Pixel-level signal modelling with spatial correlation for two-colour microarrays
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Pixel-level signal modelling with spatial correlation for two-colour microarrays. / Ekstrøm, Claus T.; Bak, Søren; Rudemo, Mats.
In: Statistical Applications in Genetics and Molecular Biology, Vol. 4, No. 1, 6, 06.04.2005.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Pixel-level signal modelling with spatial correlation for two-colour microarrays
AU - Ekstrøm, Claus T.
AU - Bak, Søren
AU - Rudemo, Mats
PY - 2005/4/6
Y1 - 2005/4/6
N2 - Statistical models for spot shapes and signal intensities are used in image analysis of laser scans of microarrays. Most models have essentially been based on the assumption of independent pixel intensity values, but models that allow for spatial correlation among neighbouring pixels can accommodate errors in the microarray slide and should improve the model fit. Five spatial correlation structures, exponential, Gaussian, linear, rational quadratic and spherical, are compared for a dataset with 50-mer two-colour oligonucleotide microarrays and 452 probes for selected Arabidopsis genes. Substantial improvement in model fit is obtained for all five correlation structures compared to the model with independent pixel values, and the Gaussian and the spherical models seem to be slightly better than the other three models. We also conclude that for the data set analysed the correlation seems negligible for non-neighbouring pixels.
AB - Statistical models for spot shapes and signal intensities are used in image analysis of laser scans of microarrays. Most models have essentially been based on the assumption of independent pixel intensity values, but models that allow for spatial correlation among neighbouring pixels can accommodate errors in the microarray slide and should improve the model fit. Five spatial correlation structures, exponential, Gaussian, linear, rational quadratic and spherical, are compared for a dataset with 50-mer two-colour oligonucleotide microarrays and 452 probes for selected Arabidopsis genes. Substantial improvement in model fit is obtained for all five correlation structures compared to the model with independent pixel values, and the Gaussian and the spherical models seem to be slightly better than the other three models. We also conclude that for the data set analysed the correlation seems negligible for non-neighbouring pixels.
KW - Censored data
KW - Polynomial-hyperbolic model
KW - Spatial correlation
KW - Spotted array
UR - http://www.scopus.com/inward/record.url?scp=84860945403&partnerID=8YFLogxK
U2 - 10.2202/1544-6115.1112
DO - 10.2202/1544-6115.1112
M3 - Journal article
AN - SCOPUS:84860945403
VL - 4
JO - Statistical Applications in Genetics and Molecular Biology
JF - Statistical Applications in Genetics and Molecular Biology
SN - 1544-6115
IS - 1
M1 - 6
ER -
ID: 203909627