forked from aimly/transcriptome-assemblies-refiner
/
validation_checker.py
281 lines (243 loc) · 12.6 KB
/
validation_checker.py
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# -*- coding: utf-8 -*-
import csv, pdb
import pprint
if True:
def check_validation_results(assembler):
missing = 0
existing = 0
good = 0
good_list = []
kernels = [{'svc': 'rbf'}, {'svc': 'poly'}, {'svc': 'sigmoid'}, {'rfc': 2}, {'rfc': 4}, {'rfc': 10}, {'rfc': 100}, {'rfc': 200}, {'rfc': 1000}]
for bottom_bound in [0.3, 0.4, 0.5, 0.6]:
for top_bound in [0.99, 0.95, 0.9]:
for ngram in [4, 5, 6, 7, 8]:
for kernel in kernels:
matched_must_fit = False
matched_must_not_fit = False
for fit in ['must-fit', 'must-not-fit']:
filename = 'validation/' + assembler + "_classified-" \
+ str(ngram) + "-" + kernel.keys()[0] + '-' + str(kernel[kernel.keys()[0]]) + '--' \
+ str(bottom_bound) + '-' + str(top_bound) + '--validating-' + fit + "-SUMMARY"
try:
f = open(filename, 'r')
cr = csv.reader(f, delimiter='\t')
for row in cr:
if fit == 'must-fit':
#pdb.set_trace()
if 8*int(row[1]) > 9*int(row[2]):
matched_must_fit = True
class_one = [row[1], row[2]]
if fit == 'must-not-fit':
if 8*int(row[2]) > 9*int(row[1]):
matched_must_not_fit = True
class_two = [row[1], row[2]]
f.close()
existing += 1
except IOError:
missing += 1
if matched_must_fit and matched_must_not_fit:
good += 1
good_list.append({'bottom': bottom_bound, 'top': top_bound, 'n': ngram,
'kernel': kernel, 'class_one': class_one, 'class_two': class_two})
print (existing, missing, good)
pp = pprint.PrettyPrinter()
pp.pprint(good_list)
return good_list
good_list_oases = check_validation_results('oases')
print "$$$$$$$$$$$$$$$$$$$$$$$$$$$"
good_list_trinity = check_validation_results('trinity')
print "$$$$$$$$$$$$$$$$$$$$$$$$$$$"
for oases_item in good_list_oases:
for trinity_item in good_list_trinity:
if oases_item['bottom'] == trinity_item['bottom'] \
and oases_item['top'] == trinity_item['top'] \
and oases_item['n'] == trinity_item['n']\
and oases_item['kernel'].values()[0] == trinity_item['kernel'].values()[0]:
print (oases_item, trinity_item)
#intersect = set(good_list_oases) & set(good_list_trinity)
exit(1)
from classifier import *
from tr_parser import *
from metrics import *
from ngram import NGram
top_bound = 0.9
bottom_bound = 0.5
(ref, oases_reads, oases_name_index, trinity_reads, trinity_name_index) = get_assemblies("data/ref_for_reads.fasta",
"data/Oases.fasta",
"data/Trinity.fasta")
(reads_names, reads_seq) = get_reads("data/ag_1_GGCTAC_filtered.fastq")
if not reads_names or not reads_seq:
print ("reads have been read unsuccessfully")
(oases_alignment_data, trinity_alignment_data) = get_alignment_data("data/results_Oases.txt",
"data/results_Trinity.txt")
if not oases_alignment_data or not trinity_alignment_data:
print ("align have been read unsuccessfully")
(oases_reads_names, oases_transcripts_names, oases_reads_seq) = get_reads_for_assembler("data/results_oases.sam")
if not oases_reads_names or not oases_transcripts_names or not oases_reads_seq:
print ("reads for oases have been read unsuccessfully")
(trinity_reads_names, trinity_transcripts_names, trinity_reads_seq) = get_reads_for_assembler("data/results_trinity.sam")
if not trinity_reads_names or not trinity_transcripts_names or not trinity_reads_seq:
print ("reads for trinity have been read unsuccessfully")
(ref, oases_reads, oases_name_index, trinity_reads, trinity_name_index) = get_assemblies("data/ref_for_reads.fasta",
"data/Oases.fasta",
"data/Trinity.fasta")
if not ref or not oases_reads or not trinity_reads or not oases_name_index or not trinity_name_index:
print ("assemblies have been read unsuccessfully")
(oases_distances_pairs, trinity_distances_pairs) = get_distances("data/Similar_transkripts_Oases.txt",
"data/Similar_transkripts_Trinity.txt")
if oases_distances_pairs is None or trinity_distances_pairs is None:
print ("unsuccessful distance reading")
reference_reads_to_transcripts = make_index_reads_to_transcripts(reads_names,
reads_seq)
oases_reads_to_transcripts = make_index_reads_to_transcripts(oases_reads_names,
oases_transcripts_names)
trinity_reads_to_transcripts = make_index_reads_to_transcripts(trinity_reads_names,
trinity_transcripts_names)
oases_transcripts_to_reads = make_index_transcripts_to_reads(oases_name_index,
oases_transcripts_names,
oases_reads_names)
trinity_transcripts_to_reads = make_index_transcripts_to_reads(trinity_name_index,
trinity_transcripts_names,
trinity_reads_names)
oases_index_by_name = make_index_by_name(oases_name_index)
trinity_index_by_name = make_index_by_name(trinity_name_index)
