1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437
|
#!/usr/bin/env python
import warnings
warnings.filterwarnings("ignore", "Motif probs overspecified")
warnings.filterwarnings("ignore", "Model not reversible")
from numpy import ones, dot, array
from cogent import LoadSeqs, DNA, LoadTree, LoadTable
from cogent.evolve.substitution_model import Nucleotide, General, \
GeneralStationary
from cogent.evolve.discrete_markov import DiscreteSubstitutionModel
from cogent.evolve.predicate import MotifChange
from cogent.util.unit_test import TestCase, main
from cogent.maths.matrix_exponentiation import PadeExponentiator as expm
__author__ = "Peter Maxwell and Gavin Huttley"
__copyright__ = "Copyright 2007-2009, The Cogent Project"
__credits__ = ["Gavin Huttley"]
__license__ = "GPL"
__version__ = "1.4.1"
__maintainer__ = "Gavin Huttley"
__email__ = "gavin.huttley@anu.edu.au"
__status__ = "Production"
def _dinuc_root_probs(x,y=None):
if y is None:
y = x
return dict([(n1+n2, p1*p2)
for n1,p1 in x.items() for n2,p2 in y.items()])
def _trinuc_root_probs(x,y,z):
return dict([(n1+n2+n3, p1*p2*p3)
for n1,p1 in x.items() for n2,p2 in y.items()
for n3,p3 in z.items()])
def make_p(length, coord, val):
"""returns a probability matrix with value set at coordinate in
instantaneous rate matrix"""
Q = ones((4,4), float)*0.25 # assumes equi-frequent mprobs at root
for i in range(4):
Q[i,i] = 0.0
Q[coord] *= val
row_sum = Q.sum(axis=1)
scale = 1/(.25*row_sum).sum()
for i in range(4):
Q[i,i] -= row_sum[i]
Q *= scale
return expm(Q)(length)
class NewQ(TestCase):
aln = LoadSeqs(data={
'seq1': 'TGTGGCACAAATACTCATGCCAGCTCATTACAGCATGAGAACAGCAGTTTATTACTCACT',
'seq2': 'TGTGGCACAAATACTCATGCCAGCTCATTACAGCATGAGAACAGCAGTTTATTACTCACT'},
moltype=DNA)
tree = LoadTree(tip_names=['seq1', 'seq2'])
symm_nuc_probs = dict(A=0.25,T=0.25,C=0.25,G=0.25)
symm_root_probs = _dinuc_root_probs(symm_nuc_probs)
asymm_nuc_probs = dict(A=0.1,T=0.1,C=0.4,G=0.4)
asymm_root_probs = _dinuc_root_probs(asymm_nuc_probs)
posn_root_probs = _dinuc_root_probs(symm_nuc_probs, asymm_nuc_probs)
cond_root_probs = dict([(n1+n2, p1*[.1, .7][n1==n2])
for n1,p1 in asymm_nuc_probs.items() for n2 in 'ATCG'])
# Each of these (data, model) pairs should give a result different
# from any of the simpler models applied to the same data.
ordered_by_complexity = [
# P(AA) == P(GG) == P(AG)
[symm_root_probs, 'tuple'],
# P(GA) == P(AG) but P(AA) != P(GG)
[asymm_root_probs, 'monomer'],
# P(AG) == P(A?)*P(?G) but P(A?) != P(?A)
[posn_root_probs, 'monomers'],
# P(AG) != P(A?)*P(?G)
[cond_root_probs, 'conditional'],
]
def test_newQ_is_nuc_process(self):
"""newQ is an extension of an independent nucleotide process"""
nuc = Nucleotide(motif_probs = self.asymm_nuc_probs)
new_di = Nucleotide(motif_length=2, mprob_model='monomer',
motif_probs = self.asymm_root_probs)
nuc_lf = nuc.makeLikelihoodFunction(self.tree)
new_di_lf = new_di.makeLikelihoodFunction(self.tree)
# newQ branch length is exactly motif_length*nuc branch length
nuc_lf.setParamRule('length', is_independent=False, init=0.2)
new_di_lf.setParamRule('length', is_independent=False, init=0.4)
nuc_lf.setAlignment(self.aln)
new_di_lf.setAlignment(self.aln)
self.assertFloatEqual(nuc_lf.getLogLikelihood(),
new_di_lf.getLogLikelihood())
def test_lf_display(self):
"""str of likelihood functions should not fail"""
for (dummy, model) in self.ordered_by_complexity:
di = Nucleotide(motif_length=2, mprob_model=model)
di.adaptMotifProbs(self.cond_root_probs, auto=True)
lf = di.makeLikelihoodFunction(self.tree)
s = str(lf)
def test_get_statistics(self):
"""get statistics should correctly apply arguments"""
