plot_integrated_density_3D: Visualization of 3D Density of various groups on Principal...

In OMICsPCA: An R package for quantitative integration and analysis of multiple omics assays from heterogeneous samples

Description

works similarly as "plot_density_3D" . See vignettes and man page of "plot_density_3D" for more details.

Usage

plot_integrated_density_3D(name, PC1 = 1, PC2 = 2, group,
gridsize = 100, static = FALSE, groupinfo = NULL, ...)

Arguments

name	Name of the integrated "PCA" object
PC1, PC2	Numbers corresponding to the principal components on which density is to be calculated
group	Names of a group
gridsize	A number used in kernel smoothing. default is 100
static	Logical if TRUE a static plot is generated. default = FALSE
groupinfo	same as integrate_variables()
...	additional arguments allowed to base function "persp" of package "graphics"

Details

2D density is calculated using the "kde2d"" function from package "MASS"" which use kernel density estimation (KDE) to calculate density of 2D data. If the variance on either or both of the PCs are 0, the KDE can't be calculated.

Value

Displays 3D density plots.

Author(s)

Subhadeep Das <[e-mail]>

Examples

exclude <- list(0,c(1,9))

int_PCA <- integrate_pca(Assays = c("H2az",
"H3k9ac"), name = multi_assay, mergetype = 2,
exclude = exclude, groupinfo = groupinfo_ext,
,graph = FALSE)

name = int_PCA$int_PCA


plot_integrated_density_3D(name = name, PC1 = 1, PC2 = 2,
group = c("WE","RE"), gridsize = 100, static = FALSE,
groupinfo = groupinfo_ext)

OMICsPCA documentation built on Nov. 8, 2020, 5:01 p.m.