"""
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
import collections
from six.moves import StringIO
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):
"""Gloger, ..., Neri Cancer Immunol Immunother 2016 [PMID 27600516]"""
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_df = pandas.DataFrame(rows, columns=["sample_id", "peptide"])
result_df["sample_type"] = "melanoma_cell_line"
result_df["cell_line"] = result_df.sample_id
result_df["mhc_class"] = "I"
result_df["pulldown_antibody"] = "W6/32"
result_df["format"] = "multiallelic"
result_df["hla"] = result_df.sample_id.map({
"FM-82": "HLA-A*02:01 HLA-A*01:01 HLA-B*08:01 HLA-B*15:01 HLA-C*03:04 HLA-C*07:01",
"FM-93/2": "HLA-A*02:01 HLA-A*26:01 HLA-B*40:01 HLA-B*44:02 HLA-C*03:04 HLA-C*05:01",
"Mel-624": "HLA-A*02:01 HLA-A*03:01 HLA-B*07:02 HLA-B*14:01 HLA-C*07:02 HLA-C*08:02",
"MeWo": "HLA-A*02:01 HLA-A*26:01 HLA-B*14:02 HLA-B*38:01 HLA-C*08:02 HLA-C*12:03",
"SK-Mel-5": "HLA-A*02:01 HLA-A*11:01 HLA-B*40:01 HLA-C*03:03",
})
return result_df
def handle_pmid_23481700(filename):
"""Hassan, ..., van Veelen Mol Cell Proteomics 2015 [PMID 23481700]"""
df = pandas.read_excel(filename, skiprows=10)
assert df["Peptide sequence"].iloc[0] == "TPSLVKSTSQL"
assert df["Peptide sequence"].iloc[-1] == "LPHSVNSKL"
hla = {
"JY": "HLA-A*02:01 HLA-B*07:02 HLA-C*07:02",
"HHC": "HLA-A*02:01 HLA-B*07:02 HLA-B*44:02 HLA-C*05:01 HLA-C*07:02",
}
results = []
for sample_id in ["JY", "HHC"]:
hits_df = df.loc[
df["Int %s" % sample_id].map(
lambda x: {"n.q.": 0, "n.q": 0}.get(x, x)).astype(float) > 0
]
result_df = pandas.DataFrame({
"peptide": hits_df["Peptide sequence"].dropna().values,
})
result_df["sample_id"] = sample_id
result_df["cell_line"] = "B-LCL-" + sample_id
result_df["hla"] = hla[sample_id]
result_df["sample_type"] = "B-LCL"
result_df["mhc_class"] = "I"
result_df["format"] = "multiallelic"
result_df["pulldown_antibody"] = "W6/32"
results.append(result_df)
result_df = pandas.concat(results, ignore_index=True)
return result_df
def handle_pmid_24616531(filename):
"""Mommen, ..., Heck PNAS 2014 [PMID 24616531]"""
df = pandas.read_excel(filename, sheet_name="EThcD")
peptides = df.Sequence.values
assert peptides[0] == "APFLRIAF"
assert peptides[-1] == "WRQAGLSYIRYSQI"
result_df = pandas.DataFrame({
"peptide": peptides,
})
result_df["sample_id"] = "24616531"
result_df["sample_type"] = "B-lymphoblastoid"
result_df["cell_line"] = "GR"
result_df["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_df["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"
result_df["mhc_class"] = "I"
result_df["format"] = "multiallelic"
return result_df
