if args.loc_out != '':
if not (os.path.isdir(args.loc_out)):
os.makedirs(args.loc_out)
log.critical("created output folder %r", args.loc_out)
config = configparser.RawConfigParser()
config.read(os.path.join(os.path.dirname(
os.path.realpath(sys.argv[0])), 'moff_setting.properties'))
# just for Galaxy input is possible to use one big input file and a list of raw file.
# the big file must have the result of each raw file and the columns 'Spectrum File' should be availabe
# This option work only with PS report using only --tsv_list and --raw_list
if ( args.tsv_list is not None) and ( args.raw_list is not None) and (len(args.tsv_list)==1) :
data_temp= pd.read_csv(args.tsv_list[0],sep="\t")
if moff.check_ps_input_data(data_temp.columns.tolist(), ast.literal_eval(config.get('moFF', 'ps_default_export_v1'))) == 1:
# split the data input file only if inave more than ONE raw file and tha input file contain identification for more the ONE run
if len(data_temp['Spectrum File'].unique())> 1 and len(args.raw_list) > 1:
output_list_loc=[]
for file in data_temp['Spectrum File'].unique():
data_temp[data_temp['Spectrum File']== file].to_csv(os.path.join(os.path.split(args.tsv_list[0])[0],file.split('.')[0]+ '.txt')
, sep='\t' , index=False )
output_list_loc.append(os.path.join(os.path.split(args.tsv_list[0])[0],file.split('.')[0]+ '.txt') )
if len(args.raw_list) != len(output_list_loc):
exit('-- Number of raw file is different to the number of input sources detectd in your one input file --')
#sort them to be sure about the association between input - raw file
args.raw_list= sorted(args.raw_list)
args.tsv_list= sorted(output_list_loc)
#clean dataset thta I don use anymore
def run_mbr(args):
"""
Macthing Between Run module.
:param args:
:return:
"""
ch = logging.StreamHandler()
ch.setLevel(logging.ERROR)
log.addHandler(ch)
if args.loc_in is None:
# the user uses --inputtsv option
if not (args.loc_out is None):
# if the user use --output_folder the mbr folder will be created there
output_dir = os.path.join(args.loc_out, 'mbr_output')
else:
# if the user does not use --output_folder the mbr folder will be created on moFF path location
output_dir = os.path.join('mbr_output')
print(os.path.abspath(output_dir))
else:
# the user use the --inputF option
if os.path.exists(os.path.join(args.loc_in)):
# if '/' in str(args.loc_in):
output_dir = os.path.join(args.loc_in, 'mbr_output')
else:
exit(
os.path.join(args.loc_in) +
' EXIT input folder path is not well specified --> / missing or wrong path'
)
# if not (os.path.isdir(args.loc_in)):
# exit(str(args.loc_in) + '--> input folder does not exist ! ')
# if str(args.loc_in) == '':
# output_dir = 'mbr_output'
# else:
# if os.path.exists(os.path.join(args.loc_in)):
# if '/' in str(args.loc_in):
# output_dir = os.path.join(args.loc_in, 'mbr_output')
# else:
# exit(os.path.join(args.loc_in) + ' EXIT input folder path not well specified --> / missing ')
if not (os.path.isdir(output_dir)):
log.critical("Created MBR output folder in : %s ",
os.path.abspath(output_dir))
os.makedirs(output_dir)
else:
log.critical("MBR Output folder in : %s ", os.path.abspath(output_dir))
# set log to file
w_mbr = logging.FileHandler(os.path.join(output_dir,
args.log_label + '_mbr_.log'),
mode='w')
w_mbr.setLevel(logging.INFO)
log.addHandler(w_mbr)
moff_path = os.path.dirname(os.path.realpath(sys.argv[0]))
config = configparser.RawConfigParser()
config.read(os.path.join(moff_path, 'moff_setting.properties'))
# it s always placed in same folder of moff_mbr.py
# read input
# comment better
# name of the input file
exp_set = []
# list of the input dataframe
exp_t = []
# list of the output dataframe
exp_out = []
# lsit of input datafra used as help
exp_subset = []
# list of the name of the mbr output
exp_out_name = []
if args.loc_in is None:
for id_name in args.tsv_list:
exp_set.append(id_name)
else:
for item in os.listdir(args.loc_in):
if os.path.isfile(os.path.join(args.loc_in, item)):
