A multiresolution approach to the detection of image discrepancies for improved quality control of microarray oligonucleotide images.

摘要

The impact of microarrays on biological research has been phenomenal. By enabling scientists to assess the expression levels of thousands of genes simultaneously this high-throughput technology significantly reduces the time spent per experiment and allows for more complicated experiments. The microarray process is a complex series of steps that yields enormous amounts of data in microarray images. The Affymetrix GeneChipRTM system pioneered microarray technology, creating an image file format that has become standard for microarray image processing. During a microarray experiment, a microarray chip is scanned using a confocal laser scanner that captures the raw data (.DAT). A summary of the raw image data is created by computing a single intensity for each probe cell in the DAT file. This summary file, cell intensity file (.CEL), is used for further analysis in microarray image processing programs. Side-by-side visual comparison of a DAT file with its associated CEL file shows that details are lost during the conversion. This research employs multiresolution analysis coupled with denoising techniques to preprocess raw microarray images before the DAT to CEL transformation. The contributions of this research are three-fold: (1) provides a systematic evaluation of wavelet-based noise reduction techniques in the context of microarray data, (2) develops a prototype to detect artifacts in microarray raw image data at various resolutions and (3) builds on the aforementioned methodology and prototype to save researchers time and economic resources by excluding microarrays of questionable image quality from further analysis, where high quality images accurately represent underlying data. The performance of the SureShrink and BayesShrink denoising algorithms, applied to oligonucleotide microarray images at various resolutions, are evaluated. Using wavelet-transform-based-multiresolution analysis, this methodology is tested on datasets obtained from the Affymetrix GeneChipRTM human genome HG-U133 Plus 2.0 and the Affymetrix GeneChipRTM Rat Genome 230 arrays using wavelet-transform-based-multiresolution analysis. Results prove denoising raw microarray data benefits the microarray process, as compared to not processing the arrays.;Keywords: microarray, quality control, multiresolution, wavelets, denoising