Sample A: Cover Page of Thesis, Project, or Dissertation Proposal

The process of determining a candidate gene list is often multi-step, with a (relatively) simple
statistical method being used to obtain an initial down-selected list, in which significant
expression change is identified, followed by a more sophisticated technique, in order to suggest a
final subset of genes in which correlation to the factor or phenotype of interest is robust [52, 69-
71, 77-83]. The assessment of these subsets can be done using supervised or unsupervised
methods [69]. Clustering is the most common form of the unsupervised methods, where the goal
is to achieve homogeneous clusters [84]. Supervised learning methods develop models from
training data and assess the quality of prediction of the test data [77, 85]. Performance metrics
are necessary for choosing among the learning algorithms: the most common metric is the area
under the receiver operating curve, which incorporates the sensitivity and specificity of the
classification results [86]. Other metrics include precision-recall, cost-sensitive analysis, etc. [87,
88].
Specific Aims
A number of confounding factors to Microarray experiments are well described in the scientific
literature [17, 18, 22, 26, 59]: to these factors is attributed the relative irreproducibility of
Microarray analysis results [35, 44, 57, 68]. While a number of investigators have reported the
effect of removing individual classes of contributions on the robustness of the results, to our
knowledge no investigation has removed the complete set of factors which we have established in
our cleansing pipeline. Of the sophisticated probe cleansing algorithms that have been developed
and are commonly used, all proceed by identifying and eliminating probes with large variance,
without exploring the underlying cause of that variance [45, 46, 48, 49, 89]. This black box
method leads to both inclusion of probes having dubious properties and exclusion of probes that
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