Belly Button Biodiversity: Part One

February 5, 2013
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(This article was originally published at Probably Overthinking It, and syndicated at StatsBlogs.)

This post is a excerpt from Think Bayes: Bayesian Statistics Made Simple, the book I am working on now.  You can read the entire current draft at http://thinkbayes.com.


Belly button bacteria

Belly Button Biodiversity 2.0 (BBB2) is a nation-wide citizen science project with the goal of identifying bacterial species that can be found in human navels (http://bbdata.yourwildlife.org).

The project might seem whimsical, but it is part of an increasing interest in the human microbiome, the set of microorganisms that live on human skin and other surfaces that contact the environment.

In their pilot study, BBB2 researchers collected swabs from the navels of 60 volunteers, used multiplex pyrosequencing to extract and sequence fragments of 16S rDNA, then identified the species or genus the fragments came from. Each identified fragment is called a “read.”

We can use these data to answer several related questions:
  • Based on the number of species observed, can we estimate the total number of species in the environment?
  • Can we estimate the prevalence of each species; that is, the fraction of the total population belonging to each species?
  • If we are planning to collect additional samples, can we predict how many new species we are likely to discover?
  • How many additional reads are needed to increase the fraction of observed species to a given threshold?
These questions make up what is called the “unseen species problem.”


Lions and tigers and bears

I’ll start with a simplified version of the problem where we know that there are exactly three species. Let’s call them lions, tigers and bears. Suppose we visit a wild animal preserve and see 3 lions, 2 tigers and one bear.

If we have an equal chance of observing any animal in the preserve then the number of each species we see is governed by the multinomial distribution. If the prevalence of lions and tigers and bears is p_lion and p_tiger and p_bear, the likelihood of seeing 3 lions, 2 tigers and one bear is
p_lion**3 * p_tiger**2 * p_bear**1
An approach that is tempting, but not correct, is to use beta distributions, as in Section 4.6, to describe the prevalence of each species separately. For example, we saw 3 lions and 3 non-lions; if we think of that as 3 “heads” and 3 “tails,” then the posterior distribution of p_lion is:
    beta = thinkbayes.Beta()
beta.Update((3, 3))
print beta.MaximumLikelihood()

The maximum likelihood estimate for p_lion is the observed rate, 50%. Similarly the MLEs for p_tiger and p_bearare 33% and 17%.
But there are two problems:
  • We have implicitly used a prior for each species that is uniform from 0 to 1, but since we know that there are three species, that prior is not correct. The right prior should have a mean of 1/3, and there should be zero likelihood that any species has a prevalence of 100%.
  • The distributions for each species are not independent, because the prevalences have to add up to 1. To capture this dependence, we need a joint distribution for the three prevalences.
We can use a Dirichlet distribution to solve both of these problems (see http://en.wikipedia.org/wiki/Dirichlet_distribution). In the same way we used the beta distribution to describe the distribution of bias for a coin, we can use a Dirichlet distribution to describe the joint distribution of p_lion, p_tiger and p_bear.

The Dirichlet distribution is the multi-dimensional generalization of the beta distribution. Instead of two possible outcomes, like heads and tails, the Dirichlet distribution handles any number of outcomes: in this example, three species.

If there are n outcomes, the Dirichlet distribution is described by n parameters, written αi.
Here’s the definition, from thinkbayes.py, of a class that represents a Dirichlet distribution:

class Dirichlet(object):

def __init__(self, n):
self.n = n
self.params = numpy.ones(n, dtype=numpy.int)

n is the number of dimensions; initially the parameters are all 1. I use a numpy array to store the parameters so I can take advantage of array operations.
Given a Dirichlet distribution, the marginal distribution for each prevalence is a beta distribution, which we can compute like this:
    def MarginalBeta(self, i):
alpha0 = self.params.sum()
alpha = self.params[i]
return Beta(alpha, alpha0-alpha)

i is the index of the marginal distribution we want. alpha0 is the sum of the parameters; alpha is the parameter for the given species.
In the example, the prior marginal distribution for each species is Beta(1, 2). We can compute the prior means like this:
    dirichlet = thinkbayes.Dirichlet(3)
for i in range(3):
beta = dirichlet.MarginalBeta(i)
print beta.Mean()


As expected, the prior mean prevalence for each species is 1/3.

To update the Dirichlet distribution, we add the number of observations to each parameter, like this:
    def Update(self, data):
m = len(data)
self.params[:m] += data

Here data is a sequence of counts in the same order as params, so in this example, it should be the number of lions, tigers and bears.

But data can be shorter than params; in that case there are some hypothetical species that have not been observed.

Here’s code that updates dirichlet with the observed data and computes the posterior marginal distributions.
    data = [3, 2, 1]
dirichlet.Update(data)

for i in range(3):
beta = dirichlet.MarginalBeta(i)
pmf = beta.MakePmf()
print i, pmf.Mean()

This figure shows the results. The posterior mean prevalences are 44%, 33% and 22%.



A hierarchical model

We have solved a simplified version of the problem: if we know how many species there are, we can estimate the prevalence of each.

