Quantitative quality control and background correction for two-colour microarray data (2004)

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 logratios 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.Another important low level analysis issue is that of data quality.Filtering microarray data according to different criteria to remove suspect arrays and/or spots from further analysis is widely used.This thesis investigates a more graduated approach to quality involving the use of array and spot quality weights in the linear model analysis of expression log-ratios.For array weights, a heteroscedastic linear model is fitted using a novel gene-by-gene update algorithm to calculate the array variance parameters.These variances are used as inverse weights in re-fits of the mean i model to gain more precise estimates of the gene expression coefficients.To assign spot weights, a predictive approach based on observed relationships between precision measured for genes from a large data set and various spot quality measures is taken.From the training data set, generalised weight functions are derived, and used to assign spot specific weights to data from other experiments.These weights are also used in the linear model analysis of the gene expression measures and can be used in conjunction with array weights, both of which are demonstrated to improve our power to detect differentially expressed genes in various data sets.The methods developed in this thesis aim to give fewer false positive results in a candidate list of differentially expressed genes, reducing the wastage of resources in follow up assays and assisting in the interpretation of the results.Many people need to be thanked for supporting me throughout my PhD.First thanks go to my principal PhD supervisors, Gordon and Terry, for their guidance and direction on this project.I'm especially grateful to 'Crazy' Gordon who encouraged me to take the plunge and move from sunny Brisbane to the cooler climes of Melbourne, a decision which I haven't regretted.Thank you Gordon for your patience, generosity with your time, and for being such a social supervisor on our many Friday visits to the pub and dinners around North Melbourne and Carlton.The next beer is