def handle_pmid_25576301(filename):
"""Bassani-Sternberg, ..., Mann Mol Cell Proteomics 2015 [PMID 25576301]"""
df = pandas.read_excel(filename, sheet_name="Peptides")
peptides = df.Sequence.values
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_df = pandas.DataFrame(rows, columns=["peptide", "sample_id"])
result_df["cell_line"] = ""
result_df["pulldown_antibody"] = "W6/32"
result_df["mhc_class"] = "I"
result_df["format"] = "multiallelic"
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_df["hla"] = result_df.sample_id.map(allele_map)
print("Entries before dropping samples with unknown alleles", len(result_df))
result_df = result_df.loc[~result_df.hla.isnull()]
print("Entries after dropping samples with unknown alleles", len(result_df))
result_df["sample_type"] = result_df.sample_id.map(sample_type)
print(result_df.head(3))
return result_df
def handle_pmid_26992070(*filenames):
"""Ritz, ..., Fugmann Proteomics 2016 [PMID 26992070]"""
allele_text = """
Cell line HLA-A 1 HLA-A 2 HLA-B 1 HLA-B 2 HLA-C 1 HLA-C 2
HEK293 03:01 03:01 07:02 07:02 07:02 07:02
HL-60 01:01 01:01 57:01 57:01 06:02 06:02
RPMI8226 30:01 68:02 15:03 15:10 02:10 03:04
MAVER-1 24:02 26:01 38:01 44:02 05:01 12:03
THP-1 02:01 24:02 15:11 35:01 03:03 03:03
"""
allele_info = pandas.read_csv(
StringIO(allele_text), sep="\t", index_col=0)
allele_info.index = allele_info.index.str.strip()
for gene in ["A", "B", "C"]:
for num in ["1", "2"]:
allele_info[
"HLA-%s %s" % (gene, num)
] = "HLA-" + gene + allele_info["HLA-%s %s" % (gene, num)]
cell_line_to_allele = allele_info.apply(" ".join, axis=1)
sheets = {}
for f in filenames:
if f.endswith(".xlsx"):
d = pandas.read_excel(f, sheet_name=None, skiprows=1)
sheets.update(d)
dfs = []
for cell_line in cell_line_to_allele.index:
# Using data from DeepQuanTR, which appears to be a consensus between
# two other methods used.
sheet = sheets[cell_line + "_DeepQuanTR"]
replicated = sheet.loc[
sheet[[c for c in sheet if "Sample" in c]].fillna(0).sum(1) > 1
]
df = pandas.DataFrame({
'peptide': replicated.Sequence.values
})
df["sample_id"] = cell_line
df["hla"] = cell_line_to_allele.get(cell_line)
dfs.append(df)
result_df = pandas.concat(dfs, ignore_index=True)
result_df["pulldown_antibody"] = "W6/32"
result_df["cell_line"] = result_df["sample_id"]
result_df["sample_type"] = result_df.sample_id.map({
"HEK293": "hek",
"HL-60": "neutrophil",
"RPMI8226": "b-cell",
"MAVER-1": "b-lymphoblast",
"THP-1": "monocyte",
})
result_df["mhc_class"] = "I"
result_df["format"] = "multiallelic"
return result_df
def handle_pmid_27412690(filename):
"""Shraibman, ..., Admon Mol Cell Proteomics 2016 [PMID 27412690]"""
