plot_pca: plot_pca

In ConesaLab/MultiBaC: Multiomic Batch effect Correction

Description

plot_pca

Usage

plot_pca(mbac, col.by.batch = TRUE, col.per.group = NULL,
  comp2plot = c(1, 2), typeP = "pca.both", legend.text = NULL,
  args.legend = NULL, ...)

Arguments

mbac	Object of class mbac generated by *createMbac*, *ARSyNbac*, *MultiBaC*, *genModelList*, or *batchCorrection*.
col.by.batch	Argument for PCA plots. TRUE or FALSE. If TRUE (default) samples are gruped according to the batch factor. If FALSE samples are gruped according to the experimental desing.
col.per.group	Argument for PCA plot. Indicates the color for each group defined in "groups" argument. If NULL (default) the colors are taken from a predefined pallete.
comp2plot	Indicates which components are plotted. Default is "c(1,2)", which means that component 1 is plotted in "x" axis and component 2 in "y" axis. If more components are indicated, the function will return as many plots as needed to show all the components.
typeP	"pca.cor", "pca.org" or "pca.both". If inputOmics contains original matrices, set "pca.org". However, if inputOmics contains the corrected matrices, set "pca.cor".
legend.text	a vector of text used to construct a legend for the plot.
args.legend	list of additional arguments to pass to legend(); names of the list are used as argument names. Only used if legend.text is supplied.
...	Other graphical arguments.

Value

A PCA plot is displayed.

Examples

data('multiyeast')

my_mbac <- createMbac (inputOmics = list(A.rna, A.gro, B.rna, B.ribo, C.rna, C.par),
                       batchFactor = c("A", "A", "B", "B", "C", "C"),
                       experimentalDesign = list("A" =  c("Glu+", "Glu+",
                       "Glu+", "Glu-", "Glu-", "Glu-"),
                       "B" = c("Glu+", "Glu+", "Glu-", "Glu-"),
                       "C" = c("Glu+", "Glu+", "Glu-", "Glu-")),
                       omicNames = c("RNA", "GRO", "RNA", "RIBO", "RNA", "PAR"))

plot_pca(my_mbac, typeP = "pca.org")

my_final_mbac <- MultiBaC (my_mbac,
                           test.comp = NULL, scale = FALSE,
                           center = TRUE, crossval = NULL,
                           Variability = 0.90,
                           Interaction = TRUE ,
                           showplot = FALSE,
                           showinfo = FALSE)

plot_pca(my_final_mbac, typeP = "pca.cor")

ConesaLab/MultiBaC documentation built on Jan. 24, 2022, 5:17 a.m.