def save_without_cloneID(self, Cell2BestSeq):
Align = MegaAlignment()
tree_builder = MegaML()
tree_analyzer = TreeAnalizer()
BestSeq_builder_3 = Align.UpMeg(Cell2BestSeq, self.CellLs)
Align.save_mega_alignment_to_file(self.out_file_name,
BestSeq_builder_3)
def find_decomposed_clone(self, no_back_para_mut_decomposed_seq, REP,
Tree):
Align = MegaAlignment()
CloLs, Clo2Seq = Align.name2seq(no_back_para_mut_decomposed_seq)
DecTipLs = []
DecLs = []
DecAncLs = []
RmDecom = []
for Clo in CloLs:
# ClosestAnc = Align.find_closest_anc(Clo,Clo2Seq)
# if ClosestAnc!='' :
# DecAncLs.appned(Clo)
if Clo.find('Clu') != -1:
ID = 'REP' + str(REP)
In = -1 * len(ID)
if Clo[In:] == ID:
DecLs.append(Clo)
Posi = Tree.find(Clo[1:] + ':') + len(Clo)
# print Tree[Posi]
Go = 'y'
BraLen = ''
while Go == 'y':
BraLen += Tree[Posi]
if Tree[Posi] == ',' or Tree[Posi] == ')': Go = 'n'
Posi += 1
# print Clo,BraLen
if float(BraLen[:-1]) == 0: DecAncLs.append(Clo) ######
else: DecTipLs.append(Clo)
# print DecLs,DecAncLs
if DecLs == []: NewDecom = 'n'
elif DecTipLs != []:
NewDecom = 'y'
for Tip in DecTipLs:
TipSeq = Clo2Seq[Tip]
OriTu = Tip.split('Clu')[0]
# TipMutC
Anc = 'n'
for Clo in Clo2Seq:
if Clo != Tip: # and OriTu!=Clo:
UniNum = Align.CountAdditionalMut(TipSeq, Clo2Seq[Clo])
if UniNum == 0: Anc = 'y'
if Anc == 'y': RmDecom.append(Tip)
else: NewDecom = 'anc'
# NewDecom='anc'
# for Dclo in DecLs:
# if DecAncLs.count(Dclo)==0: NewDecom='y'
# print Clo2Seq.keys()
if RmDecom == []: NewClo2Seq_buil = no_back_para_mut_decomposed_seq
else:
NewCloDic = {}
for Clo in Clo2Seq:
if RmDecom.count(Clo) == 0: NewCloDic[Clo] = Clo2Seq[Clo]
NewClo2Seq_buil = Align.UpMeg(NewCloDic, [])
return NewDecom, RmDecom, NewClo2Seq_buil
def add_back_anc(self, Sub_seq_builder, All_seq_builder):
Align = MegaAlignment()
Ls, Sub = Align.name2seq(Sub_seq_builder)
Ls, All = Align.name2seq(All_seq_builder)
Clo2Seq = {}
for Clo in All:
if Sub.has_key(Clo) == True: Clo2Seq[Clo] = Sub[Clo]
else: Clo2Seq[Clo] = All[Clo]
Seq_Buil = Align.UpMeg(Clo2Seq, [])
return Seq_Buil
def regress_cnv(self):
Align = MegaAlignment()
CloFreAna = CloneFrequencyAnalizer()
self.Tumor2Clone_frequency = {}
HitCloSeq_dic = {}
self.tumor2CNVSNVposi = {}
# print 'nnls removing SNV-CNVs'
for tumor in self.v_obs:
# print tumor
v_obs_single = self.v_obs[tumor]
v_obs_single_sub = []
Seq_dic_sub = {}
RmSNVPosi = []
CNVls = self._CNV_file[tumor]
Len = len(CNVls)
c = 0
while c < Len:
if CNVls[c] == 'normal':
v_obs_single_sub.append(v_obs_single[c])
else:
RmSNVPosi.append(c)
c += 1
for Clo in self.ini_clone_order:
NewSeq = ''
OldSeq = self.ini_clone_seq[Clo]
c = 0
while c < Len:
if RmSNVPosi.count(c) == 0: NewSeq += OldSeq[c]
c += 1
Seq_dic_sub[Clo] = NewSeq
self.tumor2CNVSNVposi[tumor] = RmSNVPosi
MutWildAlleleCount_noCNV = self.make_mut_wild_allele_count_noCNV(
{}, self.ini_clone_order,
Seq_dic_sub) #PreAbsCNV, clone_order, SNV_seq, Tu2CloFre
Cmatrix_noCNV, Cmatrix_noCNV_dic = self.make_Min(