Now let’s get back to the original problem, estimating the total number of species. To solve this problem I’ll define a metasuite, which is a Suite that contains other Suites as hypotheses. In this case, the top-level Suite contains hypotheses about the number of species; the bottom level contains hypotheses about prevalences. A multi-level model like this is called “hierarchical.”
Here’s the class definition:
class Species(thinkbayes.Suite):

def __init__(self, ns):
hypos = [thinkbayes.Dirichlet(n) for n in ns]
thinkbayes.Suite.__init__(self, hypos)


__init__ takes a list of possible values for n and makes a list of Dirichlet objects.

Here’s the code that creates the top-level suite:
    ns = range(3, 30)
suite = Species(ns)

ns is the list of possible values for n. We have seen 3 species, so there have to be at least that many. I chose an upper bound that seemed reasonable, but we will have to check later that the probability of exceeding this bound is low. And at least initially we assume that any value in this range is equally likely.

To update a hierarchical model, you have to update all levels. Sometimes it is necessary or more efficient to update the bottom level first and work up. In this case it doesn’t matter, so I update the top level first:
#class Species

def Update(self, data):
thinkbayes.Suite.Update(self, data)
for hypo in self.Values():
hypo.Update(data)

Species.Update invokes Update in the parent class, then loops through the sub-hypotheses and updates them.

Now all we need is a likelihood function. As usual, Likelihood gets a hypothesis and a dataset as arguments:
# class Species

def Likelihood(self, hypo, data):
dirichlet = hypo
like = 0
for i in range(1000):
like += dirichlet.Likelihood(data)

return like


hypo is a Dirichlet object; data is a sequence of observed counts. Species.Likelihood calls Dirichlet.Likelihood 1000 times and returns the total.

Why do we have to call it 1000 times? Because Dirichlet.Likelihood doesn’t actually compute the likelihood of the data under the whole Dirichlet distribution. Instead, it draws one sample from the hypothetical distribution and computes the likelihood of the data under the sampled set of prevalences.
Here’s what it looks like:
# class Dirichlet

def Likelihood(self, data):
m = len(data)
if self.n < m:
return 0

x = data
p = self.Random()
q = p[:m]**x
return q.prod()

The length of data is the number of species observed. If we see more species than we thought existed, the likelihood is 0.

Otherwise we select a random set of prevalences, p, and compute the multinomial PDF, which is
c(x
i
 pixi
pi is the prevalence of the ith species, and xi is the observed number. The first term, c(x), is the multinomial coefficient; I left it out of the computation because it is a multiplicative factor that depends only on the data, not the hypothesis, so it gets normalized away (see http://en.wikipedia.org/wiki/Multinomial_distribution).

Also, I truncated p at m, which is the number of observed species. For the unseen species, xi is 0, so pixi is 1, so we can leave them out of the product.


Random sampling

There are two ways to generate a random sample from a Dirichlet distribution. One is to use the marginal beta distributions, but in that case you have to select one at a time and scale the rest so they add up to 1 (see http://en.wikipedia.org/wiki/Dirichlet_distribution#Random_number_generation).

A less obvious, but faster, way is to select values from n gamma distributions, then normalize by dividing through by the total. Here’s the code:
# class Dirichlet

def Random(self):
p = numpy.random.gamma(self.params)
return p / p.sum()


Now we’re ready to look at some results. Here is the code that updates the top-level suite and extracts the posterior PMF of n:
    data = [3, 2, 1]
suite.Update(data)
pmf = suite.DistOfN()


To get the posterior distribution of n, DistOfN iterates through the top-level hypotheses:
    def DistOfN(self):
pmf = thinkbayes.Pmf()
for hypo, prob in self.Items():
pmf.Set(hypo.n, prob)
return pmf

This figure shows the result:


The most likely value is 5. Values from 3 to 8 are all likely; after that the probabilities drop off quickly. The probability that there are 29 species is low enough to be negligible; if we chose a higher bound, we would get the same result.

But remember that we started with a uniform prior for n. If we have background information about the number of species in the environment, we might choose a different prior.


Optimization

I have to admit that I am proud of this example. The unseen species problem is not easy, and I think this solution is simple and clear, and takes surprisingly few lines of code (about 50 so far).

The only problem is that it is slow. It’s good enough for the example with only 3 observed species, but not good enough for the belly button data, with more than 100 species in some samples.

In Think Bayes I present a series of optimizations we need to make this solution scale. Here’s a road map of the steps:
  • The first step is to recognize that if we update the Dirichlet distributions with the same data, the first m parameters are the same for all of them. The only difference is the number of hypothetical unseen species. So we don’t really need nDirichlet objects; we can store the parameters in the top level of the hierarchy. Species2 implements this optimization.
  • Species2 also uses the same set of random values for all of the hypotheses. This saves time generating random values, but it has a second benefit that turns out to be more important: by giving all hypothesis the same selection from the sample space, we make the comparison between the hypotheses more fair, so it takes fewer iterations to converge.
  • But there is still a major performance problem. As the number of observed species increases, the array of random prevalences gets bigger, and the chance of choosing one that is approximately right becomes small. So the vast majority of iterations yield small likelihoods that don’t contribute much to the total, and don’t discriminate between hypotheses.The solution is to do the updates one species at a time. Species4 is a simple implementation of this strategy using Dirichlet objects to represent the sub-hypotheses.
  • Finally, Species5 combines the sub-hypothesis into the top level and uses numpy array operations to speed things up.
I won't present the details here.  In the next part of this series, I will present results from the Belly Button Biodiversity project.



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