if os.path.join(args.loc_in, item).endswith('.' + args.ext):
log.critical(item)
exp_set.append(os.path.join(args.loc_in, item))
# sample optiion is valid only if folder iin option is valid
if (args.sample is not None) and (args.loc_in is not None):
exp_set_app = copy.deepcopy(exp_set)
for a in exp_set:
if re.search(args.sample, a) is None:
exp_set_app.remove(a)
exp_set = exp_set_app
if (exp_set == []) or (len(exp_set) == 1):
exit(
'ERROR input files not found or just one input file selected . check the folder or the extension given in input'
)
for a in exp_set:
log.critical('Reading file: %s ', a)
exp_subset.append(a)
data_moff = pd.read_csv(a, sep="\t", header=0)
list_name = data_moff.columns.values.tolist()
# get the lists of PS defaultcolumns from properties file
list_ps_def = ast.literal_eval(
config.get('moFF', 'ps_default_export_v1'))
# here it controls if the input file is a PS export; if yes it maps the input in right moFF name
if moff.check_ps_input_data(list_name, list_ps_def) == 1:
log.critical(
'Detected input file from PeptideShaker export..: %s ', a)
# map the columns name according to moFF input requirements
data_moff, list_name = moff.map_ps2moff(data_moff,
'col_must_have_mbr')
log.critical(
'Mapping columns names into the the moFF requested column name..: %s ',
a)
# print data_moff.columns
if moff.check_columns_name(
list_name,
ast.literal_eval(config.get('moFF', 'col_must_have_mbr')),
log) == 1:
exit('ERROR minimal field requested are missing or wrong')
data_moff['matched'] = 0
data_moff['mass'] = data_moff['mass'].map('{:.4f}'.format)
data_moff['code_unique'] = data_moff['mod_peptide'].astype(
str) # + '_' + data_moff['mass'].astype(str)
data_moff = data_moff.sort_values(by='rt')
exp_t.append(data_moff)
exp_out.append(data_moff)
log.critical('Read input --> done ')
# parameter of the number of query
# set a list of filed mandatory
# ['matched','peptide','mass','mz','charge','prot','rt']
n_replicates = len(exp_t)
exp_set = exp_subset
aa = range(0, n_replicates)
out = list(itertools.product(aa, repeat=2))
# just to save all the model
# add matched columns
list_name.append('matched')
# final status -1 if one of the output is empty
out_flag = 1
# input of the methods
diff_field = np.setdiff1d(exp_t[0].columns, [
'matched', 'mod_peptide', 'peptide', 'mass', 'mz', 'charge', 'prot',
'rt'
])
log.info('Outlier Filtering is %s ',
'active' if args.out_flag else 'not active')
log.info('Number of replicates %i,', n_replicates)
log.info('Pairwise model computation ----')
if args.rt_feat_file is not None:
log.critical('Custom list of peptide used provided by the user in %s',
args.rt_feat_file)
# log.info('Custom list of peptide used provided by the user in %s', args.rt_feat_file)
shared_pep_list = pd.read_csv(args.rt_feat_file, sep='\t')
shared_pep_list['mass'] = shared_pep_list['mass'].map('{:.4f}'.format)
shared_pep_list['code'] = shared_pep_list['peptide'].astype(
str) + '_' + shared_pep_list['mass'].astype(str)
list_shared_pep = shared_pep_list['code']
log.info('Custom list of peptide contains %i ',
list_shared_pep.shape[0])
for jj in aa:
# list of the model saved
model_save = []
# list of the error in min/or sec
model_err = []
# list of the status of the model -1 means model not available for low points in the training set
model_status = []
c_rt = 0
pre_pep_save = []
log.info('matching in %s', exp_set[jj])
result = itertools.filterfalse(lambda x: x[0] != jj or x[1] == jj, out)
for i in result:
#if i[0] == jj and i[1] != jj:
if args.rt_feat_file is not None:
# use of custom peptide
comA = exp_t[i[0]][exp_t[i[0]]['code_unique'].isin(
list_shared_pep)][['code_unique', 'peptide', 'prot', 'rt']]
comB = exp_t[i[1]][exp_t[i[1]]['code_unique'].isin(
list_shared_pep)][['code_unique', 'peptide', 'prot', 'rt']]
comA = comA.groupby('code_unique', as_index=False).mean()
comB = comB.groupby('code_unique', as_index=False).mean()
common = pd.merge(comA, comB, on=['code_unique'], how='inner')
else:
# use of shared peptdes.
log.info(' Matching %s peptide in searching in %s ',
exp_set[i[0]], exp_set[i[1]])
list_pep_repA = exp_t[i[0]]['code_unique'].unique()
list_pep_repB = exp_t[i[1]]['code_unique'].unique()
log.info('Peptide unique (mass + sequence) %i , %i ',
list_pep_repA.shape[0], list_pep_repB.shape[0])
set_dif_s_in_1 = np.setdiff1d(list_pep_repB, list_pep_repA)
add_pep_frame = exp_t[i[1]][exp_t[i[1]]['code_unique'].isin(
set_dif_s_in_1)].copy()
#-- prepare the testing set
add_pep_frame = add_pep_frame[[
'peptide', 'mod_peptide', 'mass', 'mz', 'charge', 'prot',
'rt'
]]
# add_pep_frame['code_unique'] = '_'.join([add_pep_frame['peptide'], add_pep_frame['prot'], add_pep_frame['mass'].astype(str), add_pep_frame['charge'].astype(str)])
add_pep_frame['code_unique'] = add_pep_frame['mod_peptide'] + '_' + \
add_pep_frame['prot'] + '_' + '_' + \
add_pep_frame['charge'].astype(str)
add_pep_frame = add_pep_frame.groupby(
'code_unique', as_index=False)['peptide', 'mod_peptide',
'mass', 'charge', 'mz',
'prot', 'rt'].aggregate(max)
add_pep_frame = add_pep_frame[[
'code_unique', 'peptide', 'mod_peptide', 'mass', 'mz',
'charge', 'prot', 'rt'
]]
list_name = add_pep_frame.columns.tolist()
list_name = [
w.replace('rt', 'rt_' + str(c_rt)) for w in list_name
]
add_pep_frame.columns = list_name
pre_pep_save.append(add_pep_frame)
c_rt += 1
#--------
pep_shared = np.intersect1d(list_pep_repA, list_pep_repB)
log.info(' Peptide (mass + sequence) added size %i ',
add_pep_frame.shape[0])
log.info(' Peptide (mass + sequence) )shared %i ',
pep_shared.shape[0])
comA = exp_t[i[0]][exp_t[i[0]]['code_unique'].isin(
pep_shared)][['code_unique', 'peptide', 'prot', 'rt']]
comB = exp_t[i[1]][exp_t[i[1]]['code_unique'].isin(
pep_shared)][['code_unique', 'peptide', 'prot', 'rt']]
# filtering using the variance added 17_08
flag_var_filt = False
if flag_var_filt:
dd = comA.groupby('code_unique', as_index=False)
top_res = dd.agg(['std', 'mean', 'count'])
# print np.nanpercentile(top_res['rt']['std'].values,[5,10,20,30,50,60,80,90,95,97,99,100])
th = np.nanpercentile(top_res['rt']['std'].values, 60)
comA = comA[~comA['code_unique'].isin(top_res[
top_res['rt']['std'] > th].index)]
# data B '
dd = comB.groupby('code_unique', as_index=False)
top_res = dd.agg(['std', 'mean', 'count'])
# print comB.shape
# print np.nanpercentile(top_res['rt']['std'].values,[5,10,20,30,50,60,80,90,95,97,99,100])
th = np.nanpercentile(top_res['rt']['std'].values, 60)
comB = comB[~comB['code_unique'].isin(top_res[
top_res['rt']['std'] > th].index)]
comA = comA.groupby('code_unique', as_index=False).mean()
comB = comB.groupby('code_unique', as_index=False).mean()
common = pd.merge(comA, comB, on=['code_unique'], how='inner')
if common.shape[0] <= 10 and args.rt_feat_file is not None:
model_status.append(-1)
continue
# filtering outlier option
else:
if args.out_flag:
filt_x, filt_y, pos_out = MD_removeOutliers(
common['rt_y'].values, common['rt_x'].values,
args.w_filt)
data_B = filt_x
data_A = filt_y
data_B = np.reshape(data_B, [filt_x.shape[0], 1])
data_A = np.reshape(data_A, [filt_y.shape[0], 1])
log.info('Outlier founded %i w.r.t %i', pos_out.shape[0],
common['rt_y'].shape[0])
else:
data_B = common['rt_y'].values
data_A = common['rt_x'].values
data_B = np.reshape(data_B, [common.shape[0], 1])
data_A = np.reshape(data_A, [common.shape[0], 1])
