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Associating expression and genomic data using co-occurrence measures

Maarten Larmuseau (UGent) , Lieven Verbeke (UGent) and Kathleen Marchal (UGent)
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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 between genes and validate our findings on the TCGA-BRCA study. Finally, we demonstrate how these co-occurrence measures can also be applied to link expression regimes to genomic aberrations, providing a more complete, integrative view on breast cancer. As a case study, an integrative analysis of the identified MLPH-FOXA1 association is performed, illustrating that the obtained expression associations are intimately linked to the underlying genomic changes.
Keywords
P53 BINDS, BREAST, GENE, MUTATIONS, ESTROGEN, PROMOTER, PATHWAY, Expression data, Co-expression, Data integration, Breast cancer

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Please use this url to cite or link to this publication:

Chicago
Larmuseau, Maarten, Lieven Verbeke, and Kathleen Marchal. 2019. “Associating Expression and Genomic Data Using Co-occurrence Measures.” Biology Direct 14.
APA
Larmuseau, Maarten, Verbeke, L., & Marchal, K. (2019). Associating expression and genomic data using co-occurrence measures. BIOLOGY DIRECT, 14.
Vancouver
1.
Larmuseau M, Verbeke L, Marchal K. Associating expression and genomic data using co-occurrence measures. BIOLOGY DIRECT. 2019;14.
MLA
Larmuseau, Maarten, Lieven Verbeke, and Kathleen Marchal. “Associating Expression and Genomic Data Using Co-occurrence Measures.” BIOLOGY DIRECT 14 (2019): n. pag. Print.
@article{8616975,
  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 between genes and validate our findings on the TCGA-BRCA study. Finally, we demonstrate how these co-occurrence measures can also be applied to link expression regimes to genomic aberrations, providing a more complete, integrative view on breast cancer. As a case study, an integrative analysis of the identified MLPH-FOXA1 association is performed, illustrating that the obtained expression associations are intimately linked to the underlying genomic changes.},
  articleno    = {10},
  author       = {Larmuseau, Maarten and Verbeke, Lieven and Marchal, Kathleen},
  issn         = {1745-6150},
  journal      = {BIOLOGY DIRECT},
  keywords     = {P53 BINDS,BREAST,GENE,MUTATIONS,ESTROGEN,PROMOTER,PATHWAY,Expression data,Co-expression,Data integration,Breast cancer},
  language     = {eng},
  pages        = {14},
  title        = {Associating expression and genomic data using co-occurrence measures},
  url          = {http://dx.doi.org/10.1186/s13062-019-0240-2},
  volume       = {14},
  year         = {2019},
}

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