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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 0.1842444 
#> Run 1 stress 0.1864672 
#> Run 2 stress 0.1816642 
#> ... New best solution
#> ... Procrustes: rmse 0.06035663  max resid 0.2763356 
#> Run 3 stress 0.1805408 
#> ... New best solution
#> ... Procrustes: rmse 0.0696832  max resid 0.2635835 
#> Run 4 stress 0.1918613 
#> Run 5 stress 0.1837193 
#> Run 6 stress 0.1856894 
#> Run 7 stress 0.1854912 
#> Run 8 stress 0.1855901 
#> Run 9 stress 0.1828179 
#> Run 10 stress 0.1797561 
#> ... New best solution
#> ... Procrustes: rmse 0.05144056  max resid 0.2994319 
#> Run 11 stress 0.1861731 
#> Run 12 stress 0.1861394 
#> Run 13 stress 0.1881243 
#> Run 14 stress 0.1837223 
#> Run 15 stress 0.18591 
#> Run 16 stress 0.1874962 
#> Run 17 stress 0.1823433 
#> Run 18 stress 0.1848213 
#> Run 19 stress 0.184988 
#> Run 20 stress 0.1831431 
#> *** Best solution was not repeated -- monoMDS stopping criteria:
#>     15: no. of iterations >= maxit
#>      5: 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"))

# }