log.info(' Size trainig shared peptide , %i %i ',
data_A.shape[0], data_B.shape[0])
clf = linear_model.RidgeCV(alphas=np.power(
2, np.linspace(-30, 30)),
scoring='neg_mean_absolute_error')
clf.fit(data_B, data_A)
clf_final = linear_model.Ridge(alpha=clf.alpha_)
clf_final.fit(data_B, data_A)
# save the model
model_save.append(clf_final)
model_err.append(
mean_absolute_error(data_A, clf_final.predict(data_B)))
log.info(
' Mean absolute error training : %4.4f sec',
mean_absolute_error(data_A, clf_final.predict(data_B)))
model_status.append(1)
'''
# GP version
model_gp, predicted_train, error = train_gp(data_A, data_B,c= str(i[0])+'_'+str(i[1]))
#print i[1], comA.shape, error
model_err.append(error)
model_save.append(model_gp)
model_status.append(1)
'''
if np.where(np.array(model_status) == -1)[0].shape[0] >= (len(aa) / 2):
log.error(
'MBR aborted : mbr cannnot be run, not enough shared pepetide among the replicates '
)
exit(
'ERROR : mbr cannnot be run, not enough shared pepetide among the replicates'
)
log.info('Combination of the model --------')
log.info('Weighted combination %s : ',
'Weighted' if args.w_comb else 'Unweighted')
if n_replicates == 2:
test = pre_pep_save[0]
else:
test = reduce(
lambda left, right: pd.merge(
left,
right,
on=[
'code_unique', 'peptide', 'mod_peptide', 'mass', 'mz',
'charge', 'prot'
],
how='outer'), pre_pep_save)
test = test.groupby('code_unique', as_index=False).aggregate(max)
test.drop('code_unique', axis=1, inplace=True)
test['time_pred'] = test.iloc[:, 6:(6 + (n_replicates - 1))].apply(
lambda x: combine_model(x, model_save, model_err, args.w_comb),
axis=1)
#test['time_pred'] = test.iloc[:, 6: (6 + (n_replicates - 1))].apply(
# lambda x: combine_model(x, model_save[(jj * (n_replicates - 1)):((jj + 1) * (n_replicates - 1))],
# model_err[(jj * (n_replicates - 1)):((jj + 1) * (n_replicates - 1))], args.w_comb),
# axis=1)
test['matched'] = 1
# still to check better
if test[test['time_pred'] <= 0].shape[0] >= 1:
log.info(
' -- Predicted negative RT: those peptide will be deleted')
test = test[test['time_pred'] > 0]
list_name = test.columns.tolist()
list_name = [w.replace('time_pred', 'rt') for w in list_name]
test.columns = list_name
# test = test[['peptide','mod_peptide', 'mass', 'mz', 'charge',
# 'prot', 'rt', 'matched','uncertainty_win']]
test = test[[
'peptide', 'mod_peptide', 'mass', 'mz', 'charge', 'prot', 'rt',
'matched'
]]
# just put nan with the missing values
for field in diff_field.tolist():
test[field] = np.nan
log.info('Before adding %s contains %i ', exp_set[jj],
exp_t[jj].shape[0])
exp_out[jj] = pd.concat([exp_t[jj], test],
join='outer',
axis=0,
sort=False)
log.info('After MBR %s contains: %i peptides', exp_set[jj],
exp_out[jj].shape[0])
log.critical('matched features %i MS2 features %i ',
exp_out[jj][exp_out[jj]['matched'] == 1].shape[0],
exp_out[jj][exp_out[jj]['matched'] == 0].shape[0])
exp_out[jj].to_csv(path_or_buf=os.path.join(
output_dir,
os.path.split(exp_set[jj])[1].split('.')[0] + '_match.txt'),
sep='\t',
index=False)
exp_out_name.append(
os.path.join(
output_dir,
os.path.split(exp_set[jj])[1].split('.')[0] + '_match.txt'))
if exp_out[jj].shape[0] > 0:
out_flag = 1 * out_flag
else:
out_flag = -1 * out_flag
w_mbr.close()
log.removeHandler(w_mbr)
return out_flag, exp_out_name
def run_mbr(args):
"""
Macthing Between Run module.