self.ini_clone_order, Seq_dic_sub, MutWildAlleleCount_noCNV)
self.Cmatrix_noCNV_mat = Cmatrix_noCNV
self.Cmatrix_noCNV_dic = Cmatrix_noCNV_dic
Clone2Freq = self.do_nnls0(Cmatrix_noCNV, self.ini_clone_order,
v_obs_single_sub)
self.Tumor2Clone_frequency['T-' + tumor] = Clone2Freq
for Clo in Clone2Freq:
if Clone2Freq[Clo] > 0:
if HitCloSeq_dic.has_key('#' + Clo) != True:
HitCloSeq_dic['#' + Clo] = self.ini_clone_seq['#' +
Clo]
self.hitclone_seq_builder = Align.UpMeg(HitCloSeq_dic, [])
CloFreAna.save_frequency_table_to_file('Ini_freq.txt',
self.Tumor2Clone_frequency, [])
Align.save_mega_alignment_to_file('Ini.meg', self.hitclone_seq_builder)
def finalize_results(self, decomposed_seq_builder,
decomposed_Tumor2Clone_frequency,
origianl_seq_builder, original_Tumor2Clone_frequency,
REP):
Align = MegaAlignment()
Ls, DecomSeqDic = Align.name2seq(decomposed_seq_builder)
# print Ls
Ls, OriSeqDic = Align.name2seq(origianl_seq_builder)
NewCloSeqDic = {}
NewCloFre = {}
# print decomposed_Tumor2Clone_frequency,original_Tumor2Clone_frequency
for Tu in original_Tumor2Clone_frequency:
if decomposed_Tumor2Clone_frequency.has_key(Tu) != True:
CloFre = original_Tumor2Clone_frequency[Tu]
for Clo in CloFre:
if CloFre[Clo] > 0:
NewCloSeqDic['#' + Clo] = OriSeqDic['#' + Clo]
elif decomposed_Tumor2Clone_frequency[Tu] == {}:
CloFre = original_Tumor2Clone_frequency[Tu]
for Clo in CloFre:
if CloFre[Clo] > 0:
NewCloSeqDic['#' + Clo] = OriSeqDic['#' + Clo]
else:
CloFre0 = decomposed_Tumor2Clone_frequency[Tu]
CloFre = {}
for Clo in CloFre0:
Fre = CloFre0[Clo]
if Fre > 0:
if (Clo.find('Clu') != -1 and Clo.find('REP')
== -1) or Clo.find('REP' + str(REP - 1)) != -1:
CloFre[Clo + 'REP' + str(REP)] = Fre
NewCloSeqDic['#' + Clo + 'REP' +
str(REP)] = DecomSeqDic['#' + Clo]
else:
CloFre[Clo] = Fre
if OriSeqDic.has_key('#' + Clo) == True:
NewCloSeqDic['#' + Clo] = OriSeqDic['#' + Clo]
else:
NewCloSeqDic['#' + Clo] = DecomSeqDic['#' +
Clo]
NewCloFre[Tu] = CloFre
rename_seq_builder = Align.UpMeg(NewCloSeqDic, [])
# open('AA','r').readlines()
return rename_seq_builder, NewCloFre
def add_back_CNVSNV(self, DecomTu2Seq_builder_sub, CNV_information,
original_seqs_builder_all,
original_Tumor2Clone_frequency, tsp_list):
all_tsp = tsp_information(tsp_list)
v_obs = all_tsp.tumor2alt_frequency()
Seq_all_dic = {}
Align = MegaAlignment()
Original_clols, Original_clodic_all = Align.name2seq(
original_seqs_builder_all)
for Tumor in DecomTu2Seq_builder_sub:
Seq_builder_sub = DecomTu2Seq_builder_sub[Tumor]
if Seq_builder_sub != []:
SNVfre_list = v_obs[Tumor]
CloLs, Clo2Seq = Align.name2seq(Seq_builder_sub)
CNVinfo = CNV_information[Tumor]
Len = len(CNVinfo)
# print Tumor, Clo2Seq.keys()
for Clo in Clo2Seq:
Seq_sub = Clo2Seq[Clo]
c_seq = 0
c_all = 0
Seq_all = ''
while c_all < Len:
if CNVinfo[c_all] == 'normal':
Seq_all += Seq_sub[c_seq]
c_seq += 1
else:
if SNVfre_list[c_all] == 0: Seq_all += 'A'
else: Seq_all += '?'
