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

A Priori Prediction
We demonstrated that a priori probe prediction is a feasible approach based upon the cleansing
results provided by the BaFL pipeline. The probe prediction implemented in this study included
the incorporation of the linear range filter. The linear range filter is the only filter which affects
samples differently based upon the biological and laboratory variation. Therefore it is paramount
that the cross experiment probe extraction is performed upon similar disease studies and similar
tissues. This is apparent with the squamous cell cancer data models which generated the lowest
probe true positive rates (probes predicted to be thereafter cleansing and actually were). While
the false positive rates for ProbeSet prediction for the adenocarcinoma models seem high, we can
partially explain some of the false positives by considering that ~5600 probes were removed due
to the batch 10 localized bare spot effect, presented in Figure 2.1. These probes would likely not
have been removed via the BaFL pipeline for this third dataset. Their presence would therefore
be accounted for in the false positive rate (10%, data not shown) observed at the probe level.
Removal of these probes may have additionally eliminated what would otherwise have been
reliable ProbeSets since the affected ProbeSets may not have met the requirement of 4 constituent
probes for the Bhattacharjee dataset. This effect again would be observed via the 25.2% ProbeSet
presence false positive rate as indicated in Table 2.7.
Modified CDFs for Computational Efficiency
Since the probe characteristics are universal to an array design, one can easily construct a
modified CDF, which decreases the total number of probes that must be considered in an analysis
to those that are usable, and thus improves the computational requirements for an analysis. We
expect that different investigators will have preferred CDFs: for example, the cross-hybridization
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