10:30 AM on Wednesday, December 5, 2012
Location: Virginia Tech Research Center -Arlington, Room 4-024
Invited Speaker: Dr. Qiu, Peng, from MD Anderson Cancer Center
We present a novel computational approach, Sample Progression Discovery (SPD), to discover patterns of biological progression underlying high-dimensional datasets. In contrast to the majority of microarray data analysis methods which focus on identifying differences between sample groups (i.e. normal vs. cancer, treated vs. control), SPD aims to identify an underlying progression among individual samples, both within and across sample groups. This is essentially a new way of asking questions. The traditional analyses ask the following question: what is the different between A and B. In this talk, I am going to ask a different question: how did A become B, or how did one biological sample/phenotype go through gradual changes and eventually progress into another phenotype. The SPD approach is designed to address this progression question. To demonstrate the potential of SPD to reveal biological processes underlying high-dimensional data, we applied it to gene expression datasets of cell cycle time series, B-cell differentiation, mouse embryonic stem cell differentiation, and prostate cancer. Each of these datasets is associated with a known biological progression. The known progression was hidden from the algorithm and was only used to validate the results. When applied to these datasets, SPD successfully recovered the underlying progression and genes that are associated with the progression. When applied to a dataset where the underlying progression is unknown, SPD may be best viewed as a novel tool for synthesizing biological hypotheses, because it provides a likely biological progression across the samples and, perhaps more importantly, the candidate genes that regulate the progression.