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Computes a manifold approximation and projection (UMAP) for phyloseq object

Source: R/plot_functions.R
umap_pq.Rd

lifecycle-experimental

https://journals.asm.org/doi/full/10.1128/msystems.00691-21

Usage

umap_pq(physeq, pkg = "umap", ...)

Arguments

physeq

(required) a phyloseq-class object obtained using the phyloseq package.

pkg

Which R packages to use, either "umap" or "uwot".

...

Additional arguments passed on to umap::umap() or uwot::umap2() function. For example n_neighbors set the number of nearest neighbors (Default 15). See umap::umap.defaults() or uwot::umap2() for the list of parameters and default values.

Value

A dataframe with samples informations and the x_umap and y_umap position

Details

This function is mainly a wrapper of the work of others. Please make a reference to umap::umap() if you use this function.

See also

umap::umap(), tsne_pq(), phyloseq::plot_ordination()

Author

Adrien Taudière

Examples

library("umap")
df_umap <- umap_pq(data_fungi_mini)
#> Taxa are now in columns.
#> Taxa are now in rows.
#> Joining with `by = join_by(Sample)`
#> Warning: The `x` argument of `as_tibble.matrix()` must have unique column names if
#> `.name_repair` is omitted as of tibble 2.0.0.
#> ℹ Using compatibility `.name_repair`.
#> ℹ The deprecated feature was likely used in the MiscMetabar package.
#>   Please report the issue at
#>   <https://github.com/adrientaudiere/MiscMetabar/issues>.
#> Joining with `by = join_by(Sample)`
ggplot(df_umap, aes(x = x_umap, y = y_umap, col = Height)) +
  geom_point(size = 2)


# \donttest{
library(patchwork)
physeq <- data_fungi_mini
res_tsne <- tsne_pq(data_fungi_mini)
df_umap_tsne <- df_umap
df_umap_tsne$x_tsne <- res_tsne$Y[, 1]
df_umap_tsne$y_tsne <- res_tsne$Y[, 2]
((ggplot(df_umap, aes(x = x_umap, y = y_umap, col = Height)) +
  geom_point(size = 2) +
  ggtitle("UMAP")) + (plot_ordination(physeq,
  ordination =
    ordinate(physeq, method = "PCoA", distance = "bray"), color = "Height"
)) +
  ggtitle("PCoA")) /
  ((ggplot(df_umap_tsne, aes(x = x_tsne, y = y_tsne, col = Height)) +
    geom_point(size = 2) +
    ggtitle("tsne")) +
    (plot_ordination(physeq,
      ordination = ordinate(physeq, method = "NMDS", distance = "bray"),
      color = "Height"
    ) +
      ggtitle("NMDS"))) +
  patchwork::plot_layout(guides = "collect")
#> Square root transformation
#> Wisconsin double standardization
#> Run 0 stress 0.1842417 
#> Run 1 stress 0.1830653 
#> ... New best solution
#> ... Procrustes: rmse 0.05975443  max resid 0.2477283 
#> Run 2 stress 0.1808269 
#> ... New best solution
#> ... Procrustes: rmse 0.07519717  max resid 0.2637364 
#> Run 3 stress 0.1802656 
#> ... New best solution
#> ... Procrustes: rmse 0.04975387  max resid 0.2982434 
#> Run 4 stress 0.183484 
#> Run 5 stress 0.184756 
#> Run 6 stress 0.1847681 
#> Run 7 stress 0.1819892 
#> Run 8 stress 0.1820539 
#> Run 9 stress 0.18634 
#> Run 10 stress 0.1856027 
#> Run 11 stress 0.1827823 
#> Run 12 stress 0.1816633 
#> Run 13 stress 0.1833599 
#> Run 14 stress 0.1842722 
#> Run 15 stress 0.1850547 
#> Run 16 stress 0.1860664 
#> Run 17 stress 0.1857189 
#> Run 18 stress 0.1795345 
#> ... New best solution
#> ... Procrustes: rmse 0.05055477  max resid 0.2998752 
#> Run 19 stress 0.1857847 
#> Run 20 stress 0.1831573 
#> *** Best solution was not repeated -- monoMDS stopping criteria:
#>     18: no. of iterations >= maxit
#>      2: stress ratio > sratmax


df_uwot <- umap_pq(data_fungi_mini, pkg = "uwot")
#> Taxa are now in columns.
#> Taxa are now in rows.
#> Joining with `by = join_by(Sample)`
#> Joining with `by = join_by(Sample)`

(ggplot(df_umap, aes(x = x_umap, y = y_umap, col = Height)) +
  geom_point(size = 2) +
  ggtitle("umap::umap")) /
  (ggplot(df_uwot, aes(x = x_umap, y = y_umap, col = Height)) +
    geom_point(size = 2) +
    ggtitle("uwot::umap2"))

# }