def test_number_of_pseudo_seqs(self):
"""number_of_pseudo_seqs: should return # of pseudo seqs"""
self.assertEqual(number_of_pseudo_seqs(self.aln1), 6)
self.assertEqual(number_of_pseudo_seqs(self.aln2), 18)
self.assertEqual(number_of_pseudo_seqs(self.aln3), 4)
self.assertEqual(number_of_pseudo_seqs(self.aln4), 9)
def VOR(alignment,n=1000,force_monte_carlo=False,mc_threshold=1000):
"""Returns sequence weights according to the Voronoi weighting method.
alignment: Alignment object
n: sampling size (in case monte carlo is used)
force_monte_carlo: generate pseudo seqs with monte carlo always (even
if there's only a small number of possible unique pseudo seqs
mc_threshold: threshold of when to use the monte carlo sampling method
if the number of possible pseudo seqs exceeds this threshold monte
carlo is used.
VOR differs from VA in the set of sequences against which it's comparing
all the sequences in the alignment. In addition to the sequences in the
alignment itself, it uses a set of pseudo sequences.
Generating discrete random sequences:
A discrete random sequence is generated by choosing with equal
likelihood at each position one of the residues observed at that position
in the alighment. An occurrence of once in the alignment column is
sufficient to make the residue type an option. Note: you're choosing
with equal likelihood from each of the observed residues (independent
of their frequency at that position). In earlier versions of the algorithm
the characters were chosen either at the frequency with which they occur
at a position or at the frequency with which they occur in the database.
Both trials were unsuccesful, because they deviate from random sampling
(see Sibbald & Argos 1990).
Depending on the number of possible pseudo sequences, all of them are
used or a random sample is taken (monte carlo).
Example:
Alignment: AA, AA, BB
AA AA BB
AA 0 (.5) 0 (.5) 2
AB 1 (1/3) 1 (1/3) 1 (1/3)
BA 1 (1/3) 1 (1/3) 1 (1/3)
BB 2 2 0 (1)
-----------------------------
total 7/6 7/6 10/6
norm .291 .291 .418
For a bigger example with more pseudo sequences, see Henikoff 1994
I tried the described optimization (pre-calculate the distance to the
closest sequence). I doesn't have an advantage over the original method.
"""
MC_THRESHOLD = mc_threshold
#decide on sampling method
if force_monte_carlo or number_of_pseudo_seqs(alignment) > MC_THRESHOLD:
sampling_method = pseudo_seqs_monte_carlo
else:
sampling_method = pseudo_seqs_exact
#change sequences into arrays
aln_array = DenseAlignment(alignment, MolType=BYTES)
weights = zeros(len(aln_array.Names),Float64)
#calc distances for each pseudo seq
rows = [array(seq, 'c') for seq in map(str, aln_array.Seqs)]
for seq in sampling_method(aln_array,n=n):
seq = array(seq, 'c')
temp = [hamming_distance(row, seq) for row in rows]
votes = row_to_vote(array(temp)) #change distances to votes
weights += votes #add to previous weights
weight_dict = Weights(dict(list(zip(aln_array.Names,weights))))
weight_dict.normalize() #normalize
return weight_dict
def test_number_of_pseudo_seqs(self):
"""number_of_pseudo_seqs: should return # of pseudo seqs"""
self.assertEqual(number_of_pseudo_seqs(self.aln1),6)
self.assertEqual(number_of_pseudo_seqs(self.aln2),18)
self.assertEqual(number_of_pseudo_seqs(self.aln3),4)
self.assertEqual(number_of_pseudo_seqs(self.aln4),9)
def VOR(alignment, n=1000, force_monte_carlo=False, mc_threshold=1000):
"""Returns sequence weights according to the Voronoi weighting method.
alignment: Alignment object
n: sampling size (in case monte carlo is used)
force_monte_carlo: generate pseudo seqs with monte carlo always (even
if there's only a small number of possible unique pseudo seqs
mc_threshold: threshold of when to use the monte carlo sampling method
if the number of possible pseudo seqs exceeds this threshold monte
carlo is used.
VOR differs from VA in the set of sequences against which it's comparing
all the sequences in the alignment. In addition to the sequences in the
alignment itself, it uses a set of pseudo sequences.
Generating discrete random sequences:
A discrete random sequence is generated by choosing with equal
likelihood at each position one of the residues observed at that position
in the alighment. An occurrence of once in the alignment column is
sufficient to make the residue type an option. Note: you're choosing
with equal likelihood from each of the observed residues (independent
of their frequency at that position). In earlier versions of the algorithm
the characters were chosen either at the frequency with which they occur
at a position or at the frequency with which they occur in the database.
Both trials were unsuccesful, because they deviate from random sampling
(see Sibbald & Argos 1990).
Depending on the number of possible pseudo sequences, all of them are
used or a random sample is taken (monte carlo).
Example:
Alignment: AA, AA, BB
AA AA BB
AA 0 (.5) 0 (.5) 2
AB 1 (1/3) 1 (1/3) 1 (1/3)
BA 1 (1/3) 1 (1/3) 1 (1/3)
BB 2 2 0 (1)
-----------------------------
total 7/6 7/6 10/6
norm .291 .291 .418
For a bigger example with more pseudo sequences, see Henikoff 1994
I tried the described optimization (pre-calculate the distance to the
closest sequence). I doesn't have an advantage over the original method.
"""
MC_THRESHOLD = mc_threshold
#decide on sampling method
if force_monte_carlo or number_of_pseudo_seqs(alignment) > MC_THRESHOLD:
sampling_method = pseudo_seqs_monte_carlo
else:
sampling_method = pseudo_seqs_exact
#change sequences into arrays
aln_array = DenseAlignment(alignment, MolType=BYTES)
weights = zeros(len(aln_array.Names), Float64)
#calc distances for each pseudo seq
rows = [array(seq, 'c') for seq in map(str, aln_array.Seqs)]
for seq in sampling_method(aln_array, n=n):
seq = array(seq, 'c')
temp = [hamming_distance(row, seq) for row in rows]
votes = row_to_vote(array(temp)) #change distances to votes
weights += votes #add to previous weights
weight_dict = Weights(dict(zip(aln_array.Names, weights)))
weight_dict.normalize() #normalize
return weight_dict