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

cleansing process. This is demonstrated by an a priori extraction of reliable probe and ProbeSets
from the third stage 1 dataset, shown below.
Inside the Black Box
The traditional blackbox approach to Microarray data analysis uses a statistical comparison of
probes across samples in classes of the experiment at hand, discards (in some cases) or weights
component probes according to some ‘fitness to a model’ scheme, and then aggregates the
measurements to give a single ProbeSet value. Thereafter the ProbeSet value is the only factor
used as input to machine learning and statistical algorithm development [22-24, 58]. For
diagnostic purposes, if the predictive results of these methods are acceptable then the goal has
been achieved. However, biological investigators are often motivated by the desire to understand
the mechanisms that cause a gene to appear on such a list [7, 8, 20, 21, 50]. Being able to target
specific mechanisms may allow an investigator to select a ‘discarded’ probe for further study:
here we are thinking particularly of those probes that are discarded because they respond to SNPs
in the coding region, which may in fact be extremely important to the phenotype, if the
investigator can apply a follow-up test to qualify the samples. Despite our attempt to identify all
such factors, it is clear that we have not done so, since we end up with three response classes and
not two in the last analysis stage. We propose that, by doing Welch’s T-test at the probe level
during the aggregation process, an estimate of the presence of such factors is produced, and the
resulting probeset value can be annotated, i.e. affixed with a numerical or categorical denotation
(such as our ‘U’, ‘DE’ and ‘S’ labels), based upon the agreement of T-tests results.
Uninformative probesets thus are comprised only of probes showing no difference in the means
between classes (for a given allowed variation) while the DE probesets are comprised only of
probes all of which show a difference in means between classes, as depicted in 2.8. These
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