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

Chapter 4: Data Mining
Adenocarcinoma is a non small cell (NSCLC) lung cancer sub-type, and the most frequent type of
lung cancer found in the world today [1, 2]. Adenocarcinomas are peripherally located in the
lungs and develop from clara cells, alveoli, and mucin producing cells [1]. While, tobacco
smoking has been well established as an initiating condition for lung cancer, with 80-90% of lung
cancer cases arising in tobacco smokers, adenocarcinoma in particular is most common among
women, non-smokers, and the young [1]. Given that the incidence rate of adenocarcinoma is
increasing and affecting non-traditional patients, understanding the disease is of immediate
concern [1, 2].
Using the methods described in the previous chapters, we have created two 2-class datasets, one
from a subset of the Bhattacharjee dataset and the other from the original Stearman (human
subset) experiments [3, 4]. In this chapter we begin with the down-selected ProbeSet list,
presented in Chapter 3, consisting of those 325 differentially expressed ProbeSets common to
both datasets. The values that the BaFL pipeline yields for these ProbeSets lead to datasets with
considerable latent structure; we will demonstrate that this latent structure is superior to that of
RMA and dCHIP supplied values using two widely-accepted dimensionality reduction methods:
Principal Components Analysis [5, 6], which is linear , and a Laplacian method which is non-
linear [7]. In validating the results of these analyses, we use sample correlation to explore the
gene/ProbeSet clusters.
92