Two-colour microarrays are a popular tool for measuring relative gene expression between RNA populations for thousands of genes simultaneously. This thesis develops methods for assessing the quality and variability of data from such experiments and for incorporating these assessments into algorithms for discovering differential expression. The variability of microarray data depends not only on the quality of the arrays, but also on how they are processed and normalised. The intimate relationship between variability of expression log-ratios and the method used for background correcting the expression values is specifically explored. The performance of different estimators of the background level and various model-based processing methods, including a novel normal-exponential convolution model are compared in search of a ‘best’ alternative. The results indicate that the choice of method should be guided by the specific question of interest; the model-based methods give gene expression measures with low bias, and do very well at choosing differentially expressed genes, while subtracting low background estimates, or not background correcting the data produces low variance estimates which are the most biased, however perform best at choosing DE genes. All of these alternatives give better results than those obtained by the standard approach of subtracting high local background estimates from the foreground signal, which is not recommended. (For complete abstract open document)
| Identifer | oai:union.ndltd.org:ADTP/245183 |
| Creators | Ritchie, Matthew Edward |
| Source Sets | Australiasian Digital Theses Program |
| Language | English |
| Detected Language | English |
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