:param args:
:return:
"""
ch = logging.StreamHandler()
ch.setLevel(logging.ERROR)
log.addHandler(ch)
if args.loc_in is None:
# the user uses --inputtsv option
if not (args.loc_out is None):
# if the user use --output_folder the mbr folder will be created there
output_dir = os.path.join(args.loc_out, 'mbr_output')
else:
# if the user does not use --output_folder the mbr folder will be created on moFF path location
output_dir = os.path.join('mbr_output')
print(os.path.abspath(output_dir))
else:
# the user use the --inputF option
if os.path.exists(os.path.join(args.loc_in)):
# if '/' in str(args.loc_in):
output_dir = os.path.join(args.loc_in, 'mbr_output')
else:
exit(os.path.join(args.loc_in) +
' EXIT input folder path is not well specified --> / missing or wrong path')
# if not (os.path.isdir(args.loc_in)):
# exit(str(args.loc_in) + '--> input folder does not exist ! ')
# if str(args.loc_in) == '':
# output_dir = 'mbr_output'
# else:
# if os.path.exists(os.path.join(args.loc_in)):
# if '/' in str(args.loc_in):
# output_dir = os.path.join(args.loc_in, 'mbr_output')
# else:
# exit(os.path.join(args.loc_in) + ' EXIT input folder path not well specified --> / missing ')
if not (os.path.isdir(output_dir)):
log.critical("Created MBR output folder in : %s ",
os.path.abspath(output_dir))
os.makedirs(output_dir)
else:
log.critical("MBR Output folder in : %s ", os.path.abspath(output_dir))
# set log to file
w_mbr = logging.FileHandler(os.path.join(
output_dir, args.log_label + '_mbr_.log'), mode='w')
w_mbr.setLevel(logging.INFO)
log.addHandler(w_mbr)
moff_path = os.path.dirname(os.path.realpath(sys.argv[0]))
config = configparser.RawConfigParser()
config.read(os.path.join(moff_path, 'moff_setting.properties'))
# it s always placed in same folder of moff_mbr.py
# read input
# comment better
# name of the input file
exp_set = []
# list of the input dataframe
exp_t = []
# list of the output dataframe
exp_out = []
# lsit of input datafra used as help
exp_subset = []
# list of the name of the mbr output
exp_out_name = []
if args.loc_in is None:
for id_name in args.tsv_list:
exp_set.append(id_name)
else:
for item in os.listdir(args.loc_in):
if os.path.isfile(os.path.join(args.loc_in, item)):
if os.path.join(args.loc_in, item).endswith('.' + args.ext):
log.critical(item)
exp_set.append(os.path.join(args.loc_in, item))
# sample optiion is valid only if folder iin option is valid
if (args.sample is not None) and (args.loc_in is not None):
exp_set_app = copy.deepcopy(exp_set)
for a in exp_set:
if re.search(args.sample, a) is None:
exp_set_app.remove(a)
exp_set = exp_set_app
if (exp_set == []) or (len(exp_set) == 1):
exit(
'ERROR input files not found or just one input file selected . check the folder or the extension given in input')
for a in exp_set:
log.critical('Reading file: %s ', a)
exp_subset.append(a)
data_moff = pd.read_csv(a, sep="\t", header=0)
list_name = data_moff.columns.values.tolist()
# get the lists of PS defaultcolumns from properties file
list_ps_def = ast.literal_eval(
config.get('moFF', 'ps_default_export_v1'))
