Build a sample information tibble from physeq object

Hill numbers are the number of equiprobable species giving the same diversity value as the observed distribution.

Note that contrary to hill_pq(), this function does not take into account for difference in the number of sequences per samples/modalities. You may use rarefy_by_sample = TRUE if the mean number of sequences per samples differs among modalities.

Usage

psmelt_samples_pq(
  physeq,
  q = c(0, 1, 2),
  hill_scales = lifecycle::deprecated(),
  filter_zero = TRUE,
  rarefy_by_sample = FALSE,
  rngseed = FALSE,
  verbose = TRUE,
  taxa_ranks = NULL,
  ...
)

Arguments

physeq

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

q

(numeric vector) Hill diversity orders to compute. If NULL, no Hill numbers are computed. Default computes Hill number 0 (species richness), 1 (exponential of Shannon index) and 2 (inverse of Simpson index). Formerly q. Hill numbers are more appropriate in DNA metabarcoding studies when q > 0 (Alberdi & Gilbert, 2019; Calderón-Sanou et al., 2019).

hill_scales

Use q instead.

filter_zero

(logical, default TRUE) Do we filter non present OTU from samples ? For the moment, this has no effect on the result because the dataframe is grouped by samples with abundance summed across OTU.

rarefy_by_sample

(logical, default FALSE) If TRUE, rarefy samples using phyloseq::rarefy_even_depth() function.

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.

verbose

(logical). If TRUE, print additional information.

taxa_ranks

A vector of taxonomic ranks. For examples c("Family","Genus"). If taxa ranks is not set (default value = NULL), taxonomic information are not present in the resulting tibble.

...

Additional arguments passed to divent_hill_matrix_pq() and hence to divent::div_hill() (e.g. estimator = "naive"). Only used when q is not NULL.

Value

A tibble with a row for each sample. Columns provide information from sam_data slot as well as hill numbers, Abundance (nb of sequences), and Abundance_log10 (log10(1+Abundance)).

References

Alberdi, A., & Gilbert, M. T. P. (2019). A guide to the application of Hill numbers to DNA-based diversity analyses. Molecular Ecology Resources. doi:10.1111/1755-0998.13014

Calderón-Sanou, I., Münkemüller, T., Boyer, F., Zinger, L., & Thuiller, W. (2019). From environmental DNA sequences to ecological conclusions: How strong is the influence of methodological choices? Journal of Biogeography, 47. doi:10.1111/jbi.13681

Author

Adrien Taudière

Examples

if (requireNamespace("ggstatsplot")) {
  psm_tib <- psmelt_samples_pq(data_fungi_mini, hill_scales = c(0, 2, 7))
  ggstatsplot::ggbetweenstats(psm_tib, Height, Hill_0)
  ggstatsplot::ggbetweenstats(psm_tib, Height, Hill_7)

  psm_tib_tax <- psmelt_samples_pq(data_fungi_mini, taxa_ranks = c("Class", "Family"))
  ggplot(filter(psm_tib_tax, Abundance > 2000), aes(y = Family, x = Abundance, fill = Time)) +
    geom_bar(stat = "identity") +
    facet_wrap(~Height)
}
#> Warning: The `hill_scales` argument of `psmelt_samples_pq()` is deprecated as of
#> MiscMetabar 0.15.1.
#> ℹ Please use the `q` argument instead.
#> ! Sample coverage is 0, most estimators will return `NaN`.
#> ! Sample coverage is 0, most estimators will return `NaN`.
#> ! Sample coverage is 0, most estimators will return `NaN`.
#> ! Sample coverage is 0, most estimators will return `NaN`.
#> ! Sample coverage is 0, most estimators will return `NaN`.
#> ! Sample coverage is 0, most estimators will return `NaN`.
#> Joining with `by = join_by(Sample)`
#> ! Sample coverage is 0, most estimators will return `NaN`.
#> ! Sample coverage is 0, most estimators will return `NaN`.
#> ! Sample coverage is 0, most estimators will return `NaN`.
#> ! Sample coverage is 0, most estimators will return `NaN`.
#> ! Sample coverage is 0, most estimators will return `NaN`.
#> ! Sample coverage is 0, most estimators will return `NaN`.
#> Joining with `by = join_by(Sample)`