Spot shape modelling and data transformations for microarrays.
BACKGROUND
To study lowly expressed genes in microarray experiments, it is useful to increase the photometric gain in the scanning. However, a large gain may cause some pixels for highly expressed genes to become saturated. Spatial statistical models that model spot shapes on the pixel level may be used to infer information about the saturated pixel intensities. Other possible applications for spot shape models include data quality control and accurate determination of spot centres and spot diameters.


RESULTS
Spatial statistical models for spotted microarrays are studied including pixel level transformations and spot shape models. The models are applied to a dataset from 50mer oligonucleotide microarrays with 452 selected Arabidopsis genes. Logarithmic, Box-Cox and inverse hyperbolic sine transformations are compared in combination with four spot shape models: a cylindric plateau shape, an isotropic Gaussian distribution and a difference of two-scaled Gaussian distribution suggested in the literature, as well as a proposed new polynomial-hyperbolic spot shape model. A substantial improvement is obtained for the dataset studied by the polynomial-hyperbolic spot shape model in combination with the Box-Cox transformation. The spatial statistical models are used to correct spot measurements with saturation by extrapolating the censored data.


BACKGROUND
Source code for R is available at http://www.matfys.kvl.dk/~ekstrom/spotshapes/
Ekstrøm
Claus Thorn
CT
Department of Mathematics and Physics, Center of Molecular Plant Physiology, Royal Veterinary and Agricultural University, Thorvaldsensvej, Fredriksberg, Denmark. ekstrom@dina.kvl.dk
Bak
Søren
S
Kristensen
Charlotte
C
Rudemo
Mats
M
D000465:Algorithms; D017360:Arabidopsis / Q000235:genetics / Q000378:metabolism; D029681:Arabidopsis Proteins / Q000235:genetics / Q000378:metabolism; D001185:Artificial Intelligence; D020869:Gene Expression Profiling / Q000379:methods*; D018506:Gene Expression Regulation, Plant / Q000502:physiology; D014644:Genetic Variation; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008856:Microscopy, Fluorescence / Q000379:methods*; D008957:Models, Genetic; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D010783:Photometry / Q000379:methods; D012815:Signal Processing, Computer-Assisted; D014157:Transcription Factors / Q000235:genetics / Q000378:metabolism
D029681:Arabidopsis Proteins; D014157:Transcription Factors