hla_types = {
"U-87": "HLA-A*02:01 HLA-B*44:02 HLA-C*05:01",
"T98G": "HLA-A*02:01 HLA-B*39:06 HLA-C*07:02",
"LNT-229": "HLA-A*03:01 HLA-B*35:01 HLA-C*04:01",
}
sample_id_to_cell_line = {
"U-87": "U-87",
"T98G": "T98G",
"LNT-229": "LNT-229",
"U-87+DAC": "U-87",
"T98G+DAC": "T98G",
"LNT-229+DAC": "LNT-229",
}
df = pandas.read_excel(filename)
assert df.Sequence.iloc[0] == "AAAAAAGSGTPR"
intensity_col_to_sample_id = {}
for col in df:
if col.startswith("Intensity "):
sample_id = col.split()[1]
assert sample_id in sample_id_to_cell_line, (col, sample_id)
intensity_col_to_sample_id[col] = sample_id
dfs = []
for (sample_id, cell_line) in sample_id_to_cell_line.items():
intensity_cols = [
c for (c, v) in intensity_col_to_sample_id.items()
if v == sample_id
]
hits_df = df.loc[
(df[intensity_cols] > 0).sum(1) > 1
]
result_df = pandas.DataFrame({
"peptide": hits_df.Sequence.values,
})
result_df["sample_id"] = sample_id
result_df["cell_line"] = cell_line
result_df["hla"] = hla_types[cell_line]
dfs.append(result_df)
result_df = pandas.concat(dfs, ignore_index=True)
result_df["sample_type"] = "glioblastoma"
result_df["pulldown_antibody"] = "W6/32"
result_df["mhc_class"] = "I"
result_df["format"] = "multiallelic"
return result_df
def handle_pmid_28832583(*filenames):
"""Bassani-Sternberg, ..., Gfeller PLOS Comp. Bio. 2017 [PMID 28832583]"""
# This work also reanalyzes data from
# Pearson, ..., Perreault J Clin Invest 2016 [PMID 27841757]
(filename_dataset1, filename_dataset2) = sorted(filenames)
dataset1 = pandas.read_csv(filename_dataset1, sep="\t")
dataset2 = pandas.read_csv(filename_dataset2, sep="\t")
df = pandas.concat([dataset1, dataset2], ignore_index=True, sort=False)
info_text = """
cell_line origin original_pmid allele1 allele2 allele3 allele4 allele5 allele6
CD165 B-cell 28832583 HLA-A*02:05 HLA-A*24:02 HLA-B*15:01 HLA-B*50:01 HLA-C*03:03 HLA-C*06:02
CM467 B-cell 28832583 HLA-A*01:01 HLA-A*24:02 HLA-B*13:02 HLA-B*39:06 HLA-C*06:02 HLA-C*12:03
GD149 B-cell 28832583 HLA-A*01:01 HLA-A*24:02 HLA-B*38:01 HLA-B*44:03 HLA-C*06:02 HLA-C*12:03
MD155 B-cell 28832583 HLA-A*02:01 HLA-A*24:02 HLA-B*15:01 HLA-B*18:01 HLA-C*03:03 HLA-C*07:01
PD42 B cell 28832583 HLA-A*02:06 HLA-A*24:02 HLA-B*07:02 HLA-B*55:01 HLA-C*01:02 HLA-C*07:02
RA957 B cell 28832583 HLA-A*02:20 HLA-A*68:01 HLA-B*35:03 HLA-B*39:01 HLA-C*04:01 HLA-C*07:02
TIL1 TIL 28832583 HLA-A*02:01 HLA-A*02:01 HLA-B*18:01 HLA-B*38:01 HLA-C*05:01
TIL3 TIL 28832583 HLA-A*01:01 HLA-A*23:01 HLA-B*07:02 HLA-B*15:01 HLA-C*12:03 HLA-C*14:02
Apher1 Leukapheresis 28832583 HLA-A*03:01 HLA-A*29:02 HLA-B*44:02 HLA-B*44:03 HLA-C*12:03 HLA-C*16:01
