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

whole model analysis without permutation, where BaFL achieved an average AUC of 91.2% ±
3.4% across the 4 lists.
The analysis of the results we obtained using subsets of the author’s candidate lists leads to a
number of observations. First, the gene list based on the more stringent correlation coefficient in
the Bhattacharjee experiment (0.85 corr) consistently underperforms with respect to the results
for the less stringent correlation coefficient. These two Bhattacharjee lists share the fewest
common DE classified genes between datasets. According to the original article, these ProbeSets
were selected as having the highest Pearson correlation values with respect to phenotype across
45 adenocarcinoma sample replicates [3]. This should result in more consistent expression value
changes, but as summarized in Figure 3.7, there is more disparity in the cross-experiment fold
changes calculated for RMA and dCHIP including the ProbeSets which are not differentially
expressed. Figure 3.7 presents the comparison of fold changes (disease/normal) of each ProbeSet
identified by the 4 filtered candidate lists, with the DE ProbeSets identified by arrows. The
columns are the 4 candidate lists: Bhattacharjee Correlation80, Bhattacharjee Correlation85,
Stearman markers, and our 325 DE ProbeSets, respectively. The rows are the three cleansing
approaches: RMA, dCHIP, and BaFL, respectively. Uninformative ProbeSets appear as grey
circles near 1 and linear regression of the data using QR decomposition [16] is drawn for
orientation.
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