c_all += 1
if Original_clodic_all.has_key(Clo) == True:
Seq_all_dic[Clo] = Original_clodic_all[Clo]
else:
Seq_all_dic[Clo] = Seq_all
else:
CloFre = original_Tumor2Clone_frequency['T-' + Tumor]
for Clo in CloFre:
if CloFre[Clo] > 0:
if Seq_all_dic.has_key('#' + Clo) != True:
Seq_all_dic['#' + Clo] = Original_clodic_all['#' +
Clo]
decom_all_seq_builder = Align.UpMeg(Seq_all_dic, [])
return decom_all_seq_builder
def get_best_alignment(self, files, ID, remove_redundant, tree_list):
Align = MegaAlignment()
AncFile = files[0]
print 'processing file: ' + AncFile
AncFile=open(AncFile,'r').readlines()
Lines=[]
Add='n'
for i in AncFile:
if i.find('Index ')!=-1: Add='y'
elif Add=='y':
i=i.strip().split(' ')
In=[]
for ii in i:
if ii!='': In.append(ii)
Lines.append(In)
Anc2Seq=self.GetRel(Lines)
outseq= Align.UpMeg(Anc2Seq,[])
outseq = Align.remove_redund_seqs(outseq)
return outseq, tree_list[0]#, best_outset[1], best_outset[2], best_outset[4] #NadeMapInfo, mask_seq, Good_posi_info]
def regress_cnv(self):
Align = MegaAlignment()
self.Tumor2Clone_frequency = {}
HitCloSeq_dic = {}
for tumor in self.v_obs:
v_obs_single = self.v_obs[tumor]
v_obs_single_sub = []
Seq_dic_sub = {}
RmSNVPosi = []
CNVls = self._CNV_file[tumor]
Len = len(CNVls)
c = 0
while c < Len:
if CNVls[c] == 'normal':
v_obs_single_sub.append(v_obs_single[c])
else:
RmSNVPosi.append(c)
c += 1
for Clo in self.ini_clone_order:
NewSeq = ''
OldSeq = self.ini_clone_seq[Clo]
c = 0
while c < Len:
if RmSNVPosi.count(c) == 0: NewSeq += OldSeq[c]
c += 1
Seq_dic_sub[Clo] = NewSeq
print(tumor, 'exclude bad SNVs for clone frequency computation',
RmSNVPosi)
Cmatrix_noCNV = self.make_Cmatrix(Seq_dic_sub)
Clone2Freq = self.do_nnls0(Cmatrix_noCNV, v_obs_single_sub)
self.Tumor2Clone_frequency['T-' + tumor] = Clone2Freq
for Clo in Clone2Freq:
if Clone2Freq[Clo] > 0:
HitCloSeq_dic['#' + Clo] = self.ini_clone_seq['#' + Clo]
self.hitclone_seq_builder = Align.UpMeg(HitCloSeq_dic, [])
def get_candidate_decomposed_clones(self, target_tumor, CluInf_tu,Tuseq):
Align = MegaAlignment()
NameOrder, Name2Seq = Align.name2seq(CluInf_tu[0])
LenSeq = len(Name2Seq[NameOrder[0]])
SigCluLs=[]
for Name in NameOrder: #Root is the first cluster or initial candidate clone
if Name!='#Clu0' and Name.find('Clu')!=-1: SigCluLs.append(Name)
CluCombLs,IDend=self.combinations([],SigCluLs,0,{})
print(target_tumor,'make cluster comb',SigCluLs,CluCombLs,NameOrder)
if CluCombLs!={}:
CloCan2Seq={}
Got_Candidate='n'
for Root in NameOrder: #Root is the first cluster or initial candidate clone
if Root=='#Clu0' or Root.find('Clu')==-1:
RootSeq=Name2Seq[Root]
