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

whether analyses based upon individual probes is more or less informative than the aggregated
probesets.
Confounding factors have been documented as to their effect upon the aggregated probeset’s
measurement, and algorithms such as dCHIP, RMA, gcRMA, FARM, etc. have been developed
to identify the less variant probes within a given experiment [45, 46, 48]. The assumption
underlying most of these algorithms is that cross-sample variance is indicative of biologically
confounding factors rather than technical factors. A paradox of this assumption is that these
biological factors may be of real biological interest and removing the outliers results in loss of an
important experimental result; that is, what if the molecular phenotype of importance is that
variation of a set of genes increases as a result of an increase in the presence of a particular gene,
rather than that the expression of the affected set all increases (or decreases) homogeneously [67].
The methods reported below were developed to contend with observed short-comings of
Microarray results, including the inability to generate cross platform concordance of Microarray
experiments, the inability of resultant experimental gene subsets to demonstrate similar
performance on independent datasets, and the discordance in resultant gene subsets when
different methodologies are applied [35, 44, 57, 68]. In the first part of the research presented
here we have been able to show that these experimental discrepancies are readily explained by
specific sources of biological and laboratory variation.
The research goal of any Microarray experiment is to identify an informative but still
‘manageable’ subset of genes, from the thousands of observed genes, whose response correlates
significantly with the experimental factor(s) and which can additionally be investigated for
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