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

Hereafter the pipeline which we present is referred to as BaFL, or Biologically applied Filter
Levels.
Black Box Strategies
A large number of purely statistical approaches have been applied (e.g. dCHIP, RMA, gcRMA)
[5, 22-24] to remove variation (sample or technical) unrelated to the factor of interest, but these
function as black-box techniques that do not enlighten the investigator about the extent that each
factor influences the experimental results. These methods tend to augment the data’s sensitivities
to classification algorithms; the outcome has been that the processed data performs well within
but not between experiments, using the same or different classifications methods [8]. The
implication is that these approaches over-train for the factors that apply in one experiment and
that those factors are not consistent in the next experiment. This would be expected if some of the
result is due to variables with systematic effects on a subset of particular probes, such as the
occurrence of different SNP-responsive probes that will give distinct patterns in different study
populations [9, 10, 12, 25-30]. In order to demonstrate that the data inconsistencies are
sample/population or platform dependent, an investigator needs to be able to delve into the
aggregated signal and identify discordant probes and the likely cause of their behavior, and then
perform follow-up assays as needed, such as genotyping samples. A black box method does not
allow the investigator to understand which particular type of secondary assay must be performed.
Our approach is to identify and remove all problematic probes in a progressive manner,
categorizing them as they are removed. Post- BaFL filtering the final set of data from all samples
gives a considerably more homogeneous response; in addition the investigator is provided
categorizations of the excluded sets that allow examination of each subcategory, and subsets of
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