Serial Analysis of Gene Expression (SAGE) has proven to be an important
alternative to microarray techniques for global profiling of mRNA
populations. We have developed preprocessing methodologies to address
problems in analyzing SAGE data due to noise caused by sequencing error,
normalization methodologies to account for libraries sampled at different
depths, and missing tag imputation methodologies to aid in the analysis of
poorly sampled SAGE libraries. We have also used subspace selection using the
Wilcoxon rank sum test to exclude tags that have similar expression levels
regardless of source. Using these methodologies we have clustered, using the
OPTICS algorithm, 88 SAGE libraries derived from cancerous and normal tissues
as well as cell line material. Our results produced eight dense clusters
representing ovarian cancer cell line, brain cancer cell line, brain cancer
bulk tissue, prostate tissue, pancreatic cancer, breast cancer cell line,
normal brain, and normal breast bulk tissue. The ovarian cancer and brain
cancer cell lines clustered closely together, leading to a further
investigation on possible associations between these two cancer types. We
also investigated the utility of gene expression data in the classification
between normal and cancerous tissues. Our results indicate that brain and
breast cancer libraries have strong identities allowing robust discrimination
from their normal counterparts. However, the SAGE expression data provide
poor predictive accuracy in discriminating between prostate and ovarian
cancers and their respective normal tissues.