Apher6 Leukapheresis 28832583 HLA-A*02:01 HLA-A*03:01 HLA-B*07:02 HLA-C*07:02
pat_AC2 B lymphoblast 27841757 HLA-A*03:01 HLA-A*32:01 HLA-B*27:05 HLA-B*45:01
pat_C B lymphoblast 27841757 HLA-A*02:01 HLA-A*03:01 HLA-B*07:02 HLA-C*07:02
pat_CELG B lymphoblast 27841757 HLA-A*02:01 HLA-A*24:02 HLA-B*15:01 HLA-B*73:01 HLA-C*03:03 HLA-C*15:05
pat_CP2 B lymphoblast 27841757 HLA-A*11:01 HLA-B*14:02 HLA-B*44:02
pat_FL B lymphoblast 27841757 HLA-A*03:01 HLA-A*11:01 HLA-B*44:03 HLA-B*50:01
pat_J B lymphoblast 27841757 HLA-A*02:01 HLA-A*03:01 HLA-B*07:02 HLA-C*07:02
pat_JPB3 B lymphoblast 27841757 HLA-A*02:01 HLA-A*11:01 HLA-B*27:05 HLA-B*56:01
pat_JT2 B lymphoblast 27841757 HLA-A*11:01 HLA-B*18:03 HLA-B*35:01
pat_M B lymphoblast 27841757 HLA-A*03:01 HLA-A*29:02 HLA-B*08:01 HLA-B*44:03 HLA-C*07:01 HLA-C*16:01
pat_MA B lymphoblast 27841757 HLA-A*02:01 HLA-A*29:02 HLA-B*44:03 HLA-B*57:01 HLA-C*07:01 HLA-C*16:01
pat_ML B lymphoblast 27841757 HLA-A*02:01 HLA-A*11:01 HLA-B*40:01 HLA-B*44:03
pat_NS2 B lymphoblast 27841757 HLA-A*02:01 HLA-B*13:02 HLA-B*41:01
pat_NT B lymphoblast 27841757 HLA-A*01:01 HLA-A*32:01 HLA-B*08:01
pat_PF1 B lymphoblast 27841757 HLA-A*01:01 HLA-A*02:01 HLA-B*07:02 HLA-B*44:03 HLA-C*07:02 HLA-C*16:01
pat_R B lymphoblast 27841757 HLA-A*03:01 HLA-A*29:02 HLA-B*08:01 HLA-B*44:03 HLA-C*07:01 HLA-C*16:01
pat_RT B lymphoblast 27841757 HLA-A*01:01 HLA-A*02:01 HLA-B*18:01 HLA-B*39:24 HLA-C*05:01 HLA-C*07:01
pat_SR B lymphoblast 27841757 HLA-A*02:01 HLA-A*23:01 HLA-B*18:01 HLA-B*44:03
pat_ST B lymphoblast 27841757 HLA-A*03:01 HLA-A*24:02 HLA-B*07:02 HLA-B*27:05
"""
info_df = pandas.read_csv(StringIO(info_text), sep="\t", index_col=0)
info_df.index = info_df.index.str.strip()
info_df["hla"] = info_df[
[c for c in info_df if c.startswith("allele")]
].fillna("").apply(" ".join, axis=1)
results = []
for col in df.columns:
if col.startswith("Intensity "):
sample_id = col.replace("Intensity ", "")
assert sample_id in info_df.index, sample_id
peptides = df.loc[df[col].fillna(0) > 0].Sequence.unique()
result_df = pandas.DataFrame({"peptide": peptides})
result_df["sample_id"] = sample_id
result_df["hla"] = info_df.loc[sample_id].hla
result_df["sample_type"] = info_df.loc[sample_id].origin
result_df["original_pmid"] = str(
info_df.loc[sample_id].original_pmid)
results.append(result_df)
result_df = pandas.concat(results, ignore_index=True)
samples = result_df.sample_id.unique()
for sample_id in info_df.index:
assert sample_id in samples, (sample_id, samples)
result_df["mhc_class"] = "I"
result_df["format"] = "multiallelic"
result_df["cell_line"] = ""
result_df["pulldown_antibody"] = "W6/32"
return result_df