for (mprobs, model) in self.ordered_by_complexity:
di = Nucleotide(motif_length=2, motif_probs=mprobs,
mprob_model=model)
lf = di.makeLikelihoodFunction(self.tree)
for wm, wt in [(True, True), (True, False), (False, True),
(False, False)]:
stats = lf.getStatistics(with_motif_probs=wm, with_titles=wt)
def test_sim_alignment(self):
"""should be able to simulate an alignment under all models"""
for (mprobs, model) in self.ordered_by_complexity:
di = Nucleotide(motif_length=2, motif_probs=mprobs,
mprob_model=model)
lf = di.makeLikelihoodFunction(self.tree)
lf.setParamRule('length', is_independent=False, init=0.4)
lf.setAlignment(self.aln)
sim = lf.simulateAlignment()
def test_reconstruct_ancestor(self):
"""should be able to reconstruct ancestral sequences under all
models"""
for (mprobs, model) in self.ordered_by_complexity:
di = Nucleotide(motif_length=2, mprob_model=model)
di.adaptMotifProbs(mprobs, auto=True)
lf = di.makeLikelihoodFunction(self.tree)
lf.setParamRule('length', is_independent=False, init=0.4)
lf.setAlignment(self.aln)
ancestor = lf.reconstructAncestralSeqs()
def test_results_different(self):
for (i, (mprobs, dummy)) in enumerate(self.ordered_by_complexity):
results = []
for (dummy, model) in self.ordered_by_complexity:
di = Nucleotide(motif_length=2, motif_probs=mprobs,
mprob_model=model)
lf = di.makeLikelihoodFunction(self.tree)
lf.setParamRule('length', is_independent=False, init=0.4)
lf.setAlignment(self.aln)
lh = lf.getLogLikelihood()
for other in results[:i]:
self.failIfAlmostEqual(other, lh, places=2)
for other in results[i:]:
self.assertFloatEqual(other, lh)
results.append(lh)
def test_position_specific_mprobs(self):
"""correctly compute likelihood when positions have distinct
probabilities"""
aln_len = len(self.aln)
posn1 = []
posn2 = []
for name, seq in self.aln.todict().items():
p1 = [seq[i] for i in range(0,aln_len,2)]
p2 = [seq[i] for i in range(1,aln_len,2)]
posn1.append([name, ''.join(p1)])
posn2.append([name, ''.join(p2)])
# the position specific alignments
posn1 = LoadSeqs(data=posn1)
posn2 = LoadSeqs(data=posn2)
# a newQ dinucleotide model
sm = Nucleotide(motif_length=2, mprob_model='monomer', do_scaling=False)
lf = sm.makeLikelihoodFunction(self.tree)
lf.setAlignment(posn1)
posn1_lnL = lf.getLogLikelihood()
lf.setAlignment(posn2)
posn2_lnL = lf.getLogLikelihood()
expect_lnL = posn1_lnL+posn2_lnL
# the joint model
lf.setAlignment(self.aln)
aln_lnL = lf.getLogLikelihood()
# setting the full alignment, which has different motif probs, should
# produce a different lnL
self.failIfAlmostEqual(expect_lnL, aln_lnL)
# set the arguments for taking position specific mprobs
sm = Nucleotide(motif_length=2, mprob_model='monomers',
do_scaling=False)
lf = sm.makeLikelihoodFunction(self.tree)
lf.setAlignment(self.aln)
posn12_lnL = lf.getLogLikelihood()
self.assertFloatEqual(expect_lnL, posn12_lnL)
def test_compute_conditional_mprobs(self):
"""equal likelihood from position specific and conditional mprobs"""
def compare_models(motif_probs, motif_length):
# if the 1st and 2nd position motifs are independent of each other
# then conditional is the same as positional
ps = Nucleotide(motif_length=motif_length, motif_probs=motif_probs,
mprob_model='monomers')
cd = Nucleotide(motif_length=motif_length,motif_probs=motif_probs,
mprob_model='conditional')
ps_lf = ps.makeLikelihoodFunction(self.tree)
ps_lf.setParamRule('length', is_independent=False, init=0.4)
ps_lf.setAlignment(self.aln)
cd_lf = cd.makeLikelihoodFunction(self.tree)
cd_lf.setParamRule('length', is_independent=False, init=0.4)