#TODO: check if it's really class trinity
class_good_trinity = reads_for_class(oases_alignment_data,
oases_name_index,
oases_transcripts_to_reads,
trinity_reads_to_transcripts,
trinity_index_by_name,
trinity_distances_pairs,
trinity_alignment_data,
reference_reads_to_transcripts,
top_bound,
bottom_bound)
f = file("data/Class_Trinity.txt", "w")
pickle.dump(class_good_trinity, f)
f.close()
class_good_oases = reads_for_class(trinity_alignment_data,
trinity_name_index,
trinity_transcripts_to_reads,
oases_reads_to_transcripts,
oases_index_by_name,
oases_distances_pairs,
oases_alignment_data,
reference_reads_to_transcripts,
top_bound,
bottom_bound)
f = file("data/Class_Oases.txt", "w")
pickle.dump(class_good_oases, f)
f.close()
def generate_dictionary(alphabet, length):
c = [[]]
dictionary = []
for i in range(length):
c = [[x]+y for x in alphabet for y in c]
for sym_list in c:
dictionary.append(''.join(sym_list))
return dictionary
def init_grams_dict(n, alphabet):
dictionary = generate_dictionary(alphabet, n)
full_dict = dict()
for word in dictionary:
full_dict[word] = float(0)
return full_dict
def get_distr(strlist, n_len):
alphabet = ['A', 'C', 'G', 'T', 'N']
n = NGram(N=n_len, pad_len=0)
all_ngrams = 0
grams = init_grams_dict(n_len, alphabet)
for item in strlist:
if item == '':
continue
ngram_list = list(n._split(item))
for ng in ngram_list:
if ng in grams:
grams[ng] += float(1)
all_ngrams += 1
for item in grams.keys():
grams[item] /= all_ngrams
return grams
pp = pprint.PrettyPrinter()
#pp.pprint(grams)
#print len(grams)
#pdb.set_trace()
import plotly.plotly as py
from plotly.graph_objs import *
from scipy.stats import wilcoxon
for n in [2, 3, 4, 5, 6, 7, 8]:
if False:
reference_distr = get_distr(ref, n)
full_reads_distr = get_distr(reads_seq, n)
oases_distr = sorted(get_distr(class_good_oases, n).items())
oases_bad_distr = sorted(get_distr(list(set([item for item in reads_seq if item not in class_good_oases])), n).items())
trinity_distr = sorted(get_distr(class_good_trinity, n).items())
trinity_bad_distr = sorted(get_distr(list(set([item for item in reads_seq if item not in class_good_trinity])), n).items())
py.sign_in("al_indigo", "dca63z15bu")
trace0 = Bar(
name=str(n)+u'-граммы ридов Класса1 ("хорошие риды")',
x=[k for (k, v) in oases_distr],
y=[v for (k, v) in oases_distr],
opacity=0.9,
marker=Marker(color='black')
)
trace1 = Bar(
name=str(n)+u'-граммы ридов Класса2 ("плохие риды")',
x=[k for (k, v) in oases_bad_distr],
y=[v for (k, v) in oases_bad_distr],
opacity=0.9,
marker=Marker(color='grey')
)
trace2 = Bar(
name=str(n)+u'-граммы ридов Класса1 ("хорошие риды")',
x=[k for (k, v) in trinity_distr],
y=[v for (k, v) in trinity_distr],
opacity=0.9,
marker=Marker(color='black')
)
trace3 = Bar(
name=str(n)+u'-граммы ридов Класса2 ("плохие риды")',
x=[k for (k, v) in trinity_bad_distr],
y=[v for (k, v) in trinity_bad_distr],
opacity=0.9,
marker=Marker(color='grey')
)
data_oases = Data([trace0, trace1])
data_trinity = Data([trace2, trace3])
# print("Oases: " + str(n))
# print wilcoxon(oases_distr.values(), oases_bad_distr.values())
# print("Trinity:" + str(n))
# print wilcoxon(trinity_distr.values(), oases_bad_distr.values())
if n == 2:
layout_oases = Layout(
title=u'Вероятности встречаемости для ' + str(n) + u'-грамм в Классе1 и Классе2 для Oases',
xaxis={'title': str(n) + u'-граммы'},
yaxis={'title': u'Вероятность'},
legend=Legend(x=0, y=1)
)
layout_trinity = Layout(
title=u'Вероятности встречаемости для ' + str(n) + u'-грамм в Классе1 и Классе2 для Trinity',
xaxis={'title': str(n) + u'-граммы'},
yaxis={'title': u'Вероятность'},
legend=Legend(x=0, y=1)
)
else:
layout_oases = Layout(
title=u'Вероятности встречаемости для ' + str(n) + u'-грамм в Классе1 и Классе2 для Oases',
xaxis={'title': str(n) + u'-граммы', 'autotick': False, 'dtick': int(pow(5, n - 2))},
yaxis={'title': u'Вероятность'},
legend=Legend(x=0, y=1)
)
layout_trinity = Layout(
title=u'Вероятности встречаемости для ' + str(n) + u'-грамм в Классе1 и Классе2 для Trinity',
xaxis={'title': str(n) + u'-граммы', 'autotick': False, 'dtick': int(pow(5, n - 2))},
yaxis={'title': u'Вероятность'},
legend=Legend(x=0, y=1)
)
fig_oases = Figure(data=data_oases, layout=layout_oases)
fig_trinity = Figure(data=data_trinity, layout=layout_trinity)
plot_url1 = py.plot(fig_oases, filename=str(n)+'-grams-histogram-oases')
time.sleep(10)
plot_url2 = py.plot(fig_trinity, filename=str(n)+'-grams-histogram-trinity')
#(t, pv) = wilcoxon([float(g)/reference_len for g in reference_distr], [float(g)/all_r_len for g in all_reads_dist])
#print t, pv