skbio.diversity.block_beta_diversity#
- skbio.diversity.block_beta_diversity(metric, counts, ids, validate=True, k=64, reduce_f=None, map_f=None, **kwargs)[source]#
Perform a block-decomposition beta diversity calculation.
- Parameters:
- metricstr or callable
The pairwise distance function to apply. If
metricis a string, it must be resolvable by scikit-bio (e.g., UniFrac methods), or must be callable.- counts2D array_like of ints or floats
Matrix containing count/abundance data where each row contains counts of taxa in a given sample.
- idsiterable of strs
Identifiers for each sample in
counts.- validatebool, optional
See
skbio.diversity.beta_diversityfor details.- reduce_ffunction, optional
A method to reduce PartialDistanceMatrix objects into a single DistanceMatrix. The expected signature is:
f(Iterable of DistanceMatrix) -> DistanceMatrix
Note, this is the reduce within a map/reduce.
- map_f: function, optional
A method that accepts a _block_compute. The expected signature is:
f(**kwargs) -> DistanceMatrix
NOTE: ipyparallel’s map_async will not work here as we need to be able to pass around **kwargs`.
- kint, optional
The blocksize used when computing distances
- kwargskwargs, optional
Metric-specific parameters.
- Returns:
- DistanceMatrix
A distance matrix relating all samples represented by counts to each other.
Notes
This method is designed to facilitate computing beta diversity in parallel. In general, if you are processing a few hundred samples or less, then it is likely the case that skbio.diversity.beta_diversity will be faster. The original need which motivated the development of this method was processing the Earth Microbiome Project [1] dataset which at the time spanned over 25,000 samples and 7.5 million open reference taxa.
References