ecodive (version 2.2.2)

alpha_div: Alpha Diversity Wrapper Function

Description

Alpha Diversity Wrapper Function

Usage

alpha_div(
  counts,
  metric,
  norm = "percent",
  cutoff = 10L,
  digits = 3L,
  tree = NULL,
  margin = 1L,
  cpus = n_cpus()
)

Value

A numeric vector.

Arguments

counts

A numeric matrix of count data where each column is a feature, and each row is a sample. Any object coercible with as.matrix() can be given here, as well as phyloseq, rbiom, SummarizedExperiment, and TreeSummarizedExperiment objects. For optimal performance with very large datasets, see the guide in vignette('performance').

metric

The name of an alpha diversity metric. One of c('ace', 'berger', 'brillouin', 'chao1', 'faith', 'fisher', 'inv_simpson', 'margalef', 'mcintosh', 'menhinick', 'observed', 'shannon', 'simpson', 'squares'). Case-insensitive and partial name matching is supported. Programmatic access via list_metrics('alpha').

norm

Normalize the incoming counts. Options are:

norm = "percent" -

Relative abundance (sample abundances sum to 1).

norm = "binary" -

Unweighted presence/absence (each count is either 0 or 1).

norm = "clr" -

Centered log ratio.

norm = "none" -

No transformation.

Default: 'percent', which is the expected input for these formulas.

cutoff

The maximum number of observations to consider "rare". Default: 10.

digits

Precision of the returned values, in number of decimal places. E.g. the default digits=3 could return 6.392.

tree

A phylo-class object representing the phylogenetic tree for the OTUs in counts. The OTU identifiers given by colnames(counts) must be present in tree. Can be omitted if a tree is embedded with the counts object or as attr(counts, 'tree').

margin

If your samples are in the matrix's rows, set to 1L. If your samples are in columns, set to 2L. Ignored when counts is a phyloseq, rbiom, SummarizedExperiment, or TreeSummarizedExperiment object. Default: 1L

cpus

How many parallel processing threads should be used. The default, n_cpus(), will use all logical CPU cores.

Details

Integer Count Requirements

A frequent and critical error in alpha diversity analysis is providing the wrong type of data to a metric's formula. Some indices are mathematically defined based on counts of individuals and require raw, integer abundance data. Others are based on proportional abundances and can accept either integer counts (which are then converted to proportions) or pre-normalized proportional data. Using proportional data with a metric that requires integer counts will return an error message.

Requires Integer Counts Only

• Chao1

• ACE

• Squares Richness Estimator

• Margalef's Index

• Menhinick's Index

• Fisher's Alpha

• Brillouin Index

Can Use Proportional Data

• Observed Features

• Shannon Index

• Gini-Simpson Index

• Inverse Simpson Index

• Berger-Parker Index

• McIntosh Index

• Faith's PD

Examples

    # Example counts matrix
    ex_counts
    
    # Shannon diversity values
    alpha_div(ex_counts, 'Shannon')
    
    # Chao1 diversity values
    alpha_div(ex_counts, 'c')
    
    # Faith PD values
    alpha_div(ex_counts, 'faith', tree = ex_tree)