Abstract
cDNA microarrays are revolutionizing post-genomic biology, by monitoring the expression activity of thousands of genes simultaneously. The first
stage in the analysis of microarray data is estimation of the level of gene expres-
sion from digital images obtained by laser scanning. However these data often
include saturated pixel values set to the software limit of 65535. We consider
three statistical models that correct for bias introduced by these saturation ef-
fects. Model 1 is applicable to the digital image of a microarray, and uses a linear model based on the principal components of uncensored spots, with parameters estimated by penalised least squares. Model 2 also uses pixel values, but we show that the ratio of expressions between pairs of samples can be estimated simply using the uncensored pixels associated with a spot. Model 3 is instead applicable to mean spot values from multiple laser scans at different settings. A functional regression model is used, based on a nonlinear relationship with both additive and multiplicative error terms, and fitted by robust methods. All three models are shown to be effective in correcting for the bias.
stage in the analysis of microarray data is estimation of the level of gene expres-
sion from digital images obtained by laser scanning. However these data often
include saturated pixel values set to the software limit of 65535. We consider
three statistical models that correct for bias introduced by these saturation ef-
fects. Model 1 is applicable to the digital image of a microarray, and uses a linear model based on the principal components of uncensored spots, with parameters estimated by penalised least squares. Model 2 also uses pixel values, but we show that the ratio of expressions between pairs of samples can be estimated simply using the uncensored pixels associated with a spot. Model 3 is instead applicable to mean spot values from multiple laser scans at different settings. A functional regression model is used, based on a nonlinear relationship with both additive and multiplicative error terms, and fitted by robust methods. All three models are shown to be effective in correcting for the bias.
| Original language | English |
|---|---|
| Title of host publication | Statistical Solutions to Modern Problems: Proceedings of the 20th International Workshop on Statistical Modelling |
| Editors | A. R. Francis, K. M. Matawie, A. Oshlack, G. K. Smyth |
| Publisher | University of Western Sydney Press |
| Pages | 17-35 |
| Number of pages | 19 |
| Publication status | Published - 2005 |