cd_lf.setAlignment(self.aln)
self.assertFloatEqual(cd_lf.getLogLikelihood(),
ps_lf.getLogLikelihood())
compare_models(self.posn_root_probs, 2)
# trinucleotide
trinuc_mprobs = _trinuc_root_probs(self.asymm_nuc_probs,
self.asymm_nuc_probs, self.asymm_nuc_probs)
compare_models(trinuc_mprobs, 3)
def test_cond_pos_differ(self):
"""lnL should differ when motif probs are not multiplicative"""
dinuc_probs = {'AA': 0.088506666666666664, 'AC': 0.044746666666666664,
'GT': 0.056693333333333332, 'AG': 0.070199999999999999,
'CC': 0.048653333333333333, 'TT': 0.10678666666666667,
'CG': 0.0093600000000000003, 'GG': 0.049853333333333333,
'GC': 0.040253333333333335, 'AT': 0.078880000000000006,
'GA': 0.058639999999999998, 'TG': 0.081626666666666667,
'TA': 0.068573333333333333, 'CA': 0.06661333333333333,
'TC': 0.060866666666666666, 'CT': 0.069746666666666665}
mg = Nucleotide(motif_length=2, motif_probs=dinuc_probs,
mprob_model='monomer')
mg_lf = mg.makeLikelihoodFunction(self.tree)
mg_lf.setParamRule('length', is_independent=False, init=0.4)
mg_lf.setAlignment(self.aln)
cd = Nucleotide(motif_length=2, motif_probs=dinuc_probs,
mprob_model='conditional')
cd_lf = cd.makeLikelihoodFunction(self.tree)
cd_lf.setParamRule('length', is_independent=False, init=0.4)
cd_lf.setAlignment(self.aln)
self.assertNotAlmostEqual(mg_lf.getLogLikelihood(),
cd_lf.getLogLikelihood())
def test_getting_node_mprobs(self):
"""return correct motif probability vector for tree nodes"""
tree = LoadTree(treestring='(a:.2,b:.2,(c:.1,d:.1):.1)')
aln = LoadSeqs(data={
'a': 'TGTG',
'b': 'TGTG',
'c': 'TGTG',
'd': 'TGTG',
})
motifs = ['T', 'C', 'A', 'G']
aX = MotifChange(motifs[0], motifs[3], forward_only=True).aliased('aX')
bX = MotifChange(motifs[3], motifs[0], forward_only=True).aliased('bX')
edX = MotifChange(motifs[1], motifs[2], forward_only=True).aliased('edX')
cX = MotifChange(motifs[2], motifs[1], forward_only=True).aliased('cX')
sm = Nucleotide(predicates=[aX, bX, edX, cX], equal_motif_probs=True)
lf = sm.makeLikelihoodFunction(tree)
lf.setParamRule('aX', edge='a', value=8.0)
lf.setParamRule('bX', edge='b', value=8.0)
lf.setParamRule('edX', edge='edge.0', value=2.0)
lf.setParamRule('cX', edge='c', value=0.5)
lf.setParamRule('edX', edge='d', value=4.0)
lf.setAlignment(aln)
# we construct the hand calc variants
mprobs = ones(4, float) * .25
a = make_p(.2, (0,3), 8)
a = dot(mprobs, a)
b = make_p(.2, (3, 0), 8)
b = dot(mprobs, b)
e = make_p(.1, (1, 2), 2)
e = dot(mprobs, e)
c = make_p(.1, (2, 1), 0.5)
c = dot(e, c)
d = make_p(.1, (1, 2), 4)
d = dot(e, d)
prob_vectors = lf.getMotifProbsByNode()
self.assertFloatEqual(prob_vectors['a'].array, a)
self.assertFloatEqual(prob_vectors['b'].array, b)
self.assertFloatEqual(prob_vectors['c'].array, c)
self.assertFloatEqual(prob_vectors['d'].array, d)
self.assertFloatEqual(prob_vectors['edge.0'].array, e)
def _make_likelihood(model, tree, results, is_discrete=False):
"""creates the likelihood function"""
# discrete model fails to make a likelihood function if tree has
# lengths
if is_discrete:
kwargs={}
else:
kwargs=dict(expm='pade')
lf = model.makeLikelihoodFunction(tree,
optimise_motif_probs=True, **kwargs)
if not is_discrete:
for param in lf.getParamNames():
if param in ('length', 'mprobs'):
continue
lf.setParamRule(param, is_independent=True, upper=5)
lf.setAlignment(results['aln'])
return lf
def MakeCachedObjects(model, tree, seq_length, opt_args):
"""simulates an alignment under F81, all models should be the same"""
lf = model.makeLikelihoodFunction(tree)
lf.setMotifProbs(dict(A=0.1,C=0.2,G=0.3,T=0.4))
aln = lf.simulateAlignment(seq_length)
results = dict(aln=aln)