def handle_pmid_31495665(filename):
"""Abelin, ..., Rooney Immunity 2019 [PMID 31495665]"""
hla_type = {
"HLA-DR_A375": None,
"HLA-DR_Lung": "DRB1*01:01 DRB1*03:01 DRB3*01:01",
"HLA-DR_PBMC_HDSC": "DRB1*03:01 DRB1*11:01 DRB3*01:01 DRB3*02:02",
"HLA-DR_PBMC_RG1095": "HLA-DRA1*01:01-DRB1*03:01 HLA-DRA1*01:01-DRB1*11:01 HLA-DRA1*01:01-DRB3*01:01 HLA-DRA1*01:01-DRB3*02:02",
"HLA-DR_PBMC_RG1104": "DRB1*01:01 DRB1*11:01 DRB3*02:02",
"HLA-DR_PBMC_RG1248": "DRB1*03:01 DRB1*03:01 DRB3*01:01 DRB3*01:01",
"HLA-DR_SILAC_Donor1_10minLysate": None,
"HLA-DR_SILAC_Donor1_5hrLysate": None,
"HLA-DR_SILAC_Donor1_DConly": None,
"HLA-DR_SILAC_Donor1_UVovernight": None,
"HLA-DR_SILAC_Donor2_DC_UV_16hr": None,
"HLA-DR_SILAC_Donor2_DC_UV_24hr": None,
"HLA-DR_Spleen": "DRB1*04:01 DRB4*01:03 DRB1*15:03 DRB5*01:01",
"MAPTAC_A*02:01": "HLA-A*02:01",
"MAPTAC_A*11:01": "HLA-A*11:01",
"MAPTAC_A*32:01": "HLA-A*32:01",
"MAPTAC_B*07:02": "HLA-B*07:02",
"MAPTAC_B*45:01": "HLA-B*45:01",
"MAPTAC_B*52:01": "HLA-B*52:01",
"MAPTAC_C*03:03": "HLA-C*03:03",
"MAPTAC_C*06:02": "HLA-C*06:02",
"MAPTAC_DPB1*06:01/DPA1*01:03_dm+": "HLA-DPB1*06:01-DPA1*01:03",
"MAPTAC_DPB1*06:01/DPA1*01:03_dm-": "HLA-DPB1*06:01-DPA1*01:03",
"MAPTAC_DQB1*06:04/DQA1*01:02_dm+": "HLA-DQB1*06:04-DQA1*01:02",
"MAPTAC_DQB1*06:04/DQA1*01:02_dm-": "HLA-DQB1*06:04-DQA1*01:02",
"MAPTAC_DRB1*01:01": "HLA-DRA1*01:01-DRB1*01:01",
"MAPTAC_DRB1*03:01": "HLA-DRA1*01:01-DRB1*03:01",
"MAPTAC_DRB1*04:01": "HLA-DRA1*01:01-DRB1*04:01",
"MAPTAC_DRB1*07:01": "HLA-DRA1*01:01-DRB1*07:01",
"MAPTAC_DRB1*11:01": "HLA-DRA1*01:01-DRB1*11:01",
"MAPTAC_DRB1*12:01_dm+": "HLA-DRA1*01:01-DRB1*12:01",
"MAPTAC_DRB1*12:01_dm-": "HLA-DRA1*01:01-DRB1*12:01",
"MAPTAC_DRB1*15:01": "HLA-DRA1*01:01-DRB1*15:01",
"MAPTAC_DRB3*01:01_dm+": "HLA-DRA1*01:01-DRB3*01:01",
"MAPTAC_DRB3*01:01_dm-": "HLA-DRA1*01:01-DRB3*01:01",
}
pulldown_antibody = {
"HLA-DR_Lung": "L243 (HLA-DR)",
"HLA-DR_PBMC_HDSC": "tal1b5 (HLA-DR)",
"HLA-DR_PBMC_RG1095": "tal1b5 (HLA-DR)",
"HLA-DR_PBMC_RG1104": "tal1b5 (HLA-DR)",
"HLA-DR_PBMC_RG1248": "tal1b5 (HLA-DR)",
"HLA-DR_Spleen": "L243 (HLA-DR)",
"MAPTAC_A*02:01": "MAPTAC",
"MAPTAC_A*11:01": "MAPTAC",
"MAPTAC_A*32:01": "MAPTAC",
"MAPTAC_B*07:02": "MAPTAC",
"MAPTAC_B*45:01": "MAPTAC",
"MAPTAC_B*52:01": "MAPTAC",
"MAPTAC_C*03:03": "MAPTAC",
"MAPTAC_C*06:02": "MAPTAC",
"MAPTAC_DPB1*06:01/DPA1*01:03_dm+": "MAPTAC",
"MAPTAC_DPB1*06:01/DPA1*01:03_dm-": "MAPTAC",
"MAPTAC_DQB1*06:04/DQA1*01:02_dm+": "MAPTAC",
"MAPTAC_DQB1*06:04/DQA1*01:02_dm-": "MAPTAC",
"MAPTAC_DRB1*01:01": "MAPTAC",
"MAPTAC_DRB1*03:01": "MAPTAC",
"MAPTAC_DRB1*04:01": "MAPTAC",
"MAPTAC_DRB1*07:01": "MAPTAC",
"MAPTAC_DRB1*11:01": "MAPTAC",