# here it controls if the input file is a PS export; if yes it maps the input in right moFF name
if moff.check_ps_input_data(list_name, list_ps_def) == 1:
log.critical(
'Detected input file from PeptideShaker export..: %s ', a)
# map the columns name according to moFF input requirements
data_moff, list_name = moff.map_ps2moff(
data_moff, 'col_must_have_mbr')
log.critical(
'Mapping columns names into the the moFF requested column name..: %s ', a)
# print data_moff.columns
if moff.check_columns_name(list_name, ast.literal_eval(config.get('moFF', 'col_must_have_mbr')), log) == 1:
exit('ERROR minimal field requested are missing or wrong')
data_moff['matched'] = 0
data_moff['mass'] = data_moff['mass'].map('{:.4f}'.format)
data_moff['code_unique'] = data_moff['mod_peptide'].astype(
str) # + '_' + data_moff['mass'].astype(str)
data_moff = data_moff.sort_values(by='rt')
exp_t.append(data_moff)
exp_out.append(data_moff)
log.critical('Read input --> done ')
# parameter of the number of query
# set a list of filed mandatory
# ['matched','peptide','mass','mz','charge','prot','rt']
n_replicates = len(exp_t)
exp_set = exp_subset
aa = range(0, n_replicates)
out = list(itertools.product(aa, repeat=2))
# just to save all the model
# add matched columns
list_name.append('matched')
# final status -1 if one of the output is empty
out_flag = 1
# input of the methods
diff_field = np.setdiff1d(exp_t[0].columns, [
'matched', 'mod_peptide', 'peptide', 'mass', 'mz', 'charge', 'prot', 'rt'])
log.info('Outlier Filtering is %s ', 'active' if
args.out_flag else 'not active')
log.info('Number of replicates %i,', n_replicates)
log.info('Pairwise model computation ----')
if args.rt_feat_file is not None:
log.critical(
'Custom list of peptide used provided by the user in %s', args.rt_feat_file)
# log.info('Custom list of peptide used provided by the user in %s', args.rt_feat_file)
shared_pep_list = pd.read_csv(args.rt_feat_file, sep='\t')
shared_pep_list['mass'] = shared_pep_list['mass'].map('{:.4f}'.format)
shared_pep_list['code'] = shared_pep_list['peptide'].astype(
str) + '_' + shared_pep_list['mass'].astype(str)
list_shared_pep = shared_pep_list['code']
log.info('Custom list of peptide contains %i ',
list_shared_pep.shape[0])
for jj in aa:
# list of the model saved
model_save = []
# list of the error in min/or sec
model_err = []
# list of the status of the model -1 means model not available for low points in the training set
model_status = []
c_rt = 0
pre_pep_save = []
log.info('matching in %s', exp_set[jj])
result = itertools.filterfalse(lambda x: x[0] != jj or x[1] == jj, out)
for i in result:
#if i[0] == jj and i[1] != jj:
if args.rt_feat_file is not None:
# use of custom peptide
comA = exp_t[i[0]][exp_t[i[0]]['code_unique'].isin(list_shared_pep)][
['code_unique', 'peptide', 'prot', 'rt']]
comB = exp_t[i[1]][exp_t[i[1]]['code_unique'].isin(list_shared_pep)][
['code_unique', 'peptide', 'prot', 'rt']]
comA = comA.groupby('code_unique', as_index=False).mean()
comB = comB.groupby('code_unique', as_index=False).mean()
common = pd.merge(
comA, comB, on=['code_unique'], how='inner')
else:
