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

transformation and quantile normalization of the data by array [18]. The additive model is as
follows:
Y ' ( & ( %
ij
" , (2)
where i is the samples and j is the ProbeSets. This model averages the (PM only) probes per
i
sample, accepting some random error in the model ( % ij ), and assumes that the probes have been
designed such that the accumulated probe affinities, "j = 0 [18]. The algorithm implements the
median polish [16] to detect outlier probes, which violate the probe affinity assumption of the
model. In both of these algorithms, some ProbeSet value is determined for every set on the array.
In contrast, the BaFL cleansing protocol eliminates probes and through enforcing a minimum set
size (for statistical rigor) and consistency of set members across samples, may result in the
removal of entire ProbeSets. This often results in the absence of a large fraction of the original
data set: in the case of the Bhattacharjee dataset 66% of the original ProbeSets are removed. It
must be acknowledged that the disparity in the number of genes in the input set makes a
straightforward comparison of the output lists of the three methods problematic, but it is possible
to highlight some sources of error that the statistical methods do not identify and exclude. The
output data files are included in the Supplementary Materials, in the Data folder
Down-Selection
A Microarray experiment typically has 10,000 features to explore, at the gene level, of which
around half are expected to be expressed; generally only a small proportion of the genes will be
differentially expressed. Many of the statistical methods used to determine whether differences
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j
ij