met.plot_PCA2DScore | R Documentation |
met.plot_PCA2DScore
visualizes clusters of samples based on their similarity in principal component analysis.
met.plot_PCA2DScore(
mSetObj = NA,
imgName = "PCA_2DScores",
format = "pdf",
dpi = NULL,
subtitle = FALSE,
width = NA,
pcx,
pcy,
reg = 0.95,
show = 1,
grey.scale = 0,
plot = TRUE,
export = TRUE
)
mSetObj |
Input name of the created mSet object,
Data container after principal component analysis ( |
imgName |
(Character) Enter a name for the image file (if |
format |
(Character, |
dpi |
(Numeric) resolution of the image file (if |
subtitle |
(Logical, |
width |
(Numeric) width of the the image file in inches (if |
pcx |
Specify the principal component on the x-axis |
pcy |
Specify the principal component on the y-axis |
reg |
Numeric, input a number between 0 and 1, 0.95 will display the 95 percent confidence regions, and 0 will not. |
show |
Display sample names, |
grey.scale |
Use grey-scale colors, |
plot |
(Logical, |
export |
(Logical, |
The input mSet object with added scatter plot. The plot can be retrieved from within R via print(mSetObj$imgSet$pca.score2d_PCx_PCy.plot)
.
Nicolas T. Wirth mail.nicowirth@gmail.com Technical University of Denmark License: GNU GPL (>= 2)
adapted from PlotPCA2DScore
(https://github.com/xia-lab/MetaboAnalystR).
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