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

Figure 3.2: Down selection models- Stearman preditct Bhattacharjee. Classification results summarized,
for kNN, RF and LDA classifier models, where training used the Stearman experiment-derived gene list as
the initial input, and testing was done on the Bhattacharjee data. The last column summarizes the
performance at each selection level across the three algorithms. Each graph shows the training set number
(training sets described below) on the y-axis and the cumulative AUC value on the x-axis. Each row of
graphs shows outcomes based on starting with a particular cleansing method (RMA, dCHIP and BAFL)
followed by t-test classification for DE genes lists. The 4 sets of input genes result from additional selection
criteria and are denoted by the different shades of blue circles in each figure. The 4 sets: set 1 was ALL, or
no additional selection, the complete set of ProbeSets, 12,625 for the RMA and dCHIP algorithms and
4200 for BAFL data, set 2 was Stearman DE, the DE genes from the Stearman (RMA 5291, dCHIP 5208,
BAFL 3344) experiment, set 3 was Bhattacharjee DE, the DE genes from the Bhattacharjee experiment
(RMA 6595, dCHIP 6429, BAFL 480) and set 4 was the intersection of DE, the intersection of the DE
genes in the two experiments (RMA 5085, dCHIP 4125, BAFL 325).
Figure 3.3 is the reverse experiment, using the cross-sections for the Bhattacharjee dataset-
derived down selected ProbeSet lists and values generated by RMA, dCHIP and BaFL to train the
models, with subsequent testing of the models’ performance on the Stearman data.
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