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,
  rngseed = 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.
rngseed: (Optional). A single integer value passed to phyloseq::rarefy_even_depth(), which is used to fix a seed for reproducibly random number generation (in this case, reproducibly random subsampling). If set to FALSE, then no fiddling with the RNG seed is performed, and it is up to the user to appropriately call set.seed beforehand to achieve reproducible results. Default is FALSE.
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.

Author

Adrien Taudière

Examples

if (requireNamespace("ggVennDiagram")) {
  ggvenn_pq(data_fungi_mini, fact = "Height")
}
#> 47 were discarded due to NA in variable fact
#> Taxa are now in columns.
#> Cleaning suppress 11 taxa and 0 samples.
#> Cleaning suppress 11 taxa and 0 samples.
#> Cleaning suppress 11 taxa and 0 samples.

# \donttest{
if (requireNamespace("ggVennDiagram")) {
  ggvenn_pq(data_fungi_mini, fact = "Height") +
    ggplot2::scale_fill_distiller(palette = "BuPu", direction = 1)
  pl <- ggvenn_pq(data_fungi_mini, 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_mini,
    data_fungi_mini@sam_data$Tree_name == "A10-005" |
    data_fungi_mini@sam_data$Height %in% c("Low", "High"))
  ggvenn_pq(data_fungi2, fact = "Height")

  ggvenn_pq(data_fungi2, fact = "Height", type = "nb_seq")

  ggvenn_pq(data_fungi_mini, fact = "Height", add_nb_seq = TRUE, set_size = 4)
  ggvenn_pq(data_fungi_mini, fact = "Height", rarefy_before_merging = TRUE)
  ggvenn_pq(data_fungi_mini, 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_mini, 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()
}
#> 47 were discarded due to NA in variable fact
#> Taxa are now in columns.
#> Cleaning suppress 11 taxa and 0 samples.
#> Cleaning suppress 11 taxa and 0 samples.
#> Cleaning suppress 11 taxa and 0 samples.
#> 47 were discarded due to NA in variable fact
#> Taxa are now in columns.
#> Cleaning suppress 11 taxa and 0 samples.
#> Cleaning suppress 11 taxa and 0 samples.
#> Cleaning suppress 11 taxa and 0 samples.
#> Cleaning suppress 10 taxa and 0 samples.
#> Cleaning suppress 6 taxa and 0 samples.
#> Cleaning suppress 9 taxa and 0 samples.
#> Cleaning suppress 15 taxa and 0 samples.
#> Cleaning suppress 11 taxa and 0 samples.
#> Cleaning suppress 13 taxa and 0 samples.
#> Two modalities differ greatly (more than x2) in their number of sequences (29085 vs 4489). You may be interested by the parameter rarefy_after_merging
#> Cleaning suppress 9 taxa and 0 samples.
#> Cleaning suppress 7 taxa and 0 samples.
#> Cleaning suppress 12 taxa and 0 samples.
#> Two modalities differ greatly (more than x2) in their number of sequences (13275 vs 5567). You may be interested by the parameter rarefy_after_merging
#> Cleaning suppress 6 taxa and 0 samples.
#> Cleaning suppress 5 taxa and 0 samples.
#> Cleaning suppress 6 taxa and 0 samples.
#> Two modalities differ greatly (more than x2) in their number of sequences (43910 vs 20393). You may be interested by the parameter rarefy_after_merging




#> Taxa are now in columns.
#> Cleaning suppress 11 taxa and 0 samples.
#> Cleaning suppress 11 taxa and 0 samples.
#> Cleaning suppress 38 taxa and 0 samples.
#> Two modalities differ greatly (more than x2) in their number of sequences (87919 vs 1134). You may be interested by the parameter rarefy_after_merging
#> Taxa are now in columns.
#> Cleaning suppress 11 taxa and 0 samples.
#> Cleaning suppress 11 taxa and 0 samples.
#> Cleaning suppress 38 taxa and 0 samples.
#> Two modalities differ greatly (more than x2) in their number of sequences (87919 vs 1134). You may be interested by the parameter rarefy_after_merging
#> 47 were discarded due to NA in variable fact
#> Taxa are now in columns.
#> Cleaning suppress 11 taxa and 0 samples.
#> Cleaning suppress 11 taxa and 0 samples.
#> Cleaning suppress 11 taxa and 0 samples.
#> 47 were discarded due to NA in variable fact
#> Taxa are now in columns.
#> You set `rngseed` to FALSE. Make sure you've set & recorded
#>  the random seed of your session for reproducibility.
#> See `?set.seed`
#> ...
#> You set `rngseed` to FALSE. Make sure you've set & recorded
#>  the random seed of your session for reproducibility.
#> See `?set.seed`
#> ...
#> 16OTUs were removed because they are no longer 
#> present in any sample after random subsampling
#> ...
#> Cleaning suppress 14 taxa and 0 samples.
#> Cleaning suppress 10 taxa and 0 samples.
#> Cleaning suppress 12 taxa and 0 samples.
#> 47 were discarded due to NA in variable fact
#> Taxa are now in columns.
#> You set `rngseed` to FALSE. Make sure you've set & recorded
#>  the random seed of your session for reproducibility.
#> See `?set.seed`
#> ...
#> You set `rngseed` to FALSE. Make sure you've set & recorded
#>  the random seed of your session for reproducibility.
#> See `?set.seed`
#> ...
#> 4OTUs were removed because they are no longer 
#> present in any sample after random subsampling
#> ...
#> Cleaning suppress 8 taxa and 0 samples.
#> Cleaning suppress 7 taxa and 0 samples.
#> Cleaning suppress 8 taxa and 0 samples.
#> 47 were discarded due to NA in variable fact
#> Taxa are now in columns.
#> Cleaning suppress 11 taxa and 0 samples.
#> Cleaning suppress 11 taxa and 0 samples.
#> Cleaning suppress 11 taxa and 0 samples.

# }

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

Usage

Arguments

Value

See also

Author

Examples