PREDICTION AND UNCERTAINTY IN THE ANALYSIS OF GENE EXPRESSION PROFILES
Rainer Spang, Harry Zuzan, Mike West, Joseph Nevins, Carrie Blanchette
and Jeffrey R. Marks
September 2000,
We have developed a complete statistical model for the analysis of
tumor specific gene expression profiles. It gives investigators
a global overview on large scale gene expression data, indicating
trends in the data as to which tumor phenotype a certain sample
belongs, but also summarizing the uncertainties inherent to these
trends.
In this paper we demonstrate the use of this method in the context
of a gene expression profiling study of 27 human breast cancers.
The study is aimed on unrevealing the molecular differences of
tumors with different estrogen receptor status.
In addition to good predictive performance with respect to
pure classification of the expression profiles, the model also uncovers
conflicts in the data with respect to the classification of some of the
tumors, highlighting them as critical cases where additional
investigations are appropriate