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

It has long been known that the variation due to sample handling may be far greater than the
variation due to the primary experimental variable [17], but in the absence of internal controls and
general calibration standards we must resort to experiment-specific calibrations [18]. The total
fluorescence per array has been previously suggested as one test of batch consistency [19],
alternately represented as the average signal per probe or ProbeSet, although those investigators
did not incorporate the scanner limitation. This metric reflects the labeling efficiency per
molecule, but is not sensitive to sample degradation or large differences in the number of genes
expressed, so we extended the metric to include the total number of responsive probes in the
linear range [15, 16]. As indicated by the references given for each factor, individual investigators
have shown that each of these effects can have a significant impact on the outcome of an analysis,
yet, to the best of our knowledge, no one has put all of them together into a simple-to-use pipeline
and then tested the final effect on analysis and comparison of experiments.
The significance of the factors varies across datasets by sample characteristics that are
independent of the experimental factor ( i.e. still biological variation but not correlated to the
factor of interest and not subject to controls) and this type of biological variation has created
distinct dilemmas for the Microarray field: 1) cross experiment, particularly across platforms,
analysis has been deemed impractical and 2) resultant gene lists are not reproducible in
classification accuracy across datasets, across classification algorithms, and in their construction
[7, 8, 20, 21]. We will demonstrate that the commonly applied statistical algorithms interpret
signal intensities differently for each dataset, which changes individual ProbeSet’s significance
within each dataset. We will also demonstrate that by identifying and removing these types of
biological variation the behavior of the ProbeSets becomes more consistent with the experimental
factor across datasets, thereby minimizing the identified dilemmas in the Microarray field.
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