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

are significant assume such a data structure [7]. This still leaves several hundred values to
compare per sample. For classification purposes a much smaller diagnostic set is desired, so
analysts typically employ a down-selection method to their dataset [12, 19-29]. The rationale for
such an approach is two fold: it should improve the statistical power of subsequent analysis, by
minimizing the N>>P issue and it will enrich the impact of the final candidates, by eliminating
weak classifiers within the candidate list [11, 12, 20, 21, 27]. Weak classifiers are features within
a solution subset which are not critical to the subset’s classification performance [12, 30, 31].
Common approaches to down selection in Microarray analysis pipelines have included:
significance analysis of Microarray (SAM), t-tests, fold changes, expression differences, signal to
noise ratios, etc. [12, 21, 23, 26, 27, 32]. For the following analyses we employed Welch’s t-test
on the ProbeSet values, rather than the individual probes across the ProbeSet since this is the only
comparison possible with the RMA and dCHIP methods (see Chapter 2 for details). Welch’s t-
test first requires performing the F test for equality of variance, as given by:
Welch’s t-test is formulated as:
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F " , (3)
s
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t " X ) X / s / n ( s / n , (4)
where s1 = s2 given equal variance, which must be tested for. The variable X represents the
sample population mean, and n is the sample size [5]. The t-test is usually considered to be a
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weak diagnostic and efforts to control the family wise error rate through Bonferroni corrections or
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