def BranchDecClone(self, seq_list, clone_frequency, Tu2CNV):
Align = MegaAlignment()
TumorSampleExtract = tsp_information(self.tsp_list)
CloFreAna = CloneFrequencyAnalizer()
CloOrder, Clo2Seq = Align.name2seq(seq_list)
Align.save_mega_alignment_to_file('Test.meg', seq_list)
tree_builder = MegaMP()
tree_builder.mao_file = self.mao_file
id = 'branchdec_mega_alignment'
status = tree_builder.do_mega_mp(seq_list, id)
if status == True:
seqs_with_ancestor, tree, nade_map, mask_seq, Good_posi_info = tree_builder.alignment_least_back_parallel_muts(
True
) # True will execute code to remove redundant seqs (True is default)
else:
print 'failed to run megaMP'
BadPosiLs = [] #multiple mutations
BadPosi2ChnageCloLs = {}
for c in Good_posi_info:
Posi_Inf = Good_posi_info[c]
if Posi_Inf != ['Good']:
if Posi_Inf[0] == 'ToWild':
BadPosiLs.append(c)
BadPosi2ChnageCloLs[c] = Posi_Inf[1][0]
print 'bad positions', BadPosiLs #,BadPosi2ChnageCloLs
if BadPosiLs != []:
NewT2C2F = {}
NewT2Cls = {}
for Tu in clone_frequency:
NewC2F = {}
single_tsp_list = TumorSampleExtract.make_single_tsp_list(Tu)
CloFreDic = clone_frequency[Tu]
CNV = Tu2CNV[Tu[2:]]
Tu = Tu[2:]
TuSeq = self.tumor2seq['#' + Tu]
NewCloLs = []
NewCloLs1 = []
for Clo in CloFreDic: #original hit clo for the tumor
ChangeOptions = 'n'
# print Tu,CloFreDic
if CloFreDic[Clo] > 0:
CSeq0 = Clo2Seq['#' + Clo]
ChangePosi = [] #list to fix multiple mutaitons
NewBadPosi = [
] #remove fixed multiple mutations from BadExtMutPosi
for Bad in BadPosi2ChnageCloLs:
if BadPosi2ChnageCloLs[Bad].count(
'#' + Clo) != 0 and (CNV[Bad] == 'normal'
or CNV[Bad]
== 'Bad-normal'):
Change = 'n'
for Oth in CloFreDic: #find multiple mutations at the external branch
if Oth != Clo and CloFreDic[Oth] > 0:
Soth = Clo2Seq['#' + Oth]
if Soth[Bad] == 'T' and BadPosi2ChnageCloLs[
Bad].count('#' + Oth) == 0:
Change = 'y'
if Change == 'y':
ChangePosi.append(Bad)
else:
NewBadPosi.append(Bad)
print 'change positions', Tu, ChangePosi
if ChangePosi != []: #fix multiple mutaitons
# print 'hhh'
CutCloSeq = Align.ModSeq(CSeq0, ChangePosi, 'A',
self.Len)
NewCloLs.append(Clo + 'Cut' + Tu)
NewC2F[Clo + 'Cut' + Tu] = CloFreDic[Clo]
Clo2Seq['#' + Clo + 'Cut' + Tu] = CutCloSeq
ChangeOptions = 'y'
if ChangeOptions == 'n':
NewC2F[Clo] = 1
NewT2C2F[Tu] = NewC2F
# print Clo2Seq
hitseq_align, hitclone_frequency = CloFreAna.ListHitCloAndSeq(
NewT2C2F, Clo2Seq)
outSeqMaj, outSeqAmb, NewT2C2F = Align.CombSimClo(
hitseq_align, hitclone_frequency, 0.0)
# print outSeqMaj, NewT2C2F
return outSeqMaj, NewT2C2F
else:
return seq_list, clone_frequency
seqs_with_ancestor_newName, tsp_list, CNV_information_test,
params.freq_cutoff, ReadCount_table)
clone_frequency_cnv.regress_cnv()
final_seq1, final_clone_frequency1, final_clone_order1 = OutFile.ReNameCloFreMeg(
clone_frequency_cnv.hitclone_seq_builder,
clone_frequency_cnv.Tumor2Clone_frequency, 'number')
id = 'mega_alignment'
status = tree_builder.do_mega_mp(final_seq1, id)
if status == True:
seqs_with_ancestor, tree, nade_map, mask_seq, Initial_Good_posi_info = tree_builder.alignment_least_back_parallel_muts(
True
) # True will execute code to remove redundant seqs (True is default)
print 'best alignment'
else:
print 'failed to run megaMP'
AA, mask_seq_comb, clone_freq = Align.CombSimClo(mask_seq,
final_clone_frequency1, 0)
print 'test clone hit and remove insignificant clones'
significant_clone = cluster_test()
significant_seq, significant_clone_frequency = significant_clone.remove_insignificant_clones(
v_obs, clone_freq, mask_seq_comb, CNV_information_test,
Significant_cutoff)
Align.save_mega_alignment_to_file(params.input_id + '_CloneFinder.meg',
significant_seq)
CloFreAna.save_frequency_table_to_file(
params.input_id + '_CloneFinder.txt', significant_clone_frequency, [])
#######################
os.remove(params.input_id + '.txt')
os.remove(params.input_id + '-CNV.txt')
os.remove('Ini.meg')
os.remove('Ini_freq.txt')