if Root=='#Clu0': CloCan2Seq['#'+target_tumor+'Clu0']=RootSeq #Root is candidate clone
RootMut=Align.GetMutPos(RootSeq)
Got_Candidate='y'
if CluCombLs!={}:
for ID in CluCombLs:
CluLs=CluCombLs[ID]
CluN=''
MutPosLs=[]
for Clu in CluLs:
Seq=Name2Seq[Clu]
CluMut=Align.GetMutPos(Seq)
MutPosLs+= CluMut
CluN+=Clu.replace('#','')
Good='y'
for Mut in MutPosLs:
if RootMut.count(Mut)!=0: Good='n'
if Good=='y':
AllMutPosLs=MutPosLs+RootMut
Seq=Align.ModSeq('A'*LenSeq,AllMutPosLs,'T',LenSeq)
Redun_ls=Align.find_redundant(Seq,self.clone_seq) #all other clones ####
if Redun_ls==[]:
CloCan2Seq['#'+target_tumor+Root.replace('#','')+CluN]=Seq
if CloCan2Seq!={}:
CloCan2Seq.update(self.clone_seq)
Can_list=list(CloCan2Seq.keys())
new_seq = Align.UpMeg(CloCan2Seq,Can_list)
clone_frequency_combo = CloneFrequencyComputer_cnv1(new_seq, {target_tumor:self.v_obs[target_tumor]}, {target_tumor:self._CNV_file[target_tumor]}, self.freq_cutoff)
clone_frequency_combo.regress_cnv()
CluComboHit=self.findcombohit(clone_frequency_combo.hitclone_seq_builder)
if CluComboHit=='y':
print('test the quality of clustercombo, by removing tumor seq (if any)')
hit_seq_ls,hit_seq_dic=Align.name2seq(clone_frequency_combo.hitclone_seq_builder)
Tuseq_ls=Align.find_redundant(Tuseq,hit_seq_dic)
if Tuseq_ls==[]:
print('tumor genotype did not hit, so clustercombo is good')
return clone_frequency_combo.hitclone_seq_builder,Tuseq
else:
print('tumor genotype was hit, so test if clustercombo still hit without tumor genotype: testing if clustercombo genotypes fit well')
Tuseq_ls=Align.find_redundant(Tuseq,CloCan2Seq)
sub_hit_seq=[]
for seqname in CloCan2Seq:
if Tuseq_ls.count(seqname)==0:sub_hit_seq+=[seqname,CloCan2Seq[seqname]]
clone_frequency_combo_new = CloneFrequencyComputer_cnv1(sub_hit_seq, {target_tumor:self.v_obs[target_tumor]}, {target_tumor:self._CNV_file[target_tumor]}, self.freq_cutoff)
clone_frequency_combo_new.regress_cnv()
CluComboHit=self.findcombohit(clone_frequency_combo_new.hitclone_seq_builder)
if CluComboHit=='y':
return clone_frequency_combo_new.hitclone_seq_builder,Tuseq
else:
return CluInf_tu[0],''
else: return CluInf_tu[0] ,''
else: return CluInf_tu[0],''
return CluInf_tu[0],''
print(Message)
summary_file += Message + '\n'
DecomTuNum = len(Message.split(','))
if Message[:10] != 'decomposed' or DecomTuNum > (1.0 * len(v_obs) /
2):
Repeat = 'n'
final_seq = clone_frequency_cnv.hitclone_seq_builder
final_clofre = clone_frequency_cnv.Tumor2Clone_frequency
print('no more new clones',
clone_frequency_cnv.Tumor2Clone_frequency)
else:
hit_seq_dic['#hg19'] = 'A' * num_sites
hit_seq_build = Align.UpMeg(hit_seq_dic,
