MethylMix_PlotModel: The MethylMix_PlotModel function.
In gevaertlab/MethylMix: MethylMix: Identifying methylation driven cancer genes

Description Usage Arguments Value Examples

Produces plots to represent MethylMix's output.

1 2	MethylMix_PlotModel(GeneName, MixtureModelResults, METcancer, GEcancer = NULL, METnormal = NULL)

`GeneName`	Name of the gene for which to create a MethylMix plot.
`MixtureModelResults`	List returned by MethylMix function.
`METcancer`	Matrix with the methylation data of cancer tissue with genes in rows and samples in columns.
`GEcancer`	Gene expression data for cancer tissue with genes in rows and samples in columns (optional).
`METnormal`	Matrix with the normal methylation data of the same genes as in METcancer (optional). Again genes in rows and samples in columns.

a list with MethylMix plots, a histogram of the methylation data (MixtureModelPlot) and a scatterplot between DNA methylation and gene expression (CorrelationPlot, is NULL if gene expression data is not provided). Both plots show the different mixture components identified.

# Load the three data sets needed for MethylMix
data(METcancer)
data(METnormal)
data(GEcancer)

# Run methylmix on a small set of example data
MethylMixResults <- MethylMix(METcancer, GEcancer, METnormal)

# Plot the most famous methylated gene for glioblastoma
MethylMix_PlotModel("MGMT", MethylMixResults, METcancer)

# Plot MGMT also with its normal methylation variation
MethylMix_PlotModel("MGMT", MethylMixResults, METcancer, METnormal = METnormal)

# Plot a MethylMix model for another gene
MethylMix_PlotModel("ZNF217", MethylMixResults, METcancer, METnormal = METnormal)

# Also plot the inverse correlation with gene expression (creates two separate plots)
MethylMix_PlotModel("MGMT", MethylMixResults, METcancer, GEcancer, METnormal)

# Plot all functional and differential genes
for (gene in MethylMixResults$MethylationDrivers) {
     MethylMix_PlotModel(gene, MethylMixResults, METcancer, METnormal = METnormal)
}