PCA_score: Scatter Plot of 2 Specified Principal Components

In statVisual: Statistical Visualization Tools

Description

Scatter plot of 2 specified principal components. The size of the data points on the PCA plot indicates the Mahalanobis distance (distance between each point and mean value).

Usage

PCA_score(
    prcomp_obj, 
    data, 
    dims = c(1, 2),
    color = NULL, 
    MD = TRUE, 
    loadings = FALSE, 
    loadings.color = "black", 
    loadings.label = FALSE,
    title = "pca plot",
    addThemeFlag = TRUE)

Arguments

prcomp_obj	the object returned by the function prcomp.
data	A data frame. Rows are subjects; Columns are variables describing the subjects. The object prcomp_obj is based on data
dims	a numeric vector with 2 elements indicating which two principal components will be used to draw scatter plot.
color	character. The column name of data that indicates the subject groups. The data points on the PCA plot will be colored by the group info.
MD	logical. Indicate if the Mahalanobis distance (distance between each point and mean value) would be used to indicate the size of data points on the PCA plot
loadings	logical. Indicate if loading plot would be superimposed on the PCA plot. (default: FALSE)
loadings.color	character. Indicate the color of the loading axis.
loadings.label	logical. Indicating if loading labels should be added to the plot. (default: FALSE)
title	character. Figure title.
addThemeFlag	logical. Indicates if light blue background and white grid should be added to the figure.

Value

A list with 9 elements. data, layers, scales, mapping, theme, coordinates, facet, plot_env, and labels.

Author(s)

Wenfei Zhang <[e-mail]>, Weiliang Qiu <[e-mail]>, Xuan Lin <[e-mail]>, Donghui Zhang <[e-mail]>

Examples

library(factoextra)

data(esSim)
print(esSim)

# expression data
dat = exprs(esSim)
print(dim(dat))
print(dat[1:2,])

# phenotype data
pDat = pData(esSim)
print(dim(pDat))
print(pDat[1:2,])

# feature data
fDat = fData(esSim)
print(dim(fDat))
print(fDat[1:2,])

# choose the first 6 probes (3 OE probes, 2 UE probes, and 1 NE probe)
pDat$probe1 = dat[1,]
pDat$probe2 = dat[2,]
pDat$probe3 = dat[3,]
pDat$probe4 = dat[4,]
pDat$probe5 = dat[5,]
pDat$probe6 = dat[6,]

print(pDat[1:2, ])

# check histograms of probe 1 expression in cases and controls
print(table(pDat$grp, useNA = "ifany"))

pDat$grp = factor(pDat$grp)

###

pca.obj = iprcomp(pDat[, c(3:8)], scale. = TRUE)

# scree plot
factoextra::fviz_eig(pca.obj, addlabels = TRUE)

# scatter plot of PC1 vs PC2
statVisual(type = 'PCA_score',
           prcomp_obj = pca.obj, 
           dims = c(1, 2),
           data = pDat, 
           color = 'grp',
           loadings = FALSE)

PCA_score(prcomp_obj = pca.obj, 
          dims = c(1, 3),
          data = pDat, 
          color = 'grp',
          loadings = FALSE)

statVisual documentation built on Feb. 21, 2020, 1:08 a.m.