list(hit_seq_dic.keys()))
id = 'dec_mega_alignment'
status = tree_builder.do_mega_mp(hit_seq_build, id)
if status == True and ItC < 5:
decomseqs_with_ancestor, A1 = tree_builder.alignment_least_back_parallel_muts(
) # True will execute code to remove redundant seqs (True is default)
# self._cleanup_temp_files()
clone_frequency_cnv2 = CloneFrequencyComputer_cnv1(
decomseqs_with_ancestor, v_obs, CNV_information_test,
params.freq_cutoff)
clone_frequency_cnv2.regress_cnv()
decomseqs_with_ancestor_clone_freq = clone_frequency_cnv2.Tumor2Clone_frequency
decomseqs_with_ancestor = clone_frequency_cnv2.hitclone_seq_builder
seqs_with_ancestor, A1, A2 = OutFile.ReNameCloFreMeg(
decomseqs_with_ancestor,
decomseqs_with_ancestor_clone_freq, 'number')
def remove_insignificant_clones(self, v_obs, CloFre_clone,
clone_seq_builder, Tu2CNV, Cut):
Align = MegaAlignment()
OutAncAll = 'SigTest.txt'
outAncAll = 'tumor\tDecsendant-Ancestor\tSNV posi\tType\tObsFre\n'
Clone_list, clone_seq_dic = Align.name2seq(clone_seq_builder)
new_clone_freq = {}
new_clone_seq_dic = {}
for tumor in v_obs:
CNV = Tu2CNV[tumor]
Clo2Fre = CloFre_clone['T-' + tumor]
ObsFre = v_obs[tumor]
clone_order = []
MutNum2Clo = {}
MutNum_ls = []
for Clo in Clo2Fre:
if Clo2Fre[Clo] > 0:
MutPosLs = Align.GetMutPos(clone_seq_dic['#' + Clo])
MutNum = len(MutPosLs)
if MutNum2Clo.has_key(MutNum) != True:
MutNum2Clo[MutNum] = []
MutNum2Clo[MutNum].append(Clo)
MutNum_ls.append(MutNum)
MutNum_ls = list(set(MutNum_ls))
MutNum_ls.sort(reverse=True)
for MutNum in MutNum_ls:
clone_order += MutNum2Clo[MutNum]
CloNum = len(clone_order)
C1Max = CloNum - 1
InsigLs = []
C1 = 0
while C1 < C1Max:
Clo1 = clone_seq_dic['#' + clone_order[C1]]
num_sites = len(Clo1)
Min_num = 0.01 * num_sites
C2 = C1 + 1
while C2 < CloNum:
Clo2 = clone_seq_dic['#' + clone_order[C2]]
Share = []
Unique = []
c = 0
while c < num_sites:
if CNV[c] == 'normal':
if Clo1[c] == 'T' and Clo2[c] == 'T':
Share.append(ObsFre[c])
outAncAll += tumor + '\t' + clone_order[
C1] + '-' + clone_order[C2] + '\t' + str(
c) + '\tShare\t' + str(
ObsFre[c]) + '\n'
elif Clo1[c] == 'T' and Clo2[c] == 'A':
Unique.append(ObsFre[c])
outAncAll += tumor + '\t' + clone_order[
C1] + '-' + clone_order[C2] + '\t' + str(
c) + '\tUnique\t' + str(
ObsFre[c]) + '\n'
c += 1
if (len(Share) < 3
or len(Unique) < 3) or (len(Share) < Min_num
or len(Unique) < Min_num):
P = 1
else:
P = scipy.stats.ttest_ind(Share,
Unique,
equal_var=False)
P = P[-1]
if P > Cut:
if clone_order[C1].find('Clu') != -1 and clone_order[
C2].find('Clu') == -1:
InsigLs.append(clone_order[C1])
else:
InsigLs.append(clone_order[C2])
C2 += 1
C1 += 1
InsigLs = list(set(InsigLs))
if InsigLs != []: print 'insignificant clones', tumor, InsigLs
new_clone_fre_in = {}
for Clo in Clo2Fre:
if Clo2Fre[Clo] > 0 and InsigLs.count(Clo) == 0:
new_clone_fre_in[Clo] = Clo2Fre[Clo]
new_clone_seq_dic['#' + Clo] = clone_seq_dic['#' + Clo]
new_clone_freq['T-' + tumor] = new_clone_fre_in
new_seq_builder = Align.UpMeg(new_clone_seq_dic, [])
return new_seq_builder, new_clone_freq
def get_candidate_decomposed_clones(self, target_tumor):
Align = MegaAlignment()
CluInf_tu = self.ClusterInfo #[target_tumor]
NameOrder, Name2Seq = Align.name2seq(CluInf_tu[2])
# print target_tumor, CluInf_tu[0],CluInf_tu[1]
HitCloCluLs = CluInf_tu[1] #['T-'+target_tumor]
TuIdentical_seq = self.identical_seq['T-' + target_tumor]
LenSeq = len(Name2Seq[NameOrder[0]])
TuSeq = self.T2Seq['#' + target_tumor]
Clu2center = CluInf_tu[0]
SigCluLs = []
HitCloLs = []
HitCloSeq_dic = {}
RootClu = ''
LarCen = 0.0
for Hit in HitCloCluLs:
if HitCloCluLs[Hit] > 0.02:
if Hit[:len(target_tumor +
'Clu')] == target_tumor + 'Clu' and Hit.find(
'REP') == -1:
SigCluLs.append(Hit)
CluName = 'Clu' + Hit.split('Clu')[-1]
Center = float(Clu2center[CluName].split('-')[0])
for CluN in Clu2center:
Center2 = float(Clu2center[CluN].split('-')[0])
Sign = Clu2center[CluN].split('-')[1]
if Center == Center2 and CluName != CluN:
SigCluLs.append(target_tumor + CluN)
if LarCen < Center2: # and Sign=='Pos': #Pos for middle cut, Neg for K-means
LarCen = Center2
if Center == Center2: RootClu = target_tumor + CluN
elif LarCen <= Center2 and Sign == 'Pos': #Pos for middle cut, Neg for K-means
LarCen = Center2
if Center == Center2: RootClu = target_tumor + CluN
else:
HitCloLs.append(Hit)
HitCloSeq_dic['#' + Hit] = Name2Seq['#' + Hit]
# print 'cluls0',SigCluLs, HitCloLs, RootClu
if RootClu != '':
SigCluLs.remove(RootClu)
HitCloLs.append(RootClu)
# print 'cluls',SigCluLs, HitCloLs, RootClu
if SigCluLs != []:
CluCombLs, IDend = self.combinations([], SigCluLs, 0, {})
else:
CluCombLs = {}
# print CluCombLs
if RootClu != '' or CluCombLs != {}:
print 'make cluster comb'
CloCan2Seq = {}
Got_Candidate = 'n'
for Root in HitCloLs:
RootSeq = Name2Seq['#' + Root]
LenSeq = len(RootSeq)
RootMut = Align.GetMutPos(RootSeq)
CloCan2Seq['#' + Root] = RootSeq
Got_Candidate = 'y'
if CluCombLs != {}:
for ID in CluCombLs:
CluLs = CluCombLs[ID]
# print 'try make combo',Root,CluLs
CluN = ''
MutPosLs = []
for Clu in CluLs:
Seq = Name2Seq['#' + Clu]
CluMut = Align.GetMutPos(Seq)
MutPosLs += CluMut
CluN += Clu.replace(target_tumor + 'Clu', 'Clu')
MutPosLs = list(set(MutPosLs))
Go = 'y'
for Mut in MutPosLs:
if RootMut.count(Mut) != 0: Go = 'n'
if Go == 'y':
AllMutPosLs = MutPosLs + RootMut
Seq = Align.ModSeq('A' * LenSeq, AllMutPosLs, 'T',
LenSeq)
Redun_ls = Align.find_redundant(Seq, HitCloSeq_dic)
if Redun_ls == []:
CloCan2Seq['#' + target_tumor + Root.replace(
target_tumor + 'Clu', 'Clu') + CluN] = Seq
Got_Candidate = 'y'
if Got_Candidate == 'y':
Can_list = CloCan2Seq.keys()
# print 'find the good comb',Can_list
new_seq = Align.UpMeg(CloCan2Seq, Can_list)
alt_frequency = []
CNVls = self.CNV_info[target_tumor]
Len = len(CNVls)
c = 0
TuMatPosi = []
tumor_genotype = ''
while c < Len:
if CNVls[c] == 'normal':
alt_frequency.append(self.v_obs[target_tumor][c])
if self.v_obs[target_tumor][c] > 0:
TuMatPosi.append(c)
tumor_genotype += 'T'
else:
tumor_genotype += 'A'
c += 1
clone_frequency = CloneFrequencyComputer_cnv1({}, {}, {},
self.freq_cutoff,
{})
MutWildAlleleCount_noCNV = clone_frequency.make_mut_wild_allele_count_noCNV(
{}, Can_list,
CloCan2Seq) #PreAbsCNV, clone_order, SNV_seq, Tu2CloFre
Cmatrix_noCNV, Cmatrix_noCNV_dic = clone_frequency.make_Min(
Can_list, CloCan2Seq, MutWildAlleleCount_noCNV)
Clone2Freq = clone_frequency.do_nnls0(Cmatrix_noCNV, Can_list,
alt_frequency)
out2 = ['#MEGA', '!Title SNVs;', '!Format datatype=dna;', ' ']
AllMut = []
NewClone2Freq = {}
CluHit = 'n'
for Clo0 in Clone2Freq:
NewClone2Freq[Clo0] = Clone2Freq[Clo0]
if Clone2Freq[Clo0] > 0.02:
SeqMutPos = Align.GetMutPos(CloCan2Seq['#' + Clo0])
TuSeq = 'y'
for Mut in SeqMutPos:
if TuMatPosi.count(Mut) != 0: AllMut.append(Mut)
for Mut in TuMatPosi:
if SeqMutPos.count(Mut) == 0: TuSeq = 'n'
Iden = 'n'
for OriClo in self.OriAnc2Seq0:
c = 0
Dif = 'n'
while c < Len:
if self.OriAnc2Seq0[OriClo][c] != CloCan2Seq[
'#' + Clo0][c]:
Dif = 'y'
c += 1
if Dif == 'n': Iden = OriClo
if Iden != 'n':
out2 += [Iden, self.OriAnc2Seq0[Iden]]
NewClone2Freq[Iden[1:]] = Clone2Freq[Clo0]
NewClone2Freq[Clo0] = 0
elif TuSeq == 'n':
out2 += [
'#' + Clo0.replace(target_tumor + target_tumor,
target_tumor),
CloCan2Seq['#' + Clo0]
]
if Clo0.find('Clu') != -1 and Clo0.find(
'REP') == -1:
CluHit = 'y'
else:
out2 += [
'#' + target_tumor, CloCan2Seq['#' + Clo0]
]
NewClone2Freq[target_tumor] = Clone2Freq[Clo0]
NewClone2Freq[Clo0] = 0
AllMut = list(set(AllMut))
if len(AllMut) < len(TuMatPosi):
out2 += ['#' + target_tumor, tumor_genotype]
if CluHit == 'y':
# print 'Decomposed!' ,target_tumor,NewClone2Freq,out2
return out2, NewClone2Freq
return [], {}
def get_tree_with_branchLen(self, ID):
Align = MegaAlignment()
self.GetOut(ID + '.nwk', self.RescaledTree)
SeqLs = Align.UpMeg(self.nodeid2seq, [])
Align.save_mega_alignment_to_file(ID + '_NodeSeq.meg', SeqLs)
