Evaluate process heterogeneity using a Hidden Markov Model

Section author: Gavin Huttley

The existence of rate heterogeneity in the evolution of biological sequences is well known. Typically such an evolutionary property is evaluated using so-called site-heterogeneity models. These models postulate the existence of discrete classes of sites, where sites within a class evolve according to a specific rate that is distinct from the rates of the other classes. These models retain the assumption that alignment columns evolve independently. One can naturally ask the question of whether rate classes occur randomly throughout the sequence or whether they are in fact auto-correlated - meaning sites of a class tend to cluster together. Because we do not have, a priori, a basis for classifying the sites the models are specified such that each column can belong to any of the designated site classes and the likelihood is computed across all possible classifications. Post numerical optimisation we can calculate the posterior probability a site column belongs to a specific site class. In cogent3, site classes are referred to as bins and so we refer to bin probabilities etc…

To illustrate how to evaluate these hypotheses formally we specify 3 nested hypotheses: (i) Ho: no rate heterogeneity; (ii) Ha(1): two classes of sites - fast and slow, but independent sites; (iii) Ha(2): fast and slowly evolving sites are auto-correlated (meaning a sites class is correlated with that of its’ immediate neighbours).

It is also possible to apply these models to different types of changes and we illustrate this with a single parameterisation at the end.

First import standard components necessary for all of the following calculations. As the likelihood ratio tests (LRT) involve nested hypotheses we will employ the chi-square approximation for assessing statistical significance.

from cogent3 import load_aligned_seqs, load_tree
from cogent3.evolve.substitution_model import (
    TimeReversibleNucleotide,
    predicate,
)
from scipy.stats.distributions import chi2

Load the alignment and tree.

aln = load_aligned_seqs("data/long_testseqs.fasta")
tree = load_tree("data/test.tree")

Model Ho: no rate heterogeneity

We define a HKY model of nucleotide substitution, which has a transition parameter. This is defined using the MotifChange class, by specifying a transition as not a transversion (~MotifChange('R','Y')).

MotifChange = predicate.MotifChange
treat_gap = dict(recode_gaps=True, model_gaps=False)
kappa = (~MotifChange("R", "Y")).aliased("kappa")
model = TimeReversibleNucleotide(predicates=[kappa], **treat_gap)

We specify a null model with no bins, and optimise it.

lf_one = model.make_likelihood_function(tree, digits=2, space=3)
lf_one.set_alignment(aln)
lf_one.optimise(show_progress=False)
lnL_one = lf_one.get_log_likelihood()
df_one = lf_one.get_num_free_params()
lf_one

Likelihood function statistics

log-likelihood = -8750.5889

number of free parameters = 8

Global params

kappa
4.10

Edge params

edge	parent	length
Human	edge.0	0.03
HowlerMon	edge.0	0.04
edge.0	edge.1	0.04
Mouse	edge.1	0.28
edge.1	root	0.02
NineBande	root	0.09
DogFaced	root	0.11

Motif params

A	C	G	T
0.37	0.19	0.21	0.23

Model Ha(1): two classes of gamma distributed but independent sites

Our next hypothesis is that there are two rate classes, or bins, with rates gamma distributed. We will restrict the bin probabilities to be equal.

bin_submod = TimeReversibleNucleotide(
    predicates=[kappa], ordered_param="rate", distribution="gamma", **treat_gap
)
lf_bins = bin_submod.make_likelihood_function(
    tree, bins=2, sites_independent=True, digits=2, space=3
)
lf_bins.set_param_rule("bprobs", is_constant=True)
lf_bins.set_alignment(aln)
lf_bins.optimise(local=True, show_progress=False)
lnL_bins = lf_bins.get_log_likelihood()
df_bins = lf_bins.get_num_free_params()
assert df_bins == 9
lf_bins

Likelihood function statistics

log-likelihood = -8739.0900

number of free parameters = 9

Global params

kappa	rate_shape
4.38	1.26

Bin params

bin	bprobs	rate
bin0	0.50	0.41
bin1	0.50	1.59

Edge params

edge	parent	length
Human	edge.0	0.03
HowlerMon	edge.0	0.04
edge.0	edge.1	0.04
Mouse	edge.1	0.31
edge.1	root	0.02
NineBande	root	0.10
DogFaced	root	0.12

