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

from each patient: 2 normal looking that are adjacent to the tumor and 2 adenocarcinoma
samples); one of the normal samples is missing, presumably for high tumor content [4]. These
sample biopsies were harvested using microdissection techniques and then snap-frozen [4]. The
final BaFL interpretation of the sample included 17 tumor samples and 14 normal (i.e. adjacent)
samples. These two datasets had a common group of 325 ProbeSets that were identified as
significantly differentially expressed between the diseased and normal states, based upon Welch’s
t-test of BaFL-provided values, applied at the ProbeSet (aggregate) level, described in detail in
Chapter 3 [9]. The latent structure of these 325 ProbeSets is presented as a final piece of
evidence showing why these ProbeSets should serve as the foundation for the feature selection,
and particularly why the BaFL interpretation of these ProbeSets’ signal intensities is preferred.
Feature Selection
Using the BaFL-provided values for these genes, the Bonferroni correction was applied to each
dataset; correction cutoffs were less than 1.18e-05 for the Bhattacharjee data and 7.68e-06 for the
Stearman data.
Q " p / t , (4.1)
where { Q , 1} if p is less than the correction. The number of total tests is represented by t, and p
represents the p-value of Welch’s t-test [8, 9]. This resulted in 79 Bonferroni corrected ProbeSets
out of the 480 Bhattacharjee DE ProbeSets and 352 Bonferroni corrected ProbeSets from the
3,162 Stearman DE ProbeSets; the intersection set was 30 ProbeSets. The Bonferroni correction
was applied also to both original BaFL-interpreted 4,253 Bhattacharjee ProbeSets and 6,506
BaFL interpreted Stearman ProbeSets. The resulting intersection of ‘most significant’ ProbeSets
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