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"""
Filter and combine various peptide/MHC datasets to derive a composite training set,
optionally including eluted peptides identified by mass-spec.
"""
import sys
import argparse
import os
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import pandas
import mhcnames
def normalize_allele_name(s):
try:
return mhcnames.normalize_allele_name(s)
except Exception:
return "UNKNOWN"
parser = argparse.ArgumentParser(usage=__doc__)
parser.add_argument(
"--item",
nargs="+",
action="append",
metavar="PMID FILE, ... FILE",
default=[],
help="Item to curate: PMID and list of files")
parser.add_argument(
"--out",
metavar="OUT.csv",
help="Out file path")
parser.add_argument(
"--debug",
action="store_true",
default=False,
help="Leave user in pdb if PMID is unsupported")
HANDLERS = {}
def load(filenames, **kwargs):
result = {}
for filename in filenames:
if filename.endswith(".csv"):
result[filename] = pandas.read_csv(filename, **kwargs)
elif filename.endswith(".xlsx") or filename.endswith(".xls"):
result[filename] = pandas.read_excel(filename, **kwargs)
else:
result[filename] = filename
return result
def debug(*filenames):
loaded = load(filenames)
import ipdb
ipdb.set_trace()
def handle_pmid_27600516(filename):
df = pandas.read_csv(filename)
sample_to_peptides = {}
current_sample = None
for peptide in df.peptide:
if peptide.startswith("#"):
current_sample = peptide[1:]
sample_to_peptides[current_sample] = []
else:
assert current_sample is not None
sample_to_peptides[current_sample].append(peptide.strip().upper())
rows = []
for (sample, peptides) in sample_to_peptides.items():
for peptide in sorted(set(peptides)):
rows.append([sample, peptide])
result = pandas.DataFrame(rows, columns=["sample_id", "peptide"])
result["sample_type"] = "melanoma_cell_line"
return result
def handle_pmid_23481700(filename):
df = pandas.read_excel(filename)
peptides = df.iloc[10:,0].values
assert peptides[0] == "TPSLVKSTSQL"
assert peptides[-1] == "LPHSVNSKL"
result = pandas.DataFrame({
"peptide": peptides,
})
result["sample_id"] = "23481700"
result["sample_type"] = "B-LCL"
return result
def handle_pmid_24616531(filename):
df = pandas.read_excel(filename, sheetname="EThcD")
peptides = df.Sequence.values
assert peptides[0] == "APFLRIAF"
assert peptides[-1] == "WRQAGLSYIRYSQI"
result = pandas.DataFrame({
"peptide": peptides,
})
result["sample_id"] = "24616531"
result["sample_type"] = "B-lymphoblastoid"
result["cell_line"] = "GR"
result["pulldown_antibody"] = "W6/32"
# Note: this publication lists hla as "HLA-A*01,-03, B*07,-27, and -C*02,-07"
# we are guessing the exact 4 digit alleles based on this.
result["hla"] = "HLA-A*01:01 HLA-A*03:01 HLA-B*07:02 HLA-B*27:05 HLA-C*02:02 HLA-C*07:01"
return result
def handle_pmid_25576301(filename):
df = pandas.read_excel(filename, sheetname="Peptides")
assert peptides[0] == "AAAAAAAQSVY"
assert peptides[-1] == "YYYNGKAVY"
column_to_sample = {}
for s in [c for c in df if c.startswith("Intensity ")]:
assert s[-2] == "-"
column_to_sample[s] = s.replace("Intensity ", "")[:-2].strip()
intensity_columns = list(column_to_sample)
rows = []
for _, row in df.iterrows():
x1 = row[intensity_columns]
x2 = x1[x1 > 0].index.map(column_to_sample).value_counts()
x3 = x2[x2 >= 2] # require at least two replicates for each peptide
for sample in x3.index:
rows.append((row.Sequence, sample))
result = pandas.DataFrame(rows, columns=["peptide", "sample_id"])
result["cell_line"] = ""
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allele_map = {
'Fib': "HLA-A*03:01 HLA-A*23:01 HLA-B*08:01 HLA-B*15:18 HLA-C*07:02 HLA-C*07:04",
'HCC1937': "HLA-A*23:01 HLA-A*24:02 HLA-B*07:02 HLA-B*40:01 HLA-C*03:04 HLA-C*07:02",
'SupB15WT': None, # four digit alleles unknown, will drop sample
'SupB15RT': None,
'HCT116': "HLA-A*01:01 HLA-A*02:01 HLA-B*45:01 HLA-B*18:01 HLA-C*05:01 HLA-C*07:01",
# Homozygous at HLA-A:
'HCC1143': "HLA-A*31:01 HLA-A*31:01 HLA-B*35:08 HLA-B*37:01 HLA-C*04:01 HLA-C*06:02",
# Homozygous everywhere:
'JY': "HLA-A*02:01 HLA-A*02:01 HLA-B*07:02 HLA-B*07:02 HLA-C*07:02 HLA-C*07:02",
}
sample_type = {
'Fib': "fibroblast",
'HCC1937': "basal like breast cancer",
'SupB15WT': None,
'SupB15RT': None,
'HCT116': "colon carcinoma",
'HCC1143': "basal like breast cancer",
'JY': "B-cell",
}
result["hla"] = result.sample_id.map(allele_map)
print("Entries before dropping samples with unknown alleles", len(result))
result = result.loc[~result.hla.isnull()]
print("Entries after dropping samples with unknown alleles", len(result))
result["sample_type"] = result.sample_id.map(sample_type)
print(result.head(3))
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return result
# Hack to add all functions with names like handle_pmid_XXXX to HANDLERS dict.
for (key, value) in list(locals().items()):
if key.startswith("handle_pmid_"):
HANDLERS[key.replace("handle_pmid_", "")] = value
def run():
args = parser.parse_args(sys.argv[1:])
dfs = []
for item_tpl in args.item:
(pmid, filenames) = (item_tpl[0], item_tpl[1:])
print("Processing item", pmid, *[os.path.abspath(f) for f in filenames])
df = None
if pmid in HANDLERS:
df = HANDLERS[pmid](*filenames)
elif args.debug:
debug(*filenames)
else:
raise NotImplementedError(args.pmid)
if df is not None:
df["pmid"] = pmid
print("*** PMID %s: %d peptides ***" % (pmid, len(df)))
print("Counts by sample id:")
print(df.groupby("sample_id").peptide.nunique())
print("")
print("Counts by sample type:")
print(df.groupby("sample_type").peptide.nunique())
print("****************************")
dfs.append(df)
df = pandas.concat(dfs, ignore_index=True)
df.to_csv(args.out, index=False)
print("Wrote: %s" % args.out)
if __name__ == '__main__':
run()