Glioblastoma is a WHO grade IV tumor with high mortality rate. In order to identify the underlying biological causation of this disease, a comparative genomic hybridization dataset generated from 170 patients' tumor samples was analyzed. Of many available segmentation algorithms, I focused mainly on two most acceptable methods: Homogeneous Hidden Markov Models (HHMM) and Circular Binary Segmentation (CBS). Simulations show that CBS tends to give better segmentation result with low false discovery rate. HHMM failed to identify many obvious breakpoints that CBS identified. On the other hand, HHMM succeeds in identifying many single probe aberrations.

Applying other statistical algorithms revealed distinct biological fingerprints of Glioblastoma disease, which includes many signature genes and biological pathways. Survival analysis also reveals that several segments actually correlate to the extended survival time of some patients.	

In summary, this work shows the importance of statistical model or algorithms in the modern genomic research.