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

In an independent QC test of the arrays, we performed a parallel analysis of the datasets with the
R-Bioconductor affy package [19] using mock .CEL files, where probes had been aggregated
by batch. The results of this widely accepted algorithm were compared with ours for both batch
and sample analysis effects: that is, with and without the ‘white box’ probe cleansing approach.
At each stage of the above-described probe filtering process graphics of the output were
generated in order to monitor batch-specific behavior.
Set of probes and ProbeSet Behavior
One goal of this research is to assess the effects that this type of probe and measurement filtering
has on the reliability of the set-of-probe patterns across experiments. Data for the first goal was
collected using the x and y values to find probes present in all samples, and from there we
enforced the requirement that there be at least four probes in a probeset. Set-of-probe profiles
(mean and variance per class) are then shown graphically. Next the values in the set are
aggregated by taking the average for each sample, which is called a ProbeSet value, despite
possible confusion with the Affymetrix ProbeSet [36]. Subsequent down selection used
component probe and overall ProbeSets’ behaviors, across and between samples, as selection
criteria. Within each experiment and for each probe in a set, Welch’s t- test analysis (" = 0.05)
[51] was performed: the variance and sample means in each class were compared and used to
define three set-of-probe categories. In the first category, all of the probes in the probe-set
demonstrate means which are not significantly different between the sample classes: this class
was designated ‘Uninformative’. The associated ProbeSets are also ‘Uninformative’, since the
aggregated values must also fail to demonstrate a statistical significance in difference of class
38