Motif params

A	C	G	T
0.37	0.19	0.21	0.23

Model Ha(2): fast and slowly evolving sites are auto-correlated

We then specify a model with switches for changing between site-classes, the HMM part. The setup is almost identical to that for above with the sole difference being setting the sites_independent=False.

lf_patches = bin_submod.make_likelihood_function(
    tree, bins=2, sites_independent=False, digits=2, space=3
)
lf_patches.set_param_rule("bprobs", is_constant=True)
lf_patches.set_alignment(aln)
lf_patches.optimise(local=True, show_progress=False)
lnL_patches = lf_patches.get_log_likelihood()
df_patches = lf_patches.get_num_free_params()
lf_patches

Likelihood function statistics

log-likelihood = -8728.1367

number of free parameters = 10

Global params

bin_switch	kappa	rate_shape
0.56	4.42	1.16

Bin params

bin	bprobs	rate
bin0	0.50	0.39
bin1	0.50	1.61

Edge params

edge	parent	length
Human	edge.0	0.03
HowlerMon	edge.0	0.04
edge.0	edge.1	0.04
Mouse	edge.1	0.31
edge.1	root	0.02
NineBande	root	0.10
DogFaced	root	0.12

Motif params

A	C	G	T
0.37	0.19	0.21	0.23

We use the following short function to compute the LR test statistic.

LR = lambda alt, null: 2 * (alt - null)

We conduct the test between the sequentially nested models.

lr = LR(lnL_bins, lnL_one)
lr

22.997883439584257

print("%.4f" % chi2.sf(lr, df_patches - df_bins))

0.0000

The stationary bin probabilities are labelled as bprobs and can be obtained as follows.

bprobs = lf_patches.get_param_value("bprobs")
print("%.1f : %.1f" % tuple(bprobs))

0.5 : 0.5

Of greater interest here (given the model was set up so the bin probabilities were equal, i.e. is_constant=True) are the posterior probabilities as those allow classification of sites. The result is a DictArray class instance, which behaves like a dictionary.

pp = lf_patches.get_bin_probs()

If we want to know the posterior probability the 21st position belongs to bin0, we can determine it as:

pp["bin0"][20]

np.float64(0.8034881117778552)

A model with patches of kappa

In this example we model sequence evolution where there are 2 classes of sites distinguished by their kappa parameters. We need to know what value of kappa to specify the delineation of the bin boundaries. We can determine this from the null model (lf_one). For this use case, we also need to use a numpy.array, so we’ll import that.

from numpy import array

single_kappa = lf_one.get_param_value("kappa")

We then construct the substitution model in a different way to that when evaluating generic rate heterogeneity (above).

kappa_bin_submod = TimeReversibleNucleotide(predicates=[kappa], **treat_gap)
lf_kappa = kappa_bin_submod.make_likelihood_function(
    tree, bins=["slow", "fast"], sites_independent=False, digits=1, space=3
)

To improve the likelihood fitting it is desirable to set starting values in the model that result in it’s initial likelihood being that of the null model (or as close as possible). To do this, we’re going to define an arbitrarily small value (epsilon) which we use to provide the starting value to the two bins as slightly smaller/greater than single_kappa for the slow/fast bins respectively. At the same time we set the upper/lower bin boundaries.

epsilon = 1e-6
lf_kappa.set_param_rule(
    kappa, init=single_kappa - epsilon, upper=single_kappa, bin="slow"
)
lf_kappa.set_param_rule(
    kappa, init=single_kappa + epsilon, lower=single_kappa, bin="fast"
)

We then illustrate how to adjust the bin probabilities, here doing it so that one of them is nearly 1, the other nearly 0. This ensures the likelihood will be near identical to that of lf_one and as a result the optimisation step will actually improve fit over the simpler model.

lf_kappa.set_param_rule("bprobs", init=array([1.0 - epsilon, 0.0 + epsilon]))
lf_kappa.set_alignment(aln)
lf_kappa.optimise(local=True, show_progress=False)
lf_kappa

Likelihood function statistics

log-likelihood = -8749.8958

number of free parameters = 11

Global params

bin_switch
1.0

Bin params

bin	bprobs	kappa
slow	1.0	4.1
fast	0.0	4.1

Edge params

edge	parent	length
Human	edge.0	0.0
HowlerMon	edge.0	0.0
edge.0	edge.1	0.0
Mouse	edge.1	0.3
edge.1	root	0.0
NineBande	root	0.1
DogFaced	root	0.1

Motif params

A	C	G	T
0.4	0.2	0.2	0.2