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

platform: only the CDF and probe sequence files are required in order to flag problematic probes.
Thus the order of the first four steps is irrelevant and can be set to optimize the computational
efficiency. Using our data the cross hybridization filter (II), implemented here only for the PM
probes, reduces the dataset most drastically, so if it is applied first the succeeding steps will be
accomplished more quickly. Once steps (I)-(IV) have been completed the results are applicable
to any future experiments using the same chip design and sequence files. The last two steps
described above, (V) and (VI), are experiment/measurement dependent, and it is here that an
investigator’s choices will affect what appears in the final gene list. Scanner response limits can
be re-set in the code, to reflect the behavior of individual instruments
Batch and Sample Filtering
Technical steps will cause the amount of target, the labeling of that target and the effective length
of the target to vary independently of the biological factors. Similarly, biological factors, such as
secondary infections in cancer patients that lead to dramatic gene expression differences
compared to uninfected cancer patients, may obscure the effect of interest. Technical differences
tend to be seen in ‘batch’ effects, i.e. in groups of samples processed in parallel, while biological
effects must be screened by comparing an array to the set of all arrays in its class (which may
include multiple batches) [19]. The Bhattacharjee data set was explicitly batch annotated [47],
while for the Stearman dataset the scan date was used as a proxy for batch annotation: there were
4 dates but in 2-day pairs one month apart, so our assumption is that this reflects only two
technical batches. In the following discussion, both individual probe and aggregated ProbeSet
values were used to compare individual array to batch and sample class trends, as follows:
I. Probes-per-Sample
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