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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 25.74325 7.672396  9.154861
#> 2 21.00831 5.665182  9.188979
#> 3 21.99348 7.046092  9.278208
#> 4 21.26223 6.744050  8.455117
#> 5 26.03726 8.543859 10.010594
#> 6 28.59126 8.696426 10.291358
head(pca_plot(iris[,1:4], method='umap', dims=3, plot=FALSE))
#>         D1          D2         D3
#> 1 17.04381 -1.47158322 -2.2488368
#> 2 15.68153 -0.17970860 -3.3249585
#> 3 15.47004 -0.36839761 -2.8585833
#> 4 15.31390 -0.07353435 -2.8990468
#> 5 17.05128 -1.69150118 -2.1813786
#> 6 16.91425 -1.76152263 -0.9563429

# 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)