skbio.diversity.alpha.
faith_pd
(counts, otu_ids, tree, validate=True)[source]¶Compute Faith’s phylogenetic diversity metric (PD)
State: Experimental as of 0.4.1.
counts (1-D array_like, int) – Vectors of counts/abundances of OTUs for one sample.
otu_ids (list, np.array) – Vector of OTU ids corresponding to tip names in tree
. Must be the
same length as counts
.
tree (skbio.TreeNode) – Tree relating the OTUs in otu_ids. The set of tip names in the tree can
be a superset of otu_ids
, but not a subset.
validate (bool, optional) – If False, validation of the input won’t be performed. This step can
be slow, so if validation is run elsewhere it can be disabled here.
However, invalid input data can lead to invalid results or error
messages that are hard to interpret, so this step should not be
bypassed if you’re not certain that your input data are valid. See
skbio.diversity
for the description of what validation entails
so you can determine if you can safely disable validation.
The phylogenetic diversity (PD) of the samples.
float
ValueError, MissingNodeError, DuplicateNodeError – If validation fails. Exact error will depend on what was invalid.
Notes
Faith’s phylogenetic diversity, often referred to as PD, was originally described in 1.
If computing Faith’s PD for multiple samples, using
skbio.diversity.alpha_diversity
will be much faster than calling this
function individually on each sample.
This implementation differs from that in PyCogent (and therefore QIIME versions less than 2.0.0) by imposing a few additional restrictions on the inputs. First, the input tree must be rooted. In PyCogent, if an unrooted tree was provided that had a single trifurcating node (a newick convention for unrooted trees) that node was considered the root of the tree. Next, all OTU IDs must be tips in the tree. PyCogent would silently ignore OTU IDs that were not present the tree. To reproduce Faith PD results from PyCogent with scikit-bio, ensure that your PyCogent Faith PD calculations are performed on a rooted tree and that all OTU IDs are present in the tree.
This implementation of Faith’s PD is based on the array-based implementation of UniFrac described in 2.
References
Faith, D. P. Conservation evaluation and phylogenetic diversity. Biol. Conserv. (1992).
Hamady M, Lozupone C, Knight R. Fast UniFrac: facilitating high- throughput phylogenetic analyses of microbial communities including analysis of pyrosequencing and PhyloChip data. ISME J. 4(1):17-27 (2010).
Examples
Assume we have the following abundance data for a sample u
,
represented as a counts vector. These counts represent the
number of times specific Operational Taxonomic Units, or OTUs, were
observed in the sample.
>>> u_counts = [1, 0, 0, 4, 1, 2, 3, 0]
Because Faith PD is a phylogenetic diversity metric, we need to know which
OTU each count corresponds to, which we’ll provide as otu_ids
.
>>> otu_ids = ['OTU1', 'OTU2', 'OTU3', 'OTU4', 'OTU5', 'OTU6', 'OTU7',
... 'OTU8']
We also need a phylogenetic tree that relates the OTUs to one another.
>>> from io import StringIO
>>> from skbio import TreeNode
>>> tree = TreeNode.read(StringIO(
... '(((((OTU1:0.5,OTU2:0.5):0.5,OTU3:1.0):1.0):0.0,'
... '(OTU4:0.75,(OTU5:0.5,((OTU6:0.33,OTU7:0.62):0.5'
... ',OTU8:0.5):0.5):0.5):1.25):0.0)root;'))
We can then compute the Faith PD of the sample.
>>> from skbio.diversity.alpha import faith_pd
>>> pd = faith_pd(u_counts, otu_ids, tree)
>>> print(round(pd, 2))
6.95