epiPCA: Make PCA Plot from Comparison Matrix
In epihet: Determining Epigenetic Heterogeneity from Bisulfite Sequencing Data

Description Usage Arguments Value Examples

From a user-inputted value, creates a PCA plot from the sample data and colors each point by the subtype information provided.

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epiPCA(compare.matrix, value, type, points.colors = NULL,
  frames = FALSE, frames.colors = NULL, probability = FALSE,
  pdf.height = 10, pdf.width = 10, sve = FALSE)

`compare.matrix`	The comparison matrix generated from the compMatrix() function
`value`	The value to be graphed in the PCA plot
`type`	A dataframe containing the type information for the samples in the comparison matrix. The row names should be the names of the samples and there should be one column containing the type information for each sample.
`points.colors`	A vector to be used as the color of the individual points for each sample. One color is used per type. the names of vector is the types(default: NULL)
`frames`	A boolean stating if the frames should be drawn around the points for each subtype cluster. (default: False)
`frames.colors`	A vector of colors to be used as the color of the frames for each subtype cluster. (default: NULL)
`probability`	A boolean stating if the frames should be drawn as probability ellipses around the points for each subtype cluster. Both 'probability' and 'frames' must be set to TRUE to have effect. (default: False)
`pdf.height`	An integer representing the height (in inches) of the outputted PCA plot pdf file (default: 10)
`pdf.width`	An integer representing the width (in inches) of the outputted PCA plot pdf file (default: 10)
`sve`	A boolean to save the plot (default: FALSE)

A PCA plot

library(ggfortify)
comp.Matrix<-data.frame(
p1 = c(0.6,0.3,0.5,0.5,0.5,0.6,0.45,0.57,0.45,0.63,0.58,0.67,0.5,0.42,0.67),
p2 = c(0.62,0.63,0.55,0.75,0.84,0.58,1,0.33,1,0.97,0.57,0.68,0.73,0.72,0.82),
p3 = c(0.72,0.53,0.62,0.69,0.37,0.85,1,0.63,0.87,0.87,0.82,0.81,0.79,
0.62,0.68),
N1=c(0.15,0.24,0.15,0.26,0.34,0.32,0.23,0.14,0.26,0.32,0.12,0.16,0.31,
0.24,0.32),
N2=c(0.32,0.26,0.16,0.36,0.25,0.37,0.12,0.16,0.41,0.47,0.13,0.52,0.42,
0.41,0.23),
N3=c(0.21,0.16,0.32,0.16,0.36,0.27,0.24,0.26,0.11,0.27,0.39,0.5,0.4,
0.31,0.33),
type=rep(c("pdr","epipoly","shannon"),c(5,5,5)),
location=rep(c("chr22-327:350:361:364","chr22-755:761:771:773",
"chr22-761:771:773:781","chr22-821:837:844:849","chr22-838:845:850:858"),
3),stringsAsFactors =FALSE )

subtype <- data.frame(Type= c(rep('CEBPA_sil', 3), rep('Normal', 3)),
row.names = colnames(comp.Matrix)[1:6],stringsAsFactors = FALSE)

epiPCA(compare.matrix = comp.Matrix, value = 'epipoly',
              type = subtype, points.colors = NULL,
            frames = FALSE, frames.colors = NULL,
            probability = FALSE, pdf.height = 10,
            pdf.width = 10, sve = TRUE)