This package provides functionality for analyzing biological diversity. It
implements metrics of alpha and beta diversity, and provides two “driver
functions” that are intended to be the primary interface for computing alpha
and beta diversity with scikit-bio. Functions are additionally provided that
support discovery of the available diversity metrics. This document provides a
high-level discussion of how to work with the
skbio.diversity module, and
should be the first document you read before working with the module.
The driver functions,
skbio.diversity.beta_diversity, are designed to compute alpha diversity for
one or more samples, or beta diversity for one or more pairs of samples. The
diversity driver functions accept a matrix containing vectors of frequencies of
OTUs within each sample.
We use the term “OTU” here very loosely, as these can in practice represent diverse feature types including bacterial species, genes, and metabolites. The term “sample” is also loosely defined for these purposes. These are intended to represent a single unit of sampling, and as such what a single sample represents can vary widely. For example, in a microbiome survey, these could represent all 16S rRNA gene sequences from a single oral swab. In a comparative genomics study on the other hand, a sample could represent an individual organism’s genome.
Each frequency in a given vector represents the number of individuals observed
for a particular OTU. We will refer to the frequencies associated with a single
sample as a counts vector or
counts throughout the documentation. Counts
vectors are array_like: anything that can be converted into a 1-D numpy array
is acceptable input. For example, you can provide a numpy array or a native
Python list and the results will be identical. As mentioned above, the driver
functions accept one or more of these vectors (representing one or more
samples) in a matrix which is also array_like. Each row in the matrix
represents a single sample’s count vector, so that rows represent samples and
columns represent OTUs.
Some diversity metrics incorporate relationships between the OTUs in their
computation through reference to a phylogenetic tree. These metrics
additionally take a
skbio.TreeNode object and a list of OTU identifiers
mapping the values in the counts vector to tips in the tree.
The driver functions are optimized so that computing a diversity metric more
than one time (i.e., for more than one sample for alpha diversity metrics, or
more than one pair of samples for beta diversity metrics) is often much faster
than repeated calls to the metric. For this reason, the driver functions take
matrices of counts vectors rather than a single counts vector for alpha
diversity metrics or two counts vectors for beta diversity metrics. The
alpha_diversity driver function will thus compute alpha diversity for all
counts vectors in the matrix, and the
beta_diversity driver function will
compute beta diversity for all pairs of counts vectors in the matrix.
The driver functions perform validation of input by default. Validation can be
slow so it is possible to disable this step by passing
can be dangerous however. If invalid input is encountered when validation is
disabled it can result in difficult-to-interpret error messages or incorrect
results. We therefore recommend that users are careful to ensure that their
input data is valid before disabling validation.
The conditions that the driver functions validate follow. If disabling validation, users should be confident that these conditions are met.
Additionally, if a phylogenetic diversity metric is being computed, the following conditions are also confirmed:
There are different ways that count vectors are represented in the ecological literature and in related software. The diversity measures provided here always assume that the input contains abundance data: each count represents the number of individuals observed for a particular OTU in the sample. For example, if you have two OTUs, where three individuals were observed from the first OTU and only a single individual was observed from the second OTU, you could represent this data in the following forms (among others).
As a vector of counts. This is the expected type of input for the diversity measures in this module. There are 3 individuals from the OTU at index 0, and 1 individual from the OTU at index 1:
>>> counts = [3, 1]
As a vector of indices. The OTU at index 0 is observed 3 times, while the OTU at index 1 is observed 1 time:
>>> indices = [0, 0, 0, 1]
As a vector of frequencies. We have 1 OTU that is a singleton and 1 OTU that is a tripleton. We do not have any 0-tons or doubletons:
>>> frequencies = [0, 1, 0, 1]
Always use the first representation (a counts vector) with this module.
The driver functions take a parameter,
metric, that specifies which
diversity metric should be applied. The value that you provide for
can be either a string (e.g.,
"faith_pd") or a function (e.g.,
skbio.diversity.alpha.faith_pd). The metric should generally be passed as a
string, as this often uses an optimized version of the metric. For example,
metric="faith_pd" (a string) to
alpha_diversity will be tens
of times faster than passing
function) when computing Faith’s PD on about 100 samples. Similarly, passing
metric="unweighted_unifrac" (a string) will be hundreds of times
faster than passing
function) when computing unweighted UniFrac on about 100 samples. The latter
may be faster if computing only one alpha or beta diversity value, but in
these cases the run times will likely be so small that the difference will be
negligible. We therefore recommend that you always pass the metric as a
string when possible.