# use of shared peptdes.
log.info(' Matching %s peptide in searching in %s ',
exp_set[i[0]], exp_set[i[1]])
list_pep_repA = exp_t[i[0]]['code_unique'].unique()
list_pep_repB = exp_t[i[1]]['code_unique'].unique()
log.info('Peptide unique (mass + sequence) %i , %i ',
list_pep_repA.shape[0],
list_pep_repB.shape[0])
set_dif_s_in_1 = np.setdiff1d(list_pep_repB, list_pep_repA)
add_pep_frame = exp_t[i[1]][exp_t[i[1]]['code_unique'].isin(
set_dif_s_in_1)].copy()
#-- prepare the testing set
add_pep_frame = add_pep_frame[[
'peptide', 'mod_peptide', 'mass', 'mz', 'charge', 'prot', 'rt']]
# add_pep_frame['code_unique'] = '_'.join([add_pep_frame['peptide'], add_pep_frame['prot'], add_pep_frame['mass'].astype(str), add_pep_frame['charge'].astype(str)])
add_pep_frame['code_unique'] = add_pep_frame['mod_peptide'] + '_' + \
add_pep_frame['prot'] + '_' + '_' + \
add_pep_frame['charge'].astype(str)
add_pep_frame = add_pep_frame.groupby('code_unique', as_index=False)[
'peptide', 'mod_peptide', 'mass', 'charge', 'mz', 'prot', 'rt'].aggregate(max)
add_pep_frame = add_pep_frame[[
'code_unique', 'peptide', 'mod_peptide', 'mass', 'mz', 'charge', 'prot', 'rt']]
list_name = add_pep_frame.columns.tolist()
list_name = [w.replace('rt', 'rt_' + str(c_rt))
for w in list_name]
add_pep_frame.columns = list_name
pre_pep_save.append(add_pep_frame)
c_rt += 1
#--------
pep_shared = np.intersect1d(list_pep_repA, list_pep_repB)
log.info(
' Peptide (mass + sequence) added size %i ', add_pep_frame.shape[0])
log.info(' Peptide (mass + sequence) )shared %i ',
pep_shared.shape[0])
comA = exp_t[i[0]][exp_t[i[0]]['code_unique'].isin(pep_shared)][
['code_unique', 'peptide', 'prot', 'rt']]
comB = exp_t[i[1]][exp_t[i[1]]['code_unique'].isin(pep_shared)][
['code_unique', 'peptide', 'prot', 'rt']]
# filtering using the variance added 17_08
flag_var_filt = False
if flag_var_filt:
dd = comA.groupby('code_unique', as_index=False)
top_res = dd.agg(['std', 'mean', 'count'])
# print np.nanpercentile(top_res['rt']['std'].values,[5,10,20,30,50,60,80,90,95,97,99,100])
th = np.nanpercentile(top_res['rt']['std'].values, 60)
comA = comA[~ comA['code_unique'].isin(
top_res[top_res['rt']['std'] > th].index)]
# data B '
dd = comB.groupby('code_unique', as_index=False)
top_res = dd.agg(['std', 'mean', 'count'])
# print comB.shape
# print np.nanpercentile(top_res['rt']['std'].values,[5,10,20,30,50,60,80,90,95,97,99,100])
th = np.nanpercentile(top_res['rt']['std'].values, 60)
comB = comB[~ comB['code_unique'].isin(
top_res[top_res['rt']['std'] > th].index)]
comA = comA.groupby('code_unique', as_index=False).mean()
comB = comB.groupby('code_unique', as_index=False).mean()
common = pd.merge(
comA, comB, on=['code_unique'], how='inner')
if common.shape[0] <= 10 and args.rt_feat_file is not None:
model_status.append(-1)
continue
# filtering outlier option
else:
if args.out_flag :
filt_x, filt_y, pos_out = MD_removeOutliers(common['rt_y'].values, common['rt_x'].values,
args.w_filt)
data_B = filt_x
data_A = filt_y
data_B = np.reshape(data_B, [filt_x.shape[0], 1])
data_A = np.reshape(data_A, [filt_y.shape[0], 1])
log.info('Outlier founded %i w.r.t %i',
pos_out.shape[0], common['rt_y'].shape[0])
else:
data_B = common['rt_y'].values
data_A = common['rt_x'].values
data_B = np.reshape(data_B, [common.shape[0], 1])
data_A = np.reshape(data_A, [common.shape[0], 1])
log.info(' Size trainig shared peptide , %i %i ',
data_A.shape[0], data_B.shape[0])
clf = linear_model.RidgeCV(alphas=np.power(
2, np.linspace(-30, 30)), scoring='neg_mean_absolute_error')
clf.fit(data_B, data_A)
clf_final = linear_model.Ridge(alpha=clf.alpha_)
clf_final.fit(data_B, data_A)
# save the model
model_save.append(clf_final)
model_err.append(mean_absolute_error(
data_A, clf_final.predict(data_B)))
log.info(' Mean absolute error training : %4.4f sec',
mean_absolute_error(data_A, clf_final.predict(data_B)))
