AI Article Synopsis
- Digital images from laser scanning of microarrays can have saturated pixel values when scan settings are too high, leading to inaccurate gene expression estimates.
- A proposed solution involves using a linear model based on the principal components of uncensored spots, which is quick and adaptable to different array profiles.
- Testing this method on biological data shows it can effectively improve detection range and correction for bias compared to other models, with available Fortran90 subroutines for implementation.
Article Abstract
Digital images obtained by the laser scanning of spotted microarrays often include saturated pixel values. These arise when the scan settings are sufficiently high and some pixels exceed the limit L=65535 and are instead set to L. Failure to adjust for this censoring leads to biased estimates of gene expression levels. To impute censored values, we propose a linear model based on the principal components of uncensored spots on the same array. This is computationally fast, flexible to adapt to distinctive spot shapes and profiles on different arrays, and is shown to be more effective than the polynomial-hyperbolic model in correcting for the bias. The application to biological data demonstrates the potential for enhancing the dynamic range of detection. Fortran90 subroutines implementing these methods are available at http://www.bioss.ac.uk/~chris.
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| http://dx.doi.org/10.2202/1544-6115.1244 | DOI Listing |
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