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fixed some bugs and adapted to TMT #7

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merged 3 commits into from
Mar 6, 2023
Merged

fixed some bugs and adapted to TMT #7

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ce787d0
Merge pull request #1 from bigbio/master
daichengxin Jan 31, 2023
7eddea0
fixed some bugs and adapted to TMT
daichengxin Feb 7, 2023
427e07a
Update peptide_normalization.py
daichengxin Mar 1, 2023
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38 changes: 36 additions & 2 deletions bin/peptide_file_generation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -56,6 +56,16 @@ def get_study_accession(sample_id: str) -> str:
return sample_id.split('-')[0]


def get_reference_name(reference_spectrum: str) -> str:
"""
Get the reference name from Reference column. The function expected a reference name in the following format eg.
20150820_Haura-Pilot-TMT1-bRPLC03-2.mzML_controllerType=0 controllerNumber=1 scan=16340
:param reference_spectrum:
:return: reference name
"""
return re.split(r'\.mzML|\.MZML|\.raw|\.RAW', reference_spectrum)[0]


def get_run_mztab(ms_run: str, metadata: OrderedDict) -> str:
"""
Convert the ms_run into a reference file for merging with msstats output
Expand Down Expand Up @@ -123,7 +133,6 @@ def peptide_file_generation(msstats: str, sdrf: str, compress: bool, output: str

# Read the msstats file
msstats_df = pd.read_csv(msstats, sep=',', compression=compression_method)
msstats_df[REFERENCE] = msstats_df[REFERENCE].apply(remove_extension_file)

msstats_df.rename(
columns={'ProteinName': PROTEIN_NAME, 'PeptideSequence': PEPTIDE_SEQUENCE, 'PrecursorCharge': PEPTIDE_CHARGE,
Expand All @@ -146,7 +155,32 @@ def peptide_file_generation(msstats: str, sdrf: str, compress: bool, output: str
BIOREPLICATE, FRACTION, FRAGMENT_ION, ISOTOPE_LABEL_TYPE]]

# Merged the SDRF with Resulted file
result_df = pd.merge(msstats_df, sdrf_df[['source name', REFERENCE]], how='left', on=[REFERENCE])
labels = set(sdrf_df['comment[label]'])
if CHANNEL not in msstats_df.columns:
msstats_df[REFERENCE] = msstats_df[REFERENCE].apply(remove_extension_file)
result_df = pd.merge(msstats_df, sdrf_df[['source name', REFERENCE]], how='left', on=[REFERENCE])
elif 'TMT' in ','.join(labels) or 'tmt' in ','.join(labels):
if (len(labels) > 11 or "TMT134N" in labels or "TMT133C" in labels
or "TMT133N" in labels or "TMT132C" in labels or "TMT132N" in labels):
choice = TMT16plex
elif len(labels) == 11 or "TMT131C" in labels:
choice = TMT11plex
elif len(labels) > 6:
choice = TMT10plex
else:
choice = TMT6plex
choice = pd.DataFrame.from_dict(choice, orient='index', columns=[CHANNEL]).reset_index().rename(
columns={'index': 'comment[label]'})
sdrf_df = sdrf_df.merge(choice, on='comment[label]', how='left')
msstats_df[REFERENCE] = msstats_df[REFERENCE].apply(get_reference_name)
result_df = pd.merge(msstats_df, sdrf_df[['source name', REFERENCE, CHANNEL]], how='left',
on=[REFERENCE, CHANNEL])
result_df.drop(CHANNEL, axis=1, inplace=True)
result_df.rename(columns={'Charge': PEPTIDE_CHARGE}, inplace=True)
else:
print("Warning: Only support label free and TMT experiment!")
exit(1)

result_df.rename(columns={'source name': SAMPLE_ID}, inplace=True)

result_df[STUDY_ID] = result_df[SAMPLE_ID].apply(get_study_accession)
Expand Down
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