model_status.append(1)
'''
# GP version
model_gp, predicted_train, error = train_gp(data_A, data_B,c= str(i[0])+'_'+str(i[1]))
#print i[1], comA.shape, error
model_err.append(error)
model_save.append(model_gp)
model_status.append(1)
'''
if np.where(np.array(model_status) == -1)[0].shape[0] >= (len(aa) / 2):
log.error(
'MBR aborted : mbr cannnot be run, not enough shared pepetide among the replicates ')
exit('ERROR : mbr cannnot be run, not enough shared pepetide among the replicates')
log.info('Combination of the model --------')
log.info('Weighted combination %s : ', 'Weighted' if
args.w_comb else 'Unweighted')
if n_replicates == 2:
test = pre_pep_save[0]
else:
test = reduce(
lambda left, right: pd.merge(left, right, on=[
'code_unique', 'peptide', 'mod_peptide', 'mass', 'mz', 'charge', 'prot'], how='outer'),
pre_pep_save)
test = test.groupby('code_unique', as_index=False).aggregate(max)
test.drop('code_unique', axis=1, inplace=True)
test['time_pred'] = test.iloc[:, 6: (6 + (n_replicates - 1))].apply(
lambda x: combine_model(x, model_save, model_err, args.w_comb),axis=1)
#test['time_pred'] = test.iloc[:, 6: (6 + (n_replicates - 1))].apply(
# lambda x: combine_model(x, model_save[(jj * (n_replicates - 1)):((jj + 1) * (n_replicates - 1))],
# model_err[(jj * (n_replicates - 1)):((jj + 1) * (n_replicates - 1))], args.w_comb),
# axis=1)
test['matched'] = 1
# still to check better
if test[test['time_pred'] <= 0].shape[0] >= 1:
log.info(' -- Predicted negative RT: those peptide will be deleted')
test = test[test['time_pred'] > 0]
list_name = test.columns.tolist()
list_name = [w.replace('time_pred', 'rt') for w in list_name]
test.columns = list_name
# test = test[['peptide','mod_peptide', 'mass', 'mz', 'charge',
# 'prot', 'rt', 'matched','uncertainty_win']]
test = test[['peptide', 'mod_peptide', 'mass',
'mz', 'charge', 'prot', 'rt', 'matched']]
# just put nan with the missing values
for field in diff_field.tolist():
test[field] = np.nan
log.info('Before adding %s contains %i ',
exp_set[jj], exp_t[jj].shape[0])
exp_out[jj] = pd.concat(
[exp_t[jj], test], join='outer', axis=0, sort=False)
log.info('After MBR %s contains: %i peptides',
exp_set[jj], exp_out[jj].shape[0])
log.critical('matched features %i MS2 features %i ', exp_out[jj][exp_out[jj]['matched'] == 1].shape[0],
exp_out[jj][exp_out[jj]['matched'] == 0].shape[0])
exp_out[jj].to_csv(
path_or_buf=os.path.join(output_dir, os.path.split(exp_set[jj])[1].split('.')[0] + '_match.txt'), sep='\t',
index=False)
exp_out_name.append(os.path.join(output_dir, os.path.split(
exp_set[jj])[1].split('.')[0] + '_match.txt'))
if exp_out[jj].shape[0] > 0:
out_flag = 1 * out_flag
else:
out_flag = -1 * out_flag
w_mbr.close()
log.removeHandler(w_mbr)
return out_flag, exp_out_name
else:
raw_list = None
loc_raw = args.raw_repo if not None else raw_list
loc_output = args.loc_out
config = configparser.RawConfigParser()
config.read(
os.path.join(os.path.dirname(os.path.realpath(sys.argv[0])),
'moff_setting.properties'))
df = pd.read_csv(file_name, sep="\t")
# add same safety checks len > 1
# Flag for pride pipeline, or to set from second to minute as input rt time scale
moff_pride_flag = False
if moff.check_ps_input_data(
df.columns.tolist(),
ast.literal_eval(config.get('moFF', 'moffpride_format'))) == 1:
# if it is a moff_pride data I do not check aany other requirement
log.critical('moffPride input detected')
moff_pride_flag = True
else:
if not 'matched' in df.columns:
# check if it is a PS file ,
list_name = df.columns.values.tolist()
# get the lists of PS defaultcolumns from properties file
list = ast.literal_eval(
config.get('moFF', 'ps_default_export_v1'))
# here it controls if the input file is a PS export; if yes it maps the input in right moFF name
if moff.check_ps_input_data(list_name, list) == 1:
# map the columns name according to moFF input requirements
if not args.peptide_summary: