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MVisAGe

DNA copy number changes (gains and losses) are commonly seen in tumor tissue. Although these events occur randomly throughout the genome, some provide a selective growth advantage because they lead to increased (gains) or decreased (losses) expression of affected genes. MVisAGe was designed to help users explore the effects of copy number changes and alterations in DNA methylation status on gene expression. Gene-level Pearson and Spearman correlation coefficients are computed and plotted according to the genomic position of the underlying genes.

Example

Please refer to the vignette.

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Version

Install

install.packages('MVisAGe')

Monthly Downloads

174

Version

0.2.1

License

GPL-3

Maintainer

Vonn Walter

Last Published

May 10th, 2018

Functions in MVisAGe (0.2.1)

cn.region.heatmap

A Function for Creating a Heatmap of DNA Copy Number Data
unsmooth.region.plot

A Function for Plotting Pearson Correlation Coefficients in a Given Genomic Region
corr.list.compute

A Function for Creating a List of Pearson Correlation Coefficients
exp.mat

Gene expression data from 100 head and neck squamous cell carcinoma (HNSC) patients
data.prep

A Function for Preparing mRNAseq and Copy Number Data Matrices
smooth.genome.plot

A Function for Plotting Smoothed Pearson Correlation Coefficients Across Multiple Chromosomes
cn.mat

DNA copy number data from 98 head and neck squamous cell carcinoma (HNSC) patients
smooth.region.plot

A Function for Plotting Smoothed Pearson Correlation Coefficients Genomewide
gene.annot

Gene annotation data (hg38)
corr.compute

A Function for Computing a Vector of Pearson Correlation Coefficients
perm.significance

A Function for Computing a Vector of Pearson Correlation Coefficients
tcga.cn.convert

A Function for Reformatting TCGA DNA Copy Number Matrices
perm.significance.list.compute

A Function for Creating a List of Pearson Correlation Coefficients
sample.annot

Sample annotation data
tcga.exp.convert

A Function for Reformatting TCGA mRNA Expression Matrices