caOmicsV

caOmicsV visualizes multidimensional cancer genomics data from sources such as the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA), integrating patient information, RNA and miRNA expression, DNA methylation, DNA copy number variations, and single nucleotide polymorphisms/mutations to support translational and clinical genomic analyses.

Key Features:

• Multidimensional Data Integration: Integrates patient information, gene expression (RNA and miRNA), DNA methylation, DNA copy number variations, and single nucleotide polymorphisms/mutations into combined visual representations.
• Matrix Layout: Presents a structured matrix view that facilitates direct comparison of genomic features across samples.
• Combined Biological Network and Circular Layout: Merges biological network representations with circular plots to visualize relationships among genes and genomic features.
• Default Plotting Methods and Dataset Generation: Provides default plotting methods for both layouts and supplemental functions to generate plotting datasets from multiple genomic sources based on specified gene and sample names.
• Flexibility and Customization: Supports customization of visual elements and layout parameters to adapt visualizations to specific analysis needs.

Scientific Applications:

• Clinical Genomic Interpretation: Supports visualization for diagnosis, prognosis, and development of therapeutic strategies by integrating multidimensional genomic data.
• Translational Genomics Research: Facilitates integration and visual exploration of large-scale cancer genomics datasets from consortia such as ICGC and TCGA.
• Pattern and Correlation Discovery: Aids identification of patterns and correlations across gene expression, methylation, copy number, and mutation data.

Methodology:

Implemented in the R programming environment, caOmicsV leverages statistical computing to process and visualize large-scale genomic data and includes functions to assemble plotting datasets from multiple genomic sources using specified gene and sample names.