discrete_tree = LoadTree(tip_names=aln.Names)
def fit_general(results=results):
if 'general' in results:
return
gen = General(DNA.Alphabet)
gen_lf = _make_likelihood(gen, tree, results)
gen_lf.optimise(**opt_args)
results['general'] = gen_lf
return
def fit_gen_stat(results=results):
if 'gen_stat' in results:
return
gen_stat = GeneralStationary(DNA.Alphabet)
gen_stat_lf = _make_likelihood(gen_stat, tree, results)
gen_stat_lf.optimise(**opt_args)
results['gen_stat'] = gen_stat_lf
def fit_constructed_gen(results=results):
if 'constructed_gen' in results:
return
preds = [MotifChange(a,b, forward_only=True) for a,b in [['A', 'C'],
['A', 'G'], ['A', 'T'], ['C', 'A'], ['C', 'G'],
['C', 'T'], ['G', 'C'], ['G', 'T'], ['T', 'A'],
['T', 'C'], ['T', 'G']]]
nuc = Nucleotide(predicates=preds)
nuc_lf = _make_likelihood(nuc, tree, results)
nuc_lf.optimise(**opt_args)
results['constructed_gen'] = nuc_lf
def fit_discrete(results=results):
if 'discrete' in results:
return
dis_lf = _make_likelihood(DiscreteSubstitutionModel(DNA.Alphabet),
discrete_tree, results, is_discrete=True)
dis_lf.optimise(**opt_args)
results['discrete'] = dis_lf
funcs = dict(general=fit_general, gen_stat=fit_gen_stat,
discrete=fit_discrete, constructed_gen=fit_constructed_gen)
def call(self, obj_name):
if obj_name not in results:
funcs[obj_name]()
return results[obj_name]
return call
# class DiscreteGeneral(TestCase):
# """test discrete and general markov"""
# tree = LoadTree(treestring='(a:0.4,b:0.4,c:0.6)')
# opt_args = dict(max_restarts=1, local=True, show_progress=False)
# make_cached = MakeCachedObjects(Nucleotide(), tree, 100000, opt_args)
#
# def _setup_discrete_from_general(self, gen_lf):
# dis_lf = self.make_cached('discrete')
# for edge in self.tree:
# init = gen_lf.getPsubForEdge(edge.Name)
# dis_lf.setParamRule('psubs', edge=edge.Name, init=init)
# dis_lf.setMotifProbs(gen_lf.getMotifProbs())
# return dis_lf
#
# def test_discrete_vs_general1(self):
# """compares fully general models"""
# gen_lf = self.make_cached('general')
# gen_lnL = gen_lf.getLogLikelihood()
# dis_lf = self._setup_discrete_from_general(gen_lf)
# self.assertFloatEqual(gen_lnL, dis_lf.getLogLikelihood())
#
# def test_general_vs_constructed_general(self):
# """a constructed general lnL should be identical to General"""
# sm_lf = self.make_cached('constructed_gen')
# sm_lnL = sm_lf.getLogLikelihood()
# gen_lf = self.make_cached('general')
# gen_lnL = gen_lf.getLogLikelihood()
# self.assertFloatEqualAbs(sm_lnL, gen_lnL, eps=0.1)
#
# def test_general_stationary(self):
# """General stationary should be close to General"""
# gen_stat_lf = self.make_cached('gen_stat')
# gen_lf = self.make_cached('general')
# gen_stat_lnL = gen_stat_lf.getLogLikelihood()
# gen_lnL = gen_lf.getLogLikelihood()
# self.assertLessThan(gen_stat_lnL, gen_lnL)
#
# def test_general_stationary_is_stationary(self):
# """should be stationary"""
# gen_stat_lf = self.make_cached('gen_stat')
# mprobs = gen_stat_lf.getMotifProbs()
# mprobs = array([mprobs[nuc] for nuc in DNA.Alphabet])
# for edge in self.tree:
# psub = gen_stat_lf.getPsubForEdge(edge.Name)
# pi = dot(mprobs, psub.array)
# self.assertFloatEqual(mprobs, pi)
#
# def test_general_is_not_stationary(self):
# """should not be stationary"""
# gen_lf = self.make_cached('general')
# mprobs = gen_lf.getMotifProbs()
# mprobs = array([mprobs[nuc] for nuc in DNA.Alphabet])
# for edge in self.tree:
# psub = gen_lf.getPsubForEdge(edge.Name)
# pi = dot(mprobs, psub.array)
# try:
# self.assertFloatEqual(mprobs, pi)
# except AssertionError:
# pass
if __name__ == "__main__":
main()
|