Associating expression and genomic data using co-occurrence measures.

ABSTRACT: Recent technological evolutions have led to an exponential increase in data in all the omics fields. It is expected that integration of these different data sources, will drastically enhance our knowledge of the biological mechanisms behind genomic diseases such as cancer. However, the integration of different omics data still remains a challenge. In this work we propose an intuitive workflow for the integrative analysis of expression, mutation and copy number data taken from the METABRIC study on breast cancer. First, we present evidence that the expression profile of many important breast cancer genes consists of two modes or 'regimes', which contain important clinical information. Then, we show how the co-occurrence of these expression regimes can be used as an association measure betwe

SUBMITTER: Larmuseau M 

PROVIDER: S-EPMC6507230 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature