ArticleBMC bioinformatics2012
Matching of array CGH and gene expression microarray features for the purpose of integrative genomic analyses.
Article in BMC bioinformatics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Identification of Deregulated Pathways, Key Regulators, and Novel miRNA-mRNA Interactions in HPV-Mediated Transformation.Cancers · 2020Article
- Characterization of a head and neck cancer-derived cell line panel confirms the distinct TP53-proficient copy number-silent subclass.Oral oncology · 2019Article
- A prognostic mRNA expression signature of four 16q24.3 genes in radio(chemo)therapy-treated head and neck squamous cell carcinoma (HNSCC).Molecular oncology · 2018Article
- 4q21.2q21.3 Duplication: Molecular and Neuropsychological Aspects.Current genomics · 2018Review
- Genomic copy number analysis of Chernobyl papillary thyroid carcinoma in the Ukrainian-American Cohort.Carcinogenesis · 2015Article
- Combining multidimensional genomic measurements for predicting cancer prognosis: observations from TCGA.Briefings in bioinformatics · 2015Article
- tigaR: integrative significance analysis of temporal differential gene expression induced by genomic abnormalities.BMC bioinformatics · 2014Article
- Integrative radiation systems biology.Radiation oncology (London, England) · 2014Review
- Focal chromosomal copy number aberrations identify CMTM8 and GPR177 as new candidate driver genes in osteosarcoma.PloS one · 2014Article
- Integrated DNA Copy Number and Gene Expression Regulatory Network Analysis of Non-small Cell Lung Cancer Metastasis.Cancer informatics · 2014Article
- iBAG: integrative Bayesian analysis of high-dimensional multiplatform genomics data.Bioinformatics (Oxford, England) · 2013Article
- Integrative genomics with mediation analysis in a survival context.Computational and mathematical methods in medicine · 2013Article
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Authors and funding
7 authors.
Funding
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Abstract
backgroundAn increasing number of genomic studies interrogating more than one molecular level is published. Bioinformatics follows biological practice, and recent years have seen a surge in methodology for the integrative analysis of genomic data. Often such analyses require knowledge of which elements of one platform link to those of another. Although important, many integrative analyses do not or insufficiently detail the matching of the platforms.
resultsWe describe, illustrate and discuss six matching procedures. They are implemented in the R-package sigaR (available from Bioconductor). The principles underlying the presented matching procedures are generic, and can be combined to form new matching approaches or be applied to the matching of other platforms. Illustration of the matching procedures on a variety of data sets reveals how the procedures differ in the use of the available data, and may even lead to different results for individual genes.
conclusionsMatching of data from multiple genomics platforms is an important preprocessing step for many integrative bioinformatic analysis, for which we present six generic procedures, both old and new. They have been implemented in the R-package sigaR, available from Bioconductor.
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