000465:Algorithms; D016477:Artifacts; D019985:Benchmarking / Q000379:methods / Q000592:standards; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D020869:Gene Expression Profiling / Q000379:methods* / Q000592:standards; D014644:Genetic Variation; D008957:Models, Genetic; D015233:Models, Statistical; D009716:Numerical Analysis, Computer-Assisted; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods* / Q000592:standards; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D017422:Sequence Analysis, DNA / Q000379:methods* / Q000592:standards; D012984:Software; D013269:Stochastic Processes