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,
  verbose = TRUE,
  type = "nb_taxa",
  na_remove = TRUE,
  ...
)

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.

verbose

(logical, default TRUE) If TRUE, prompt some messages.

type

If "nb_taxa" (default), the number of taxa (ASV, OTU or taxonomic_rank if taxonomic_rank is not NULL) is used in plot. If "nb_seq", the number of sequences is plotted. taxonomic_rank is never used if type = "nb_seq".

na_remove

(logical, default TRUE) If set to TRUE, remove samples with NA in the variables set in fact param

...

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")
}
#> 54 were discarded due to NA in variable fact
#> Cleaning suppress 501 taxa and 0 samples.
#> Cleaning suppress 457 taxa and 0 samples.
#> Cleaning suppress 469 taxa and 0 samples.

# \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_fungi2, fact = "Height", type = "nb_seq")

  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)
    ) +
    scale_fill_manual(values = funky_color(7)) +
    # 2. set edge layer
    geom_path(aes(X, Y, color = id, group = id),
      data = ggVennDiagram::venn_setedge(res_venn),
      show.legend = FALSE, linewidth = 2
    ) +
    scale_color_manual(values = c("red", "red", "blue")) +
    # 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()
}
#> 54 were discarded due to NA in variable fact
#> Cleaning suppress 501 taxa and 0 samples.
#> Cleaning suppress 457 taxa and 0 samples.
#> Cleaning suppress 469 taxa and 0 samples.
#> 54 were discarded due to NA in variable fact
#> Cleaning suppress 501 taxa and 0 samples.
#> Cleaning suppress 457 taxa and 0 samples.
#> Cleaning suppress 469 taxa and 0 samples.
#> Cleaning suppress 335 taxa and 0 samples.
#> Cleaning suppress 276 taxa and 0 samples.
#> Cleaning suppress 299 taxa and 0 samples.
#> Cleaning suppress 360 taxa and 0 samples.
#> Cleaning suppress 404 taxa and 0 samples.
#> Cleaning suppress 374 taxa and 0 samples.
#> Cleaning suppress 341 taxa and 0 samples.
#> Cleaning suppress 221 taxa and 0 samples.
#> Cleaning suppress 360 taxa and 0 samples.
#> Cleaning suppress 343 taxa and 0 samples.
#> Cleaning suppress 218 taxa and 0 samples.
#> Cleaning suppress 408 taxa and 0 samples.
#> 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




#> Cleaning suppress 501 taxa and 0 samples.
#> Cleaning suppress 457 taxa and 0 samples.
#> Cleaning suppress 1366 taxa and 0 samples.
#> 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
#> Cleaning suppress 501 taxa and 0 samples.
#> Cleaning suppress 457 taxa and 0 samples.
#> Cleaning suppress 1366 taxa and 0 samples.
#> 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
#> 54 were discarded due to NA in variable fact
#> Cleaning suppress 501 taxa and 0 samples.
#> Cleaning suppress 457 taxa and 0 samples.
#> Cleaning suppress 469 taxa and 0 samples.
#> 54 were discarded due to NA in variable fact
#> You set `rngseed` to FALSE. Make sure you've set & recorded
#>  the random seed of your session for reproducibility.
#> See `?set.seed`
#> ...
#> 1132OTUs were removed because they are no longer 
#> present in any sample after random subsampling
#> ...
#> Cleaning suppress 0 taxa and 0 samples.
#> Cleaning suppress 175 taxa and 0 samples.
#> Cleaning suppress 166 taxa and 0 samples.
#> Cleaning suppress 159 taxa and 0 samples.
#> 54 were discarded due to NA in variable fact
#> You set `rngseed` to FALSE. Make sure you've set & recorded
#>  the random seed of your session for reproducibility.
#> See `?set.seed`
#> ...
#> 161OTUs were removed because they are no longer 
#> present in any sample after random subsampling
#> ...
#> Cleaning suppress 0 taxa and 0 samples.
#> Cleaning suppress 387 taxa and 0 samples.
#> Cleaning suppress 341 taxa and 0 samples.
#> Cleaning suppress 343 taxa and 0 samples.
#> 54 were discarded due to NA in variable fact
#> Cleaning suppress 501 taxa and 0 samples.
#> Cleaning suppress 457 taxa and 0 samples.
#> Cleaning suppress 469 taxa and 0 samples.

# }