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caOmicsV: an R package for visualizing multidimensional cancer genomic data.

Hongen Zhang¹, Paul S Meltzer¹, Sean R Davis²

¹Genetics Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Building 37, Room 6138, 37 Convent Drive, Bethesda, MD, 20892-4265, USA.

BMC Bioinformatics

|March 24, 2016

Summary

This summary is machine-generated.

The caOmicsV R package offers a flexible solution for visualizing complex cancer genomic data. It aids researchers in understanding the links between genomic variations and cancer through intuitive graphical representations.

Keywords:

Genomic data visualization Multidimensional data visualization R package Software

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Area of Science:

Genomics
Bioinformatics
Cancer Research

Background:

Large-scale cancer genomics projects like ICGC and TCGA generate vast multidimensional datasets.
Effective visualization of integrated multidimensional data is crucial for clinical applications in cancer diagnosis, prognosis, and therapeutics.
Understanding the relationship between genomic variations and cancer requires advanced analytical tools.

Purpose of the Study:

To develop an R package for visualizing multidimensional cancer genomic data.
To provide tools that facilitate the interpretation of complex genomic datasets in cancer research.

Main Methods:

Implementation of the R package 'caOmicsV'.
Development of two distinct visualization layouts: matrix and combined biological network/circular.
Inclusion of supplemental functions for data preparation and default plotting methods for ease of use.

Main Results:

caOmicsV visualizes multidimensional cancer genomic data in R.
Supports matrix and combined biological network/circular layouts.
Accommodates various data types including gene expression, DNA methylation, copy number variations, and sample information.

Conclusions:

caOmicsV offers a user-friendly and adaptable method for visualizing integrated multidimensional cancer genomic data.
The package enhances the analysis and interpretation of cancer genomics datasets within the R environment.