Passing a metric as a string will not be possible if the metric you’d like to
run is not one that scikit-bio knows about. This might be the case, for
example, if you’re applying a custom metric that you’ve developed. To discover
the metric names that scikit-bio knows about as strings that can be passed as
beta_diversity, you can call
respectively. These functions return lists of alpha and beta diversity metrics
that are implemented in scikit-bio. There may be additional metrics that can be
passed as strings which won’t be listed here, such as those implemented in
||Alpha diversity measures (
||Beta diversity measures (
||Compute alpha diversity for one or more samples|
||Compute distances between all pairs of samples|
||List scikit-bio’s alpha diversity metrics|
||List scikit-bio’s beta diversity metrics|
Create a matrix containing 6 samples (rows) and 7 OTUs (columns):
>>> data = [[23, 64, 14, 0, 0, 3, 1], ... [0, 3, 35, 42, 0, 12, 1], ... [0, 5, 5, 0, 40, 40, 0], ... [44, 35, 9, 0, 1, 0, 0], ... [0, 2, 8, 0, 35, 45, 1], ... [0, 0, 25, 35, 0, 19, 0]] >>> ids = list('ABCDEF')
First, we’ll compute observed OTUs, an alpha diversity metric, for each
sample using the
alpha_diversity driver function:
>>> from skbio.diversity import alpha_diversity >>> adiv_obs_otus = alpha_diversity('observed_otus', data, ids) >>> adiv_obs_otus A 5 B 5 C 4 D 4 E 5 F 3 dtype: int64
Next we’ll compute Faith’s PD on the same samples. Since this is a phylogenetic diversity metric, we’ll first create a tree and an ordered list of OTU identifiers.
>>> from skbio import TreeNode >>> from io import StringIO >>> 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,(OTU6:0.5,OTU7:0.5):0.5):' ... '0.5):1.25):0.0)root;')) >>> otu_ids = ['OTU1', 'OTU2', 'OTU3', 'OTU4', 'OTU5', 'OTU6', 'OTU7'] >>> adiv_faith_pd = alpha_diversity('faith_pd', data, ids=ids, ... otu_ids=otu_ids, tree=tree) >>> adiv_faith_pd A 6.75 B 7.00 C 6.25 D 5.75 E 6.75 F 5.50 dtype: float64
Now we’ll compute Bray-Curtis distances, a beta diversity metric, between
all pairs of samples. Notice that the
beta_diversity are the same as those provided to
>>> from skbio.diversity import beta_diversity >>> bc_dm = beta_diversity("braycurtis", data, ids) >>> print(bc_dm) 6x6 distance matrix IDs: 'A', 'B', 'C', 'D', 'E', 'F' Data: [[ 0. 0.78787879 0.86666667 0.30927835 0.85714286 0.81521739] [ 0.78787879 0. 0.78142077 0.86813187 0.75 0.1627907 ] [ 0.86666667 0.78142077 0. 0.87709497 0.09392265 0.71597633] [ 0.30927835 0.86813187 0.87709497 0. 0.87777778 0.89285714] [ 0.85714286 0.75 0.09392265 0.87777778 0. 0.68235294] [ 0.81521739 0.1627907 0.71597633 0.89285714 0.68235294 0. ]]
Next, we’ll compute weighted UniFrac distances between all pairs of samples.
Because weighted UniFrac is a phylogenetic beta diversity metric, we’ll need
to pass the
skbio.TreeNode and list of OTU ids that we created above.
Again, these are the same values that were provided to
>>> wu_dm = beta_diversity("weighted_unifrac", data, ids, tree=tree, ... otu_ids=otu_ids) >>> print(wu_dm) 6x6 distance matrix IDs: 'A', 'B', 'C', 'D', 'E', 'F' Data: [[ 0. 2.77549923 3.82857143 0.42512039 3.8547619 3.10937312] [ 2.77549923 0. 2.26433692 2.98435423 2.24270353 0.46774194] [ 3.82857143 2.26433692 0. 3.95224719 0.16025641 1.86111111] [ 0.42512039 2.98435423 3.95224719 0. 3.98796148 3.30870431] [ 3.8547619 2.24270353 0.16025641 3.98796148 0. 1.82967033] [ 3.10937312 0.46774194 1.86111111 3.30870431 1.82967033 0. ]]
Next we’ll do some work with these beta diversity distance matrices. First, we’ll determine if the UniFrac and Bray-Curtis distance matrices are significantly correlated by computing the Mantel correlation between them. Then we’ll determine if the p-value is significant based on an alpha of 0.05.
