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Jointly Analyzing Gene Expression and Copy Number Data in Breast Cancer Using Data Reduction Models by J.A. Berger; S. Hautaniemi; S.K. Mitra; J. Astola is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

What is Jointly Analyzing Gene Expression and Copy Number Data in Breast Cancer Using Data Reduction Models about?

With the growing surge of biological measurements, the problem of integrating and analyzing different types of genomic measurements has become an immediate challenge for elucidating events at the molecular level. In order to address the problem of integrating different data types, we present a framework that locates variation patterns in two biological inputs based on the generalized singular value decomposition (GSVD). In this work, we jointly examine gene expression and copy number data and iteratively project the data on different decomposition directions defined by the projection angle theta in the GSVD. With the proper choice of theta, we locate similar and dissimilar patterns of variation between both data types. We discuss the properties of our algorithm using simulated data and conduct a case study with biologically verified results. Ultimately, we demonstrate the efficacy of our method on two genome-wide breast cancer studies to identify genes with large variation in expression and copy number across numerous cell line and tumor samples. Our method identifies genes that are statistically significant in both input measurements. The proposed method is useful for a wide varie

Who reads Jointly Analyzing Gene Expression and Copy Number Data in Breast Cancer Using Data Reduction Models?

Author

J.A. Berger; S. Hautaniemi; S.K. Mitra; J. Astola

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Published

2006

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)