Venn diagram of phyloseq-class object using ggVennDiagram::ggVennDiagram function

Note that you can use ggplot2 function to customize the plot for ex. + scale_fill_distiller(palette = "BuPu", direction = 1) and + scale_x_continuous(expand = expansion(mult = 0.5)). See examples.

Usage

ggvenn_pq(
  physeq = NULL,
  fact = NULL,
  min_nb_seq = 0,
  taxonomic_rank = NULL,
  split_by = NULL,
  add_nb_samples = TRUE,
  add_nb_seq = FALSE,
  rarefy_before_merging = FALSE,
  rarefy_after_merging = FALSE,
  return_data_for_venn = FALSE,
  ...
)

Arguments

physeq

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

fact

(required): Name of the factor to cluster samples by modalities. Need to be in physeq@sam_data.

min_nb_seq

minimum number of sequences by OTUs by samples to take into count this OTUs in this sample. For example, if min_nb_seq=2,each value of 2 or less in the OTU table will not count in the venn diagram

taxonomic_rank

Name (or number) of a taxonomic rank to count. If set to Null (the default) the number of OTUs is counted.

split_by

Split into multiple plot using variable split_by. The name of a variable must be present in sam_data slot of the physeq object.

add_nb_samples

(logical, default TRUE) Add the number of samples to levels names

add_nb_seq

(logical, default FALSE) Add the number of sequences to levels names

rarefy_before_merging

Rarefy each sample before merging by the modalities of args fact. Use phyloseq::rarefy_even_depth() function

rarefy_after_merging

Rarefy each sample after merging by the modalities of args fact.

return_data_for_venn

(logical, default FALSE) If TRUE, the plot is not returned, but the resulting dataframe to plot with ggVennDiagram package is returned.

...

Other arguments for the ggVennDiagram::ggVennDiagram function for ex. category.names.

Value

A ggplot2 plot representing Venn diagram of modalities of the argument factor or if split_by is set a list of plots.

See also

upset_pq()

Author

Adrien Taudière

Examples

if (requireNamespace("ggVennDiagram")) {
  ggvenn_pq(data_fungi, fact = "Height")
}

# \donttest{
if (requireNamespace("ggVennDiagram")) {
  ggvenn_pq(data_fungi, fact = "Height") +
    ggplot2::scale_fill_distiller(palette = "BuPu", direction = 1)
  pl <- ggvenn_pq(data_fungi, fact = "Height", split_by = "Time")
  for (i in seq_along(pl)) {
    p <- pl[[i]] +
      scale_fill_distiller(palette = "BuPu", direction = 1) +
      theme(plot.title = element_text(hjust = 0.5, size = 22))
    print(p)
  }

  data_fungi2 <- subset_samples(data_fungi, data_fungi@sam_data$Tree_name == "A10-005" |
    data_fungi@sam_data$Height %in% c("Low", "High"))
  ggvenn_pq(data_fungi2, fact = "Height")

  ggvenn_pq(data_fungi, fact = "Height", add_nb_seq = TRUE, set_size = 4)
  ggvenn_pq(data_fungi, fact = "Height", rarefy_before_merging = TRUE)
  ggvenn_pq(data_fungi, fact = "Height", rarefy_after_merging = TRUE) +
    scale_x_continuous(expand = expansion(mult = 0.5))

  # For more flexibility, you can save the dataset for more precise construction
  # with ggplot2 and ggVennDiagramm
  # (https://gaospecial.github.io/ggVennDiagram/articles/fully-customed.html)
  res_venn <- ggvenn_pq(data_fungi, fact = "Height", return_data_for_venn = TRUE)

  ggplot() +
    # 1. region count layer
    geom_polygon(aes(X, Y, group = id, fill = name),
      data = ggVennDiagram::venn_regionedge(res_venn)
    ) +
    # 2. set edge layer
    geom_path(aes(X, Y, color = id, group = id),
      data = ggVennDiagram::venn_setedge(res_venn),
      show.legend = FALSE, linewidth = 3
    ) +
    # 3. set label layer
    geom_text(aes(X, Y, label = name),
      data = ggVennDiagram::venn_setlabel(res_venn)
    ) +
    # 4. region label layer
    geom_label(
      aes(X, Y, label = paste0(
        count, " (",
        scales::percent(count / sum(count), accuracy = 2), ")"
      )),
      data = ggVennDiagram::venn_regionlabel(res_venn)
    ) +
    theme_void()
}
#> Two modalities differ greatly (more than x2) in their number of sequences (159635 vs 53355). You may be interested by the parameter rarefy_after_merging




#> Two modalities differ greatly (more than x2) in their number of sequences (432919 vs 3961). You may be interested by the parameter rarefy_after_merging
#> You set `rngseed` to FALSE. Make sure you've set & recorded
#>  the random seed of your session for reproducibility.
#> See `?set.seed`
#> ...
#> 1018OTUs were removed because they are no longer 
#> present in any sample after random subsampling
#> ...
#> Cleaning suppress 0 taxa and 0 samples.
#> Warning: `group` has missing values; corresponding samples will be dropped
#> You set `rngseed` to FALSE. Make sure you've set & recorded
#>  the random seed of your session for reproducibility.
#> See `?set.seed`
#> ...
#> 160OTUs were removed because they are no longer 
#> present in any sample after random subsampling
#> ...
#> Cleaning suppress 0 taxa and 0 samples.

# }