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Merging changes from MetaDirectMS1 directms1quantmulti and ms1groups versions #16

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406eb38
adding group column to output file
Oct 4, 2024
d973c06
Fixed bug with OX and dbname groups
Oct 8, 2024
cba124f
fixed .split(".") with replace_label
Oct 9, 2024
3976661
Added saving of proteins table in group_specific.py
PostoenkoVI Jul 8, 2025
107f977
debug saving protein tables
PostoenkoVI Jul 8, 2025
78a172f
Added taxid tax_level columns to output protein table
PostoenkoVI Jul 8, 2025
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30 changes: 17 additions & 13 deletions ms1searchpy/directms1quantmulti.py
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Original file line number Diff line number Diff line change
Expand Up @@ -35,6 +35,7 @@ def run():
parser.add_argument('-d', '-db', help='path to uniprot fasta file used for ms1searchpy', required=True)
parser.add_argument('-samples', help='tsv table with sample details', required=True)
parser.add_argument('-out', help='name of DirectMS1quant output files', default='DQmulti')
parser.add_argument('-out_folder', help='folder to store DirectMS1quant output files, default: folder of input files', default='')
parser.add_argument('-norm', help='LFQ normalization: (1) using median of CONTROL group, default; (2) using median of all groups', default=1, type=int)
parser.add_argument('-proteins_for_figure', help='path to proteins for figure plotting', default='', type=str)
parser.add_argument('-figdir', help='path to output folder for figures', default='')
Expand All @@ -50,9 +51,13 @@ def run():


def process_files(args):

infolder = args['pdir']
ms1folder = args['pdir']
if args['out_folder'] :
out_folder = path.abspath(args['out_folder'])
else :
out_folder = ms1folder
path_to_fasta = args['d']

df1 = pd.read_table(args['samples'], dtype={'group': str, 'condition': str, 'vs': str})
Expand Down Expand Up @@ -82,7 +87,7 @@ def process_files(args):

for fn in listdir(ms1folder):
if fn.endswith('_proteins_full.tsv'):
f_name = fn.replace('.features_proteins_full.tsv', '')
f_name = fn.replace('_proteins_full.tsv', '')
if f_name in f_dict_map:
s_files_dict[f_dict_map[f_name]].append(path.join(ms1folder, fn))

Expand Down Expand Up @@ -116,7 +121,7 @@ def process_files(args):
logger.info('Starting Stage 1: Run pairwise DirectMS1Quant runs...')

for i2, i1_val in all_conditions.items():
out_name = path.join(ms1folder, '%s_directms1quant_out_%s_vs_%s%s' % (outlabel, ''.join(list(i2)), control_label, i1_val))
out_name = path.join(out_folder, '%s_directms1quant_out_%s_vs_%s%s' % (outlabel, ''.join(list(i2)), control_label, i1_val))
dquant_params = copy(dquant_params_base)
dquant_params['S1'] = s_files_dict[(control_label, i1_val)]
dquant_params['S2'] = s_files_dict[i2]
Expand All @@ -132,7 +137,7 @@ def process_files(args):
pep_cnt = Counter()
pep_cnt_up = Counter()
for i2, i1_val in all_conditions.items():
out_name = path.join(ms1folder, '%s_directms1quant_out_%s_vs_%s%s.tsv' % (outlabel, ''.join(list(i2)), control_label, i1_val))
out_name = path.join(out_folder, '%s_directms1quant_out_%s_vs_%s%s.tsv' % (outlabel, ''.join(list(i2)), control_label, i1_val))
# if os.path.isfile(out_name):
df0_full = pd.read_table(out_name.replace('.tsv', '_quant_peptides.tsv'), usecols=['origseq', 'up', 'down', 'proteins'])

Expand Down Expand Up @@ -171,7 +176,7 @@ def process_files(args):
args_local['allowed_proteins'] = ''
for z in listdir(infolder):
if z.endswith(replace_label):
zname = z.split('.')[0]
zname = z.replace(replace_label, '')
if zname in df1_filenames:
args_local['S1'].append(path.join(infolder, z))
names_map[args_local['S1'][-1]] = zname
Expand All @@ -195,7 +200,7 @@ def process_files(args):
all_s_lbls[sample_num].append(label)

all_lbls = all_s_lbls['S1']
out_name = path.join(ms1folder, '%s_peptide_LFQ.tsv' % (outlabel, ))
out_name = path.join(out_folder, '%s_peptide_LFQ.tsv' % (outlabel, ))
df_final = pd.read_table(out_name)

logger.info('Skipping Stage 2: Prepare full LFQ peptide table...')
Expand All @@ -206,7 +211,6 @@ def process_files(args):
sample_num = 'S1'

all_s_lbls[sample_num] = []

for z in args_local[sample_num]:
label = sample_num + '_' + z.replace(replace_label, '')
all_s_lbls[sample_num].append(label)
Expand Down Expand Up @@ -268,7 +272,7 @@ def process_files(args):
df_final = df_final[df_final['nonmissing']]
logger.info('Total number of PFMs: %d', len(df_final))

out_name = path.join(ms1folder, '%s_peptide_LFQ.tsv' % (outlabel, ))
out_name = path.join(out_folder, '%s_peptide_LFQ.tsv' % (outlabel, ))
df_final.to_csv(out_name, sep='\t', index=False)


