GetBetaDiversity

This function calculates the beta diversity for a given mmo object, method (Generalized Unifrac, bray, jaccard, or CSCS), normalization method, distance metric, and optional feature filtering. Then it returns a distance matrix of beta diversity values. The Generalized UniFrac and CSCS method requires a distance matrix of feature dissimilarity, which is calculated using the specified distance metric. Bray-Curtis and Jaccard methods are calculated using the vegan package, not considering feature dissimilarity.

Usage

GetBetaDiversity(
  mmo,
  method = "Gen.Uni",
  normalization = "None",
  distance = "dreams",
  filter_feature = FALSE,
  feature_list = NULL,
  filter_group = FALSE,
  group_list = NULL
)

Arguments

mmo

The mmo object containing feature data and metadata

method

The method of beta diversity calculation. Options are 'Gen.Uni' for Generalized UniFrac, 'bray' for Bray-Curtis, 'jaccard' for Jaccard, or 'CSCS' for Compound Similarity and Chemical structural and compositional similarity (default: 'Gen.Uni')

normalization

The normalization method to use for feature data. Options are 'None', 'Log', 'Meancentered', or 'Z' (default: 'None')

distance

The distance metric to use for calculating dissimilarity. Options are 'dreams', 'm2ds', or 'cosine' (default: 'dreams')

filter_feature

A boolean indicating whether to filter the feature data by a specific list (default: FALSE)

feature_list

A list of feature names to filter the feature data by, if filter_feature is TRUE (default: NULL)

filter_group

A boolean indicating whether to filter the feature data by a specific group list (default: FALSE)

group_list

A list of groups to filter the feature data by, if filter_group is TRUE (default: NULL)

Value

A distance matrix of beta diversity values between samples.

Examples

if (FALSE) {
beta_diversity <- GetBetaDiversity(mmo, method = 'Gen.Uni', 
 normalization = 'None', distance = 'dreams', filter_feature = FALSE)
beta_diversity <- GetBetaDiversity(mmo, method = 'bray', 
 normalization = 'Z', filter_feature = TRUE, feature_list = Glucosinolates, 
 filter_group = TRUE, group_list = c('Control', 'Treatment1'))
}