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STATISTICAL METHODS FOR THE ANALYSIS OF CANCER GENOME SEQUENCING DATA

• Giovanni Parmigiani, The Sydney Kimmel Comprehensive Cancer Center, Johns Hopkins University & Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health
• J. Lin, The Ludwig Center and the Howard Hughes Medical Institute, The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins
• Simina Boca, Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health
• T. Sjoblom, The Ludwig Centre and the Howard Hughes Medical Institute, The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins
• K.W. Kinzler, The Ludwig Centre and the Howard Hughes Medical Institute, The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins
• V.E. Velculescu, The Ludwig Centre and the Howard Hughes Medical Institute, The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins
• B. Vogelstein, The Ludwig Centre and the Howard Hughes Medical Institute, The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins

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Abstract:

The purpose of cancer genome sequencing studies is to determine the nature and types of alterations present in a typical cancer and to discover genes mutated at high frequencies. In this article we discuss statistical methods for the analysis of data generated in these studies. We place special emphasis on a two-stage study design introduced by Sjoblom et al.[1]. In this context, we describe statistical methods for constructing scores that can be used to prioritize candidate genes for further investigation and to assess the statistical signicance of the candidates thus identfied.

Subject Area:

Computational Biology/Bioinformatics

Suggested Citation:

Giovanni Parmigiani, J. Lin, Simina Boca, T. Sjoblom, K.W. Kinzler, V.E. Velculescu, and B. Vogelstein, "STATISTICAL METHODS FOR THE ANALYSIS OF CANCER GENOME SEQUENCING DATA" (October 2007). Johns Hopkins University, Dept. of Biostatistics Working Papers. Working Paper 126.
http://www.bepress.com/jhubiostat/paper126