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

Data Analysis Overview
1. Each of three probe-cleansing methods (RMA, dCHIP, BaFL) is used to generate
ProbeSet values, on the same sample sets from each experiment (2-state case).
2. Down selection was performed for each cleansing methods’ interpretation of the data.
Down selection yields the identification of differentially expressed genes, starting with
the ProbeSet values produced by each method, based on the outcome of a Welch’s t-test
of those values across the sample sets [5]. Three such down selection lists are generated:
one list of DE genes from each experiment and a third that is the intersection of those two
lists. The values of the genes in the lists (Stearman DE, Bhattacharjee DE, and
Intersection of DE) then are used as input to three types of classifiers; kNN [6, 7], LDA
[8, 9] and RF [10-12], and the resulting models are assessed, based on the AUC curves
[13, 14], for their cross-experiment sample class prediction ability relative to the base
model (ALL), the complete set of genes’ values.
3. A second type of comparison uses the two candidate gene lists proposed by the
Bhattacharjee, et al. authors [3] and the candidate gene list proposed by the Stearman, et
al. authors [4], sub-selected in each case for those genes passed by the BaFL pipeline (but
not necessarily identified as DE). A fourth candidate gene list comprised of the BaFL-
passed and intersecting t-test identified DE genes for both BaFL datasets, and is the same
final list which was used in step 2. The four lists used the ProbeSet values originally
suggested by each cleansing method, (not the values that resulted from the methods of the
original papers, since the underlying sample sets have been modified) and then proceeds
as in step 2 for a comparison of classification strengths based on the three types of
models.
These steps are discussed in more detail in the following sections.
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