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Make a PCA/tSNE/UMAP plot

pca_plot.Rd

The function allows the passing of some parameters to Rtsne/umap:

  • Rtsne: perplexity

  • umap: nn (number of neighbors)

Usage

pca_plot(
  X,
  grp,
  txt = FALSE,
  method = c("pca", "tsne", "umap"),
  dims = 2,
  gg = TRUE,
  ellipse = FALSE,
  legend,
  repel = FALSE,
  plot = TRUE,
  ...
)

Arguments

X

A numeric matrix

grp

Optional grouping factor to color instances by

txt

Use rownames as labels instead of dots (default: false)

method

How to carry out dimension reduction (pca/tsne/umap)

dims

Dimensions to return (for tsne)

gg

Use ggplot2? (default: true)

ellipse

Draw mvn-ellipses around groups? (default: false, only available for ggplot)

legend

Include a legend (default: true)

repel

If gg=TRUE and txt=TRUE, use ggrepel::geom_text_repel()? (default: false)

plot

Create plot? (default: true)

...

Further arguments to plot()

Value

If base plot, returns PCA object invisibly. If ggplot, returns the plot object.

Examples

# Plots of the iris data
pca_plot(iris[,1:4])

pca_plot(iris[,1:4], grp=iris[,5])

pca_plot(iris[,1:4], grp=iris[,5], ellipse=TRUE)

pca_plot(iris[,1:4], grp=iris[,5], method='tsne')

pca_plot(iris[,1:4], grp=iris[,5], method='umap')


# What pca_plot returns if plot=FALSE
head(pca_plot(iris[,1:4], method='pca', plot=FALSE))
#>         PC1        PC2         PC3          PC4
#> 1 -2.257141  0.4784238 -0.12727962 -0.024087508
#> 2 -2.074013 -0.6718827 -0.23382552 -0.102662845
#> 3 -2.356335 -0.3407664  0.04405390 -0.028282305
#> 4 -2.291707 -0.5953999  0.09098530  0.065735340
#> 5 -2.381863  0.6446757  0.01568565  0.035802870
#> 6 -2.068701  1.4842053  0.02687825 -0.006586116
head(pca_plot(iris[,1:4], method='tsne', dims=3, plot=FALSE))
#>         D1        D2       D3
#> 1 14.81675 -5.620704 22.30102
#> 2 12.14287 -3.562962 18.30364
#> 3 13.78713 -4.382672 18.98358
#> 4 12.88333 -4.854907 18.23502
#> 5 16.07073 -5.663654 22.38906
#> 6 17.39607 -5.150726 24.65361
head(pca_plot(iris[,1:4], method='umap', dims=3, plot=FALSE))
#>         D1         D2          D3
#> 1 12.79487 -0.9650911 -1.71246066
#> 2 11.56405 -1.7851902 -0.27578632
#> 3 12.08099 -1.9893661  0.10532305
#> 4 11.82020 -1.8694437 -0.04011564
#> 5 12.87413 -1.2837349 -1.68737759
#> 6 13.22625 -0.9911514 -2.75013840

# Plots of the USArrests data
pca_plot(USArrests, txt=TRUE)

pca_plot(USArrests, txt=TRUE, repel=TRUE)

pca_plot(USArrests, txt=TRUE, grp=rep(LETTERS[1:5], each=10))

pca_plot(USArrests, txt=TRUE, method='tsne', perplexity=10)

pca_plot(USArrests, txt=TRUE, method='umap', nn=6)