>>> from skbio.stats.distance import mantel >>> r, p_value, n = mantel(wu_dm, bc_dm) >>> print(r) 0.922404392093 >>> alpha = 0.05 >>> print(p_value < alpha) True
Next, we’ll perform principal coordinates analysis (PCoA) on our weighted UniFrac distance matrix.
>>> from skbio.stats.ordination import pcoa >>> wu_pc = pcoa(wu_dm)
PCoA plots are only really interesting in the context of sample metadata, so let’s define some before we visualize these results.
>>> import pandas as pd >>> sample_md = [ ... ('A', ['gut', 's1']), ... ('B', ['skin', 's1']), ... ('C', ['tongue', 's1']), ... ('D', ['gut', 's2']), ... ('E', ['tongue', 's2']), ... ('F', ['skin', 's2'])] >>> sample_md = pd.DataFrame.from_items( ... sample_md, columns=['body_site', 'subject'], orient='index') >>> sample_md body_site subject A gut s1 B skin s1 C tongue s1 D gut s2 E tongue s2 F skin s2
Now let’s plot our PCoA results, coloring each sample by the subject it was taken from:
>>> fig = wu_pc.plot(sample_md, 'subject', ... axis_labels=('PC 1', 'PC 2', 'PC 3'), ... title='Samples colored by subject', cmap='jet', s=50)
We don’t see any clustering/grouping of samples. If we were to instead color the samples by the body site they were taken from, we see that the samples from the same body site (those that are colored the same) appear to be closer to one another in the 3-D space then they are to samples from other body sites.
>>> import matplotlib.pyplot as plt >>> plt.close('all') # not necessary for normal use >>> fig = wu_pc.plot(sample_md, 'body_site', ... axis_labels=('PC 1', 'PC 2', 'PC 3'), ... title='Samples colored by body site', cmap='jet', s=50)
Ordination techniques, such as PCoA, are useful for exploratory analysis. The next step is to quantify the strength of the grouping/clustering that we see in ordination plots. There are many statistical methods available to accomplish this; many operate on distance matrices. Let’s use ANOSIM to quantify the strength of the clustering we see in the ordination plots above, using our weighted UniFrac distance matrix and sample metadata.
First test the grouping of samples by subject:
>>> from skbio.stats.distance import anosim >>> results = anosim(wu_dm, sample_md, column='subject', permutations=999) >>> results['test statistic'] -0.33333333333333331 >>> results['p-value'] < 0.1 False
The negative value of ANOSIM’s R statistic indicates anti-clustering and the p-value is insignificant at an alpha of 0.1.
Now let’s test the grouping of samples by body site:
>>> results = anosim(wu_dm, sample_md, column='body_site', permutations=999) >>> results['test statistic'] 1.0 >>> results['p-value'] < 0.1 True
The R statistic indicates strong separation of samples based on body site. The p-value is significant at an alpha of 0.1.
We can also explore the alpha diversity in the context of sample metadata.
To do this, let’s add the Observed OTU and Faith PD data to our sample
metadata. This is straight-forward beause
alpha_diversity returns a
Series object, and we’re representing our sample metadata in a
>>> sample_md['Observed OTUs'] = adiv_obs_otus >>> sample_md['Faith PD'] = adiv_faith_pd >>> sample_md body_site subject Observed OTUs Faith PD A gut s1 5 6.75 B skin s1 5 7.00 C tongue s1 4 6.25 D gut s2 4 5.75 E tongue s2 5 6.75 F skin s2 3 5.50
We can investigate these alpha diversity data in the context of our metadata categories. For example, we can generate boxplots showing Faith PD by body site.
>>> import matplotlib.pyplot as plt >>> plt.close('all') # not necessary for normal use >>> fig = sample_md.boxplot(column='Faith PD', by='body_site')
We can also compute Spearman correlations between all pairs of columns in this
DataFrame. Since our alpha diversity metrics are the only two numeric
columns (and thus the only columns for which Spearman correlation is relevant),
this will give us a symmetric 2x2 correlation matrix.
>>> sample_md.corr(method="spearman") Observed OTUs Faith PD Observed OTUs 1.000000 0.939336 Faith PD 0.939336 1.000000