Expand Down Expand Up @@ -365,7 +369,7 @@ def process_files(args):

origfnames = []
for cc in all_lbls:
origfn = cc.split('/')[-1].split('.')[0]
origfn = path.basename(cc)
origfnames.append(origfn)

dft = pd.DataFrame.from_dict([df_final.groupby('proteins')[cc].median().to_dict() for cc in all_lbls])
Expand All @@ -381,15 +385,15 @@ def process_files(args):
df1x['sample+condition'] = df1x['sample'].apply(lambda x: x + ' ') + df1x['condition']


out_name = path.join(ms1folder, '%s_proteins_LFQ.tsv' % (outlabel, ))
out_namex = path.join(ms1folder, '%s_peptide_LFQ_processed.tsv' % (outlabel, ))
out_name = path.join(out_folder, '%s_proteins_LFQ.tsv' % (outlabel, ))
out_namex = path.join(out_folder, '%s_peptide_LFQ_processed.tsv' % (outlabel, ))
df1.to_csv(out_name, sep='\t', index=False)
df1x.to_csv(out_namex, sep='\t', index=False)

else:

logger.info('Skipping Stage 3: Prepare full LFQ protein table...')
out_name = path.join(ms1folder, '%s_proteins_LFQ.tsv' % (outlabel, ))
out_name = path.join(out_folder, '%s_proteins_LFQ.tsv' % (outlabel, ))
df1 = pd.read_table(out_name)

if args['start_stage'] <= 4:
Expand Down Expand Up @@ -452,7 +456,7 @@ def process_files(args):
if not path.isdir(out_figdir):
makedirs(out_figdir)
else:
out_figdir = infolder
out_figdir = out_folder
plt.savefig(path.join(out_figdir, '%s_%s.png' % (args['out'], gname, )))


Expand Down
55 changes: 32 additions & 23 deletions ms1searchpy/group_specific.py
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Original file line number Diff line number Diff line change
Expand Up @@ -62,31 +62,31 @@ def run():

cnt = Counter(dbname_map.values())

if group_to_use not in ['dbname', 'OX']:
for ox in cnt.keys():
# if group_to_use not in ['dbname', 'OX']:
for ox in cnt.keys():

line = ncbi.get_lineage(ox)
ranks = ncbi.get_rank(line)
if group_to_use not in ranks.values():
logger.warning('%s does not have %s', str(ox), group_to_use)
group_custom = 'OX:' + ox
# print('{} does not have {}'.format(i, group_to_use))
# continue
line = ncbi.get_lineage(ox)
ranks = ncbi.get_rank(line)
if group_to_use not in ranks.values():
logger.warning('%s does not have %s', str(ox), group_to_use)
group_custom = 'OX:' + ox
# print('{} does not have {}'.format(i, group_to_use))
# continue

else:
ranks_rev = {k[1]:k[0] for k in ranks.items()}
# print(ranks_rev)
group_custom = ranks_rev[group_to_use]
else:
ranks_rev = {k[1]:k[0] for k in ranks.items()}
# print(ranks_rev)
group_custom = group_to_use+':'+str(ranks_rev[group_to_use])

ox_map[ox] = group_custom
ox_map[ox] = group_custom


for dbname in list(dbname_map.keys()):
dbname_map[dbname] = ox_map[dbname_map[dbname]]
for dbname in list(dbname_map.keys()):
dbname_map[dbname] = ox_map[dbname_map[dbname]]

cnt = Counter(dbname_map.values())
cnt = Counter(dbname_map.values())

print(cnt.most_common())
logging.debug(cnt.most_common())

# return -1

Expand Down Expand Up @@ -149,9 +149,10 @@ def run():
escore = lambda x: -x[1]
fdr = float(args['fdr']) / 100

# all_proteins = []
all_proteins = []

base_out_name = args['out'] + group_to_use + '.tsv'
proteins_out_name = args['out'] + 'proteins_' + group_to_use + '.tsv'

out_dict = dict()

Expand Down Expand Up @@ -192,18 +193,26 @@ def run():


top_proteins = final_iteration(resdict, mass_diff, rt_diff, pept_prot, protsN_tmp, base_out_name, prefix, isdecoy, isdecoy_key, escore, fdr, args['nproc'], prots_spc_basic2=prots_spc_basic2, output_all=False)
# all_proteins.extend(top_proteins)
tax_level, taxonomy_id = group_name.split(':')
ext_top_proteins = []
for el in top_proteins :
tmp_lst = [elem for elem in el] + [taxonomy_id, tax_level]
ext_top_proteins.append(tmp_lst)
all_proteins.extend(ext_top_proteins)
out_dict[group_name] = len(top_proteins)
# print(top_proteins)
print('\n')
# break

with open(base_out_name, 'w') as output:
output.write('taxid\tproteins\n')
output.write('group\ttaxid\tproteins\n')
for k, v in out_dict.items():
output.write('\t'.join((str(k), str(v))) + '\n')
output.write('\t'.join(map(str, k.split(':')+[v])) + '\n')


with open(proteins_out_name, 'w') as output:
output.write('dbname\tscore\tmatched peptides\ttheoretical peptides\tdecoy\ttaxid\ttax_level\n')
for x in all_proteins:
output.write('\t'.join(x) + '\n')

if __name__ == '__main__':
run()