def bayes_fitting(self):
self = pickle.load(
open(
"/%s/%s_self_para_eval.p" %
(self.output_directory, self.output_prefix), 'rb'))
logging.info('Modeling the mean and variance ...')
if self.genes_varmodeling > 0:
self.mrm = MeanVarModel()
#self.mrm.model_mean_disp_by_lm(self.allgenedict)
self.mrm.model_mean_disp_by_glm(self.allgenedict,
self.output_prefix, self.size_f)
else:
mrm = None
pickle.dump(
self,
open(
"/%s/%s_self_fitting.p" %
(self.output_directory, self.output_prefix), 'wb'))
def bayes_fitting(self):
self=pickle.load(open("/%s/%s_self_para_eval.p" %(self.output_directory,self.output_prefix),'rb'))
logging.info('Modeling the mean and variance ...')
if self.genes_varmodeling>0:
self.mrm=MeanVarModel()
#self.mrm.model_mean_disp_by_lm(self.allgenedict)
self.mrm.model_mean_disp_by_glm(self.allgenedict,self.output_prefix,self.size_f)
else:
mrm=None
pickle.dump(self,open("/%s/%s_self_fitting.p" %(self.output_directory,self.output_prefix),'wb'))
class Mageck_Bayes():
def __init__(self, options):
self.adjust_method = options.adjust_method
self.beta_labels = options.beta_labels
self.count_table = options.count_table
self.design_matrix = options.design_matrix
self.genes_varmodeling = options.genes_varmodeling
self.include_samples = options.include_samples
self.output_prefix = options.output_prefix
self.file_directory = os.getcwd()
self.output_directory = "%s/%s" % (os.getcwd(), self.output_prefix)
if os.path.exists(self.output_directory) == False:
os.makedirs(self.output_directory)
self.permutation_round = options.permutation_round
self.outliers_removal = options.outliers_removal
self.PPI_prior = options.PPI_prior
self.PPI_weighting = options.PPI_weighting
self.PPI_diagnosis = None
self.non_PPI_beta_prior_variance = None
self.allgenedict = None
self.size_f = None
self.mrm = None
self.log_list = defaultdict(list)
self.selection_constant = None
self.invalid_gRNA_dict = None
self.negative_control = options.negative_control
self.negative_control_gRNAs = None
self.norm_method = options.norm_method
def bayes_init(self):
maxgene = np.inf
if self.negative_control == None:
self.allgenedict, self.invalid_gRNA_dict = read_gene_from_file(
self.count_table,
includesamples=self.include_samples,
negative_control=self.negative_control)
else:
self.allgenedict, self.invalid_gRNA_dict, self.negative_control_gRNAs = read_gene_from_file(
self.count_table,
includesamples=self.include_samples,
negative_control=self.negative_control)
# calculate the size factor
cttab_sel = {}
for (geneid, gk) in self.allgenedict.iteritems():
sgid = gk.sgrnaid
sgreadmat = gk.nb_count.getT().tolist()
for i in range(len(sgid)):
cttab_sel[sgid[i]] = sgreadmat[i]
if hasattr(self, 'norm_method'):
if self.norm_method != 'none':
self.size_f = normalizeCounts(
cttab_sel,
method=self.norm_method,
returnfactor=True,
reversefactor=True,
negative_control_gRNAs=self.negative_control_gRNAs)
else:
self.size_f = None
else:
self.size_f = normalizeCounts(cttab_sel,
returnfactor=True,
reversefactor=True)
logging.info('size factor: ' + ','.join([str(x) for x in self.size_f]))
#logging.info(self.design_matrix)
desmat = self.design_matrix
for (tgid, tginst) in self.allgenedict.iteritems():
#if tgid=="INO80B":
tginst.design_mat = desmat
iteratenbem(tginst,
debug=False,
estimateeff=False,
alpha_val=0.05,
size_factor=self.size_f)
tginst.w_estimate = []
deviation(self.allgenedict)
self.non_PPI_beta_prior_variance = beta_non_PPI_prior_calculation(
self.allgenedict)
pickle.dump(
self,
open(
"/%s/%s_self_bayes_init.p" %
(self.output_directory, self.output_prefix), 'wb'))
def parameter_reevalution(self):
self = pickle.load(
open(
"/%s/%s_self_bayes_init.p" %
(self.output_directory, self.output_prefix), 'rb'))
logging.info('Reestimate dispersion ...')
ngenes = 0
for (tgid, tginst) in self.allgenedict.iteritems():
if ngenes < 3000:
try:
#logging.info(tgid)
sgrna_wide_dispersion_estimation_MAP_v2(
tginst, self.design_matrix)
ngenes += 1
except:
pass
pickle.dump(
self,
open(
"/%s/%s_self_para_eval.p" %
(self.output_directory, self.output_prefix), 'wb'))
def bayes_fitting(self):
self = pickle.load(
open(
"/%s/%s_self_para_eval.p" %
(self.output_directory, self.output_prefix), 'rb'))
logging.info('Modeling the mean and variance ...')
if self.genes_varmodeling > 0:
self.mrm = MeanVarModel()
#self.mrm.model_mean_disp_by_lm(self.allgenedict)
self.mrm.model_mean_disp_by_glm(self.allgenedict,
self.output_prefix, self.size_f)
else:
mrm = None
pickle.dump(
self,
open(
"/%s/%s_self_fitting.p" %
(self.output_directory, self.output_prefix), 'wb'))
def bayes_major(self):
self = pickle.load(
open(
"/%s/%s_self_fitting.p" %
(self.output_directory, self.output_prefix), 'rb'))
logging.info('Run the algorithm for the second time ...')
ngene = 0
for (tgid, tginst) in self.allgenedict.iteritems():
#logging.info(tgid)
iteratenbem(tginst,
debug=False,
meanvarmodel=self.mrm,
restart=False,
size_factor=self.size_f,
beta1_prior_var=self.non_PPI_beta_prior_variance)
ngene += 1
for (tgid, tginst) in self.allgenedict.iteritems():
if len(tginst.w_estimate) == 0:
tginst.w_estimate = np.ones(len(tginst.sgrnaid))
pickle.dump(
self,
open(
"/%s/%s_self_bayes_major.p" %
(self.output_directory, self.output_prefix), 'wb'))
def constant_optimization(self):
# PPI
self = pickle.load(
open(
"/%s/%s_self_bayes_major.p" %
(self.output_directory, self.output_prefix), 'rb'))
logging.info('PPI validation...')
PPI_main(self)
if self.PPI_diagnosis == True:
beta_prior_output(self)
'''
copy_self=copy.copy(self)
copy_self.count_table="singlized_{}".format(self.count_table)
copy_self.output_prefix="{}_mageck_bayes".format(copy_self.count_table)
copy_self.output_directory="{}/{}".format(os.getcwd(),copy_self.output_prefix)
if os.path.exists(copy_self.output_directory)==False:
os.makedirs(copy_self.output_directory)
if not os.path.isfile("/%s/self_pre_dispersion_fitting_round.p" %copy_self.output_directory):
os.chdir(self.file_directory)
sgRNA_production(self.count_table)
copy_self.allgenedict,copy_self.invalid_gRNA_dict=read_gene_from_file(copy_self.count_table,includesamples=copy_self.include_samples)
for (tgid,tginst) in copy_self.allgenedict.items():
tginst.design_mat=copy_self.design_matrix
iteratenbem(tginst,debug=False,meanvarmodel=self.mrm,restart=False,removeoutliers=self.outliers_removal,size_factor=self.size_f,beta1_prior_var=self.non_PPI_beta_prior_variance)
pickle.dump(copy_self,open("/%s/self_pre_dispersion_fitting_round.p" %copy_self.output_directory,'wb'))
else:
copy_self=pickle.load(open("/%s/self_pre_dispersion_fitting_round.p" %copy_self.output_directory,'rb'))
logging.info("signlized_sgRNA processed!")
sgRNA_beta=background(copy_self)
beta1=list(sgRNA_beta.values())
median=np.median(beta1)
abs_beta1=[abs(i-median) for i in beta1]
abs_beta1.sort()
std_beta1=float(np.percentile(abs_beta1,95))/norm.ppf(0.975)
for geneid in list(self.allgenedict.keys()):
sgrnaid=self.allgenedict[geneid].sgrnaid
temp=[]
for sgRNA in sgrnaid:
temp.append(np.log(norm.pdf(sgRNA_beta[sgRNA],median,std_beta1)))
self.allgenedict[geneid].sgrna_probability=temp
'''
logging.info('Estimate selection constant...')
bayes_selection_constnat_optimization(self)
pickle.dump(
self,
open(
"/%s/%s_self_constant_optimization.p" %
(self.output_directory, self.output_prefix), 'wb'))
def bayes_iteration(self):
logging.info('Iteratioin...')
PPI_choice = [False, True]
for PPI in PPI_choice:
self = pickle.load(
open(
"/%s/%s_self_constant_optimization.p" %
(self.output_directory, self.output_prefix), 'rb'))
self.PPI_prior = PPI
if self.PPI_diagnosis == False and self.PPI_prior == True:
pass
else:
self.outliers_removal = False
for (tgid, tginst) in self.allgenedict.iteritems():
iteratenbem(
tginst,
debug=False,
meanvarmodel=self.mrm,
restart=False,
PPI_prior=self.PPI_prior,
removeoutliers=self.outliers_removal,
size_factor=self.size_f,
beta1_prior_var=self.non_PPI_beta_prior_variance)
pickle.dump(
self,
open(
"/%s/%s_self_bayes_iteration_PPI_%s_outliers_removal_%s.p"
% (self.output_directory, self.output_prefix,
self.PPI_prior, self.outliers_removal), 'wb'))
#base_index=np.where(~self.design_matrix[:,1:].any(axis=1))[0]
#transform_list=[1 if i in base_index else -1 for i in range(self.design_matrix.shape[0])]
#transform_matrix=np.matrix(transform_list)
for (tgid, tginst) in self.allgenedict.items():
bayes_selection(tginst,
log_list=self.log_list,
selection_constant=self.selection_constant)
self.outliers_removal = True
for (tgid, tginst) in self.allgenedict.iteritems():
iteratenbem(
tginst,
debug=False,
meanvarmodel=self.mrm,
restart=False,
PPI_prior=self.PPI_prior,
removeoutliers=self.outliers_removal,
size_factor=self.size_f,
beta1_prior_var=self.non_PPI_beta_prior_variance)
pickle.dump(
self,
open(
"/%s/%s_self_bayes_iteration_PPI_%s_outliers_removal_%s.p"
% (self.output_directory, self.output_prefix,
self.PPI_prior, self.outliers_removal), 'wb'))
def bayes_output(self):
os.chdir(self.output_directory)
for mark in [[True, False], [True, True], [False, False],
[False, True], ["major", "major"]]:
if mark != ["major", "major"]:
file = "%s_self_bayes_iteration_PPI_%s_outliers_removal_%s.p" % (
self.output_prefix, mark[0], mark[1])
else:
file = "%s_self_bayes_major.p" % self.output_prefix
if file in glob.glob("*"):
self = pickle.load(open(file, 'rb'))
# permutation
iteratenbem_permutation(
self.allgenedict,
nround=self.permutation_round,
meanvarmodel=self.mrm,
size_factor=self.size_f,
beta1_prior_var=self.non_PPI_beta_prior_variance)
# correct for FDR
gene_fdr_correction(self.allgenedict, self.adjust_method)
genefile = self.output_prefix + '_PPI_' + str(
mark[0]) + '_outliers_removal_' + str(
mark[1]) + '.gene_summary.txt'
sgrnafile = self.output_prefix + '_PPI_' + str(
mark[0]) + '_outliers_removal_' + str(
mark[1]) + '.sgrna_summary.txt'
logging.info(genefile)
logging.info(sgrnafile)
logging.info('Writing gene results to ' + genefile)
logging.info('Writing sgRNA results to ' + sgrnafile)
write_gene_to_file(self.allgenedict,
genefile,
betalabels=self.beta_labels)
write_sgrna_to_file(self.allgenedict, sgrnafile)
plot(genefile)
def bayes_output_no_permutation(self):
os.chdir(self.output_directory)
marks = [['True', 'False'], ['True', 'True'], ['False', 'False'],
['False', 'True'], ["major", "major"]]
#marks=[["major","major"]]
deviation_output = open("%s_mu_k_deviation.txt" % self.output_prefix,
'wb')
deviation_insert = [
"PPI", "outliers_removal", "central_80_percent_mean", "median"
]
deviation_output.write("%s\n" % "\t".join(deviation_insert))
PPI_diagnosis_output = open(
"%s_mu_k_PPI_diagnosis.txt" % self.output_prefix, 'wb')
PPI_diagnosis_insert = [""]
PPI_diagnosis_output.write("%s\n" % "\t".join(PPI_diagnosis_insert))
'''
os.chdir("%s/doc/" %self.output_directory)
DE_file=open("%s_DE.txt" %self.count_table)
DE_file.readline()
DE_dict=dict()
for line in DE_file:
elements=line.strip().split("\t")
DE_dict[elements[0].upper()]=elements[4]
'''
os.chdir(self.output_directory)
for mark in marks:
if mark != ["major", "major"]:
file = "%s_self_bayes_iteration_PPI_%s_outliers_removal_%s.p" % (
self.output_prefix, mark[0], mark[1])
else:
file = "%s_self_bayes_major.p" % self.output_prefix
if file in glob.glob("*"):
self = pickle.load(open(file, 'rb'))
# correct for FDR
gene_fdr_correction(self.allgenedict,
self.adjust_method,
wald_only=True)
genefile = self.output_prefix + '_PPI_' + str(
mark[0]) + '_outliers_removal_' + str(
mark[1]) + '.gene_summary.txt'
sgrnafile = self.output_prefix + '_PPI_' + str(
mark[0]) + '_outliers_removal_' + str(
mark[1]) + '.sgrna_summary.txt'
logging.info('Writing gene results to ' + genefile)
logging.info('Writing sgRNA results to ' + sgrnafile)
write_gene_to_file(self.allgenedict,
genefile,
betalabels=self.beta_labels,
wald_only=True)
#write_sgrna_to_file(self.include_samples,self.allgenedict,self.invalid_gRNA_dict,sgrnafile,DE_dict)
write_sgrna_to_file(self.include_samples, self.allgenedict,
self.invalid_gRNA_dict, sgrnafile)
plot(genefile,
negative_control_gRNAs=self.negative_control_gRNAs,
wald_only=True)
deviation_insert = mark + deviation(self.allgenedict)
deviation_output.write("%s\n" % "\t".join(deviation_insert))
class Mageck_Bayes():
def __init__(self, options):
self.adjust_method=options.adjust_method
self.beta_labels=options.beta_labels
self.count_table=options.count_table
self.design_matrix=options.design_matrix
self.genes_varmodeling=options.genes_varmodeling
self.include_samples=options.include_samples
self.output_prefix=options.output_prefix
self.file_directory=os.getcwd()
self.output_directory="%s/%s" %(os.getcwd(),self.output_prefix)
if os.path.exists(self.output_directory)==False:
os.makedirs(self.output_directory)
self.permutation_round=options.permutation_round
self.outliers_removal=options.outliers_removal
self.PPI_prior=options.PPI_prior
self.PPI_weighting=options.PPI_weighting
self.PPI_diagnosis=None
self.non_PPI_beta_prior_variance=None
self.allgenedict=None
self.size_f=None
self.mrm=None
self.log_list=defaultdict(list)
self.selection_constant=None
self.invalid_gRNA_dict=None
self.negative_control=options.negative_control
self.negative_control_gRNAs=None
self.norm_method=options.norm_method
def bayes_init(self):
maxgene=np.inf
if self.negative_control==None:
self.allgenedict,self.invalid_gRNA_dict=read_gene_from_file(self.count_table,includesamples=self.include_samples,negative_control=self.negative_control)
else:
self.allgenedict,self.invalid_gRNA_dict,self.negative_control_gRNAs=read_gene_from_file(self.count_table,includesamples=self.include_samples,negative_control=self.negative_control)
# calculate the size factor
cttab_sel={}
for (geneid,gk) in self.allgenedict.iteritems():
sgid=gk.sgrnaid
sgreadmat=gk.nb_count.getT().tolist()
for i in range(len(sgid)):
cttab_sel[sgid[i]]=sgreadmat[i]
if hasattr(self,'norm_method'):
if self.norm_method!='none':
self.size_f=normalizeCounts(cttab_sel,method=self.norm_method,returnfactor=True,reversefactor=True,negative_control_gRNAs=self.negative_control_gRNAs)
else:
self.size_f=None
else:
self.size_f=normalizeCounts(cttab_sel,returnfactor=True,reversefactor=True)
logging.info('size factor: '+','.join([str(x) for x in self.size_f]))
#logging.info(self.design_matrix)
desmat=self.design_matrix
for (tgid,tginst) in self.allgenedict.iteritems():
#if tgid=="INO80B":
tginst.design_mat=desmat
iteratenbem(tginst,debug=False,estimateeff=False,alpha_val=0.05,size_factor=self.size_f)
tginst.w_estimate=[]
deviation(self.allgenedict)
self.non_PPI_beta_prior_variance=beta_non_PPI_prior_calculation(self.allgenedict)
pickle.dump(self,open("/%s/%s_self_bayes_init.p" %(self.output_directory,self.output_prefix),'wb'))
def parameter_reevalution(self):
self=pickle.load(open("/%s/%s_self_bayes_init.p" %(self.output_directory,self.output_prefix),'rb'))
logging.info('Reestimate dispersion ...')
ngenes=0
for (tgid,tginst) in self.allgenedict.iteritems():
if ngenes<3000:
try:
#logging.info(tgid)
sgrna_wide_dispersion_estimation_MAP_v2(tginst,self.design_matrix)
ngenes+=1
except:
pass
pickle.dump(self,open("/%s/%s_self_para_eval.p" %(self.output_directory,self.output_prefix),'wb'))
def bayes_fitting(self):
self=pickle.load(open("/%s/%s_self_para_eval.p" %(self.output_directory,self.output_prefix),'rb'))
logging.info('Modeling the mean and variance ...')
if self.genes_varmodeling>0:
self.mrm=MeanVarModel()
#self.mrm.model_mean_disp_by_lm(self.allgenedict)
self.mrm.model_mean_disp_by_glm(self.allgenedict,self.output_prefix,self.size_f)
else:
mrm=None
pickle.dump(self,open("/%s/%s_self_fitting.p" %(self.output_directory,self.output_prefix),'wb'))
def bayes_major(self):
self=pickle.load(open("/%s/%s_self_fitting.p" %(self.output_directory,self.output_prefix),'rb'))
logging.info('Run the algorithm for the second time ...')
ngene=0
for (tgid,tginst) in self.allgenedict.iteritems():
#logging.info(tgid)
iteratenbem(tginst,debug=False,meanvarmodel=self.mrm,restart=False,size_factor=self.size_f,beta1_prior_var=self.non_PPI_beta_prior_variance)
ngene+=1
for (tgid,tginst) in self.allgenedict.iteritems():
if len(tginst.w_estimate)==0:
tginst.w_estimate=np.ones(len(tginst.sgrnaid))
pickle.dump(self,open("/%s/%s_self_bayes_major.p" %(self.output_directory,self.output_prefix),'wb'))
def constant_optimization(self):
# PPI
self=pickle.load(open("/%s/%s_self_bayes_major.p" %(self.output_directory,self.output_prefix),'rb'))
logging.info('PPI validation...')
PPI_main(self)
if self.PPI_diagnosis==True:
beta_prior_output(self)
'''
copy_self=copy.copy(self)
copy_self.count_table="singlized_{}".format(self.count_table)
copy_self.output_prefix="{}_mageck_bayes".format(copy_self.count_table)
copy_self.output_directory="{}/{}".format(os.getcwd(),copy_self.output_prefix)
if os.path.exists(copy_self.output_directory)==False:
os.makedirs(copy_self.output_directory)
if not os.path.isfile("/%s/self_pre_dispersion_fitting_round.p" %copy_self.output_directory):
os.chdir(self.file_directory)
sgRNA_production(self.count_table)
copy_self.allgenedict,copy_self.invalid_gRNA_dict=read_gene_from_file(copy_self.count_table,includesamples=copy_self.include_samples)
for (tgid,tginst) in copy_self.allgenedict.items():
tginst.design_mat=copy_self.design_matrix
iteratenbem(tginst,debug=False,meanvarmodel=self.mrm,restart=False,removeoutliers=self.outliers_removal,size_factor=self.size_f,beta1_prior_var=self.non_PPI_beta_prior_variance)
pickle.dump(copy_self,open("/%s/self_pre_dispersion_fitting_round.p" %copy_self.output_directory,'wb'))
else:
copy_self=pickle.load(open("/%s/self_pre_dispersion_fitting_round.p" %copy_self.output_directory,'rb'))
logging.info("signlized_sgRNA processed!")
sgRNA_beta=background(copy_self)
beta1=list(sgRNA_beta.values())
median=np.median(beta1)
abs_beta1=[abs(i-median) for i in beta1]
abs_beta1.sort()
std_beta1=float(np.percentile(abs_beta1,95))/norm.ppf(0.975)
for geneid in list(self.allgenedict.keys()):
sgrnaid=self.allgenedict[geneid].sgrnaid
temp=[]
for sgRNA in sgrnaid:
temp.append(np.log(norm.pdf(sgRNA_beta[sgRNA],median,std_beta1)))
self.allgenedict[geneid].sgrna_probability=temp
'''
logging.info('Estimate selection constant...')
bayes_selection_constnat_optimization(self)
pickle.dump(self,open("/%s/%s_self_constant_optimization.p" %(self.output_directory,self.output_prefix),'wb'))
def bayes_iteration(self):
logging.info('Iteratioin...')
PPI_choice=[False,True]
for PPI in PPI_choice:
self=pickle.load(open("/%s/%s_self_constant_optimization.p" %(self.output_directory,self.output_prefix),'rb'))
self.PPI_prior=PPI
if self.PPI_diagnosis==False and self.PPI_prior==True:
pass
else:
self.outliers_removal=False
for (tgid,tginst) in self.allgenedict.iteritems():
iteratenbem(tginst,debug=False,meanvarmodel=self.mrm,restart=False,PPI_prior=self.PPI_prior,removeoutliers=self.outliers_removal,size_factor=self.size_f,beta1_prior_var=self.non_PPI_beta_prior_variance)
pickle.dump(self,open("/%s/%s_self_bayes_iteration_PPI_%s_outliers_removal_%s.p" %(self.output_directory,self.output_prefix,self.PPI_prior,self.outliers_removal),'wb'))
#base_index=np.where(~self.design_matrix[:,1:].any(axis=1))[0]
#transform_list=[1 if i in base_index else -1 for i in range(self.design_matrix.shape[0])]
#transform_matrix=np.matrix(transform_list)
for (tgid,tginst) in self.allgenedict.items():
bayes_selection(tginst,log_list=self.log_list,selection_constant=self.selection_constant)
self.outliers_removal=True
for (tgid,tginst) in self.allgenedict.iteritems():
iteratenbem(tginst,debug=False,meanvarmodel=self.mrm,restart=False,PPI_prior=self.PPI_prior,removeoutliers=self.outliers_removal,size_factor=self.size_f,beta1_prior_var=self.non_PPI_beta_prior_variance)
pickle.dump(self,open("/%s/%s_self_bayes_iteration_PPI_%s_outliers_removal_%s.p" %(self.output_directory,self.output_prefix,self.PPI_prior,self.outliers_removal),'wb'))
def bayes_output(self):
os.chdir(self.output_directory)
for mark in [[True,False],[True,True],[False,False],[False,True],["major","major"]]:
if mark!=["major","major"]:
file="%s_self_bayes_iteration_PPI_%s_outliers_removal_%s.p" %(self.output_prefix,mark[0],mark[1])
else:
file="%s_self_bayes_major.p" %self.output_prefix
if file in glob.glob("*"):
self=pickle.load(open(file,'rb'))
# permutation
iteratenbem_permutation(self.allgenedict,nround=self.permutation_round,meanvarmodel=self.mrm,size_factor=self.size_f,beta1_prior_var=self.non_PPI_beta_prior_variance)
# correct for FDR
gene_fdr_correction(self.allgenedict,self.adjust_method)
genefile=self.output_prefix+'_PPI_'+str(mark[0])+'_outliers_removal_'+str(mark[1])+'.gene_summary.txt'
sgrnafile=self.output_prefix+'_PPI_'+str(mark[0])+'_outliers_removal_'+str(mark[1])+'.sgrna_summary.txt'
logging.info(genefile)
logging.info(sgrnafile)
logging.info('Writing gene results to '+genefile)
logging.info('Writing sgRNA results to '+sgrnafile)
write_gene_to_file(self.allgenedict,genefile,betalabels=self.beta_labels)
write_sgrna_to_file(self.allgenedict,sgrnafile)
plot(genefile)
def bayes_output_no_permutation(self):
os.chdir(self.output_directory)
marks=[['True','False'],['True','True'],['False','False'],['False','True'],["major","major"]]
#marks=[["major","major"]]
deviation_output=open("%s_mu_k_deviation.txt" %self.output_prefix,'wb')
deviation_insert=["PPI","outliers_removal","central_80_percent_mean","median"]
deviation_output.write("%s\n" %"\t".join(deviation_insert))
PPI_diagnosis_output=open("%s_mu_k_PPI_diagnosis.txt" %self.output_prefix,'wb')
PPI_diagnosis_insert=[""]
PPI_diagnosis_output.write("%s\n" %"\t".join(PPI_diagnosis_insert))
'''
os.chdir("%s/doc/" %self.output_directory)
DE_file=open("%s_DE.txt" %self.count_table)
DE_file.readline()
DE_dict=dict()
for line in DE_file:
elements=line.strip().split("\t")
DE_dict[elements[0].upper()]=elements[4]
'''
os.chdir(self.output_directory)
for mark in marks:
if mark!=["major","major"]:
file="%s_self_bayes_iteration_PPI_%s_outliers_removal_%s.p" %(self.output_prefix,mark[0],mark[1])
else:
file="%s_self_bayes_major.p" %self.output_prefix
if file in glob.glob("*"):
self=pickle.load(open(file,'rb'))
# correct for FDR
gene_fdr_correction(self.allgenedict,self.adjust_method,wald_only=True)
genefile=self.output_prefix+'_PPI_'+str(mark[0])+'_outliers_removal_'+str(mark[1])+'.gene_summary.txt'
sgrnafile=self.output_prefix+'_PPI_'+str(mark[0])+'_outliers_removal_'+str(mark[1])+'.sgrna_summary.txt'
logging.info('Writing gene results to '+genefile)
logging.info('Writing sgRNA results to '+sgrnafile)
write_gene_to_file(self.allgenedict,genefile,betalabels=self.beta_labels,wald_only=True)
#write_sgrna_to_file(self.include_samples,self.allgenedict,self.invalid_gRNA_dict,sgrnafile,DE_dict)
write_sgrna_to_file(self.include_samples,self.allgenedict,self.invalid_gRNA_dict,sgrnafile)
plot(genefile,negative_control_gRNAs=self.negative_control_gRNAs,wald_only=True)
deviation_insert=mark+deviation(self.allgenedict)
deviation_output.write("%s\n" %"\t".join(deviation_insert))
