Max-Planck-Institut für Informatik
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Analysis of arrayCGH Data

During tumor progression, chromosome regions are deleted or amplified, resulting in genetic imbalances which promote uncontrolled cell proliferation. ArrayCGH data provides information on gene copy number changes, but the signal is often altered by experimental noise. We develop statistical algorithms for the analysis of arrayCGH data in order to detect biologically and clinically relevant aberrations. From a biological point of view, the detection of narrow, highly recurrent aberrations is interesting as those might contain genes involved in tumor onset and development, whereas a meaningful statistical selection of recurrent alterations can help to define genetic markers for predicting tumor stage, tumor subtype or survival time.

The methodology we propose entails several successive noise reduction steps. We first smooth the arrays in order to eliminate experimental noise, using established algorithms proposed in the literature. Then we derive robust thresholds for the magnitude of relevant aberrations. However, our main objective is the analysis of sets of arrays from the same tumor type which reveal common patterns of aberrations. We propose a novel algorithm for automatically selecting amplifications and deletions that are informative with respect to the degree of tumor progression.


  1. Laura Tolosi
    Analysis of arrayCGH data for the Estimation of Genetic Tumor Progression
    Master thesis, 2006

Contact: Tolosi, Laura <>