Plot accumulation curves with balanced modality and depth rarefaction

This function (i) rarefy (equalize) the number of samples per modality of a factor and (ii) rarefy the number of sequences per sample (depth). The seed is set to 1:nperm. Thus, with exacly the same parameter, including nperm values, results must be identical.

Usage

accu_plot_balanced_modality(
  physeq,
  fact,
  nperm = 99,
  step = 2000,
  by.fact = TRUE,
  progress_bar = TRUE,
  quantile_prob = 0.975,
  rarefy_by_sample_before_merging = TRUE,
  sample.size = 1000,
  verbose = FALSE,
  ...
)

Arguments

physeq

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

fact

(required) The variable to rarefy. Must be present in the sam_data slot of the physeq object.

nperm

(int) The number of permutations to perform.

step

(int) distance among points calculated to plot lines. A low value give better plot but is more time consuming.

by.fact

(logical, default TRUE) First merge the OTU table by factor to plot only one line by factor

progress_bar

(logical, default TRUE) Do we print progress during the calculation?

quantile_prob

(float, [0:1]) the value to compute the quantile. Minimum quantile is compute using 1-quantile_prob.

rarefy_by_sample_before_merging

(logical, default TRUE): rarefy_by_sample_before_merging = FALSE is buggy for the moment.Please only use rarefy_by_sample_before_merging = TRUE

sample.size

(int) A single integer value equal to the number of reads being simulated, also known as the depth. See phyloseq::rarefy_even_depth().

verbose

(logical). If TRUE, print additional informations.

...

Other params for be passed on to accu_plot() function

Value

A ggplot2 plot representing the richness accumulation plot

See also

accu_plot(), rarefy_sample_count_by_modality(), phyloseq::rarefy_even_depth()

Author

Adrien Taudière

Examples

# \donttest{
data_fungi_woNA4Time <-
  subset_samples(data_fungi, !is.na(Time))
data_fungi_woNA4Time@sam_data$Time <- paste0("time-", data_fungi_woNA4Time@sam_data$Time)
accu_plot_balanced_modality(data_fungi_woNA4Time, "Time", nperm = 3)
#> `set.seed(1)` was used to initialize repeatable random subsampling.
#> Please record this for your records so others can reproduce.
#> Try `set.seed(1); .Random.seed` for the full vector
#> ...
#> Warning: no non-missing arguments to max; returning -Inf
#> 
  |                                                        
  |                                                  |   0%
  |                                                        
  |=================                                 |  33%
  |                                                        
  |=================================                 |  67%
  |                                                        
  |==================================================| 100%
#> Warning: Removed 4 rows containing missing values or values outside the scale range
#> (`geom_line()`).


data_fungi_woNA4Height <-
  subset_samples(data_fungi, !is.na(Height))
accu_plot_balanced_modality(data_fungi_woNA4Height, "Height", nperm = 3)
#> `set.seed(1)` was used to initialize repeatable random subsampling.
#> Please record this for your records so others can reproduce.
#> Try `set.seed(1); .Random.seed` for the full vector
#> ...
#> Warning: no non-missing arguments to max; returning -Inf
#> 
  |                                                        
  |                                                  |   0%
  |                                                        
  |=================                                 |  33%
  |                                                        
  |=================================                 |  67%
  |                                                        
  |==================================================| 100%
#> Warning: Removed 3 rows containing missing values or values outside the scale range
#> (`geom_line()`).

# }