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

Chapter 3: Down Selection
The focus of this chapter is to compare the performance of the BaFL method’s interpretation of
signal intensity measurements, in the form of aggregated values called ProbeSets, against the
analogous ProbeSet interpretations of two statistically based algorithms, RMA and dCHIP [1, 2].
The test used in the comparison is the performance of each method in cross-dataset classification
experiments. RMA and dCHIP (here, the R implementations thereof) are two commonly used
methods that, like BaFL, reach down to the probe level values in order to determine which
members of a set of probes are to be included in an aggregated ProbeSet, although they differ
from each other as to how the remaining members of the set should be weighted in that
aggregation [1, 2]. One challenge in doing this comparison is that RMA and dCHIP are, at the
measurement level, black boxes, in that the user does not know, and cannot retrieve, those probes
that are left out (or included), nor is the reason for elimination explained That is, although the
algorithms are fully accessible, they were not designed to assess or report on probes at the
individual level but only as aggregates. On the other hand, for the BaFL method we can precisely
assign the reason for eliminating a probe, extract information as to whether additional reasons for
eliminating a probe exist, and show what would happen were it to be included; for the other
methods we can only compare the ProbeSet differences and effects on subsequent analysis
outcomes. In this chapter we perform parallel analyses and compare the outcomes, and trace back
the root of the discovered differences to the extent that the algorithms permit. In addition, a
comparison is made to the ‘significant genes’ lists of the original authors [3, 4], bearing in
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