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

Discussion
A striking result from this series of experiments is the significant improvement in the power of
the t-test for the Bhattacharjee dataset, when the ProbeSets to be considered and the value of
those ProbeSets are produced using the BaFL pipeline [39, 40]. This increase in power was not
observed for any of the cleansing methodologies with the smaller Stearman dataset, which, is
much smaller, although it is completely replicated. From Figure 3.1 it can be seen that there is a
significant improvement in the uniformity of the p value kernel distributions of the BaFL-
generated ProbeSet values tested for significant differential expression for the Bhattacharjee
dataset, compared to those of RMA or dCHIP, particularly for the null p values [39]. Since both
datasets show similar variance after BaFL processing (see Chapter 2), and the Bhattacharjee
dataset is both more heterogeneous (disease stage) and less precise (less replication) than the
Stearman dataset, the lack of power comes down to the difference in sample size of the
experiments [5].
Models and Class Predictions
The impact of the lack of power becomes apparent in the performance of the down-selected RMA
and dCHIP-based classification models, where the resulting datasets have little improvement or
even a loss of performance when the t-test down-selection is used (compare the ALL set to the
other three Sets in the top two rows of graphs in Figures 3.2 and 3.3). A meaningful outcome
would show a gain in information when going from 12,000 to ~6,000 genes, where the method
has allowed the genes with an impact on the phenotype to be retrieved [6, 41]. This did not occur
when starting with the RMA and dCHIP cleansing methods, in spite of trying three types of
classification models in the search. In fact, there is a consistent increase in the variation across the
83