"MAPTAC_DRB1*12:01_dm+": "MAPTAC",
"MAPTAC_DRB1*12:01_dm-": "MAPTAC",
"MAPTAC_DRB1*15:01": "MAPTAC",
"MAPTAC_DRB3*01:01_dm+": "MAPTAC",
"MAPTAC_DRB3*01:01_dm-": "MAPTAC",
}
format = {
"HLA-DR_Lung": "DR-specific",
"HLA-DR_PBMC_HDSC": "DR-specific",
"HLA-DR_PBMC_RG1095": "DR-specific",
"HLA-DR_PBMC_RG1104": "DR-specific",
"HLA-DR_PBMC_RG1248": "DR-specific",
"HLA-DR_Spleen": "DR-specific",
"MAPTAC_A*02:01": "monoallelic",
"MAPTAC_A*11:01": "monoallelic",
"MAPTAC_A*32:01": "monoallelic",
"MAPTAC_B*07:02": "monoallelic",
"MAPTAC_B*45:01": "monoallelic",
"MAPTAC_B*52:01": "monoallelic",
"MAPTAC_C*03:03": "monoallelic",
"MAPTAC_C*06:02": "monoallelic",
"MAPTAC_DPB1*06:01/DPA1*01:03_dm+": "monoallelic",
"MAPTAC_DPB1*06:01/DPA1*01:03_dm-": "monoallelic",
"MAPTAC_DQB1*06:04/DQA1*01:02_dm+": "monoallelic",
"MAPTAC_DQB1*06:04/DQA1*01:02_dm-": "monoallelic",
"MAPTAC_DRB1*01:01": "monoallelic",
"MAPTAC_DRB1*03:01": "monoallelic",
"MAPTAC_DRB1*04:01": "monoallelic",
"MAPTAC_DRB1*07:01": "monoallelic",
"MAPTAC_DRB1*11:01": "monoallelic",
"MAPTAC_DRB1*12:01_dm+": "monoallelic",
"MAPTAC_DRB1*12:01_dm-": "monoallelic",
"MAPTAC_DRB1*15:01": "monoallelic",
"MAPTAC_DRB3*01:01_dm+": "monoallelic",
"MAPTAC_DRB3*01:01_dm-": "monoallelic",
}
mhc_class = {
"HLA-DR_Lung": "II",
"HLA-DR_PBMC_HDSC": "II",
"HLA-DR_PBMC_RG1095": "II",
"HLA-DR_PBMC_RG1104": "II",
"HLA-DR_PBMC_RG1248": "II",
"HLA-DR_Spleen": "II",
"MAPTAC_A*02:01": "I",
"MAPTAC_A*11:01": "I",
"MAPTAC_A*32:01": "I",
"MAPTAC_B*07:02": "I",
"MAPTAC_B*45:01": "I",
"MAPTAC_B*52:01": "I",
"MAPTAC_C*03:03": "I",
"MAPTAC_C*06:02": "I",
"MAPTAC_DPB1*06:01/DPA1*01:03_dm+": "II",
"MAPTAC_DPB1*06:01/DPA1*01:03_dm-": "II",
"MAPTAC_DQB1*06:04/DQA1*01:02_dm+": "II",
"MAPTAC_DQB1*06:04/DQA1*01:02_dm-": "II",
"MAPTAC_DRB1*01:01": "II",
"MAPTAC_DRB1*03:01": "II",
"MAPTAC_DRB1*04:01": "II",
"MAPTAC_DRB1*07:01": "II",
"MAPTAC_DRB1*11:01": "II",
"MAPTAC_DRB1*12:01_dm+": "II",
"MAPTAC_DRB1*12:01_dm-": "II",
"MAPTAC_DRB1*15:01": "II",
"MAPTAC_DRB3*01:01_dm+": "II",
"MAPTAC_DRB3*01:01_dm-": "II",
}
cell_line = {
"HLA-DR_Lung": "",
"HLA-DR_PBMC_HDSC": "",
"HLA-DR_PBMC_RG1095": "",
"HLA-DR_PBMC_RG1104": "",
"HLA-DR_PBMC_RG1248": "",
"HLA-DR_Spleen": "",
"MAPTAC_A*02:01": "",
"MAPTAC_A*11:01": "",
"MAPTAC_A*32:01": "",
"MAPTAC_B*07:02": "",
"MAPTAC_B*45:01": "",
"MAPTAC_B*52:01": "",
"MAPTAC_C*03:03": "",
"MAPTAC_C*06:02": "",
"MAPTAC_DPB1*06:01/DPA1*01:03_dm+": "expi293",
"MAPTAC_DPB1*06:01/DPA1*01:03_dm-": "expi293",
"MAPTAC_DQB1*06:04/DQA1*01:02_dm+": "expi293", # don't actually see this in DataS1A!
"MAPTAC_DQB1*06:04/DQA1*01:02_dm-": "expi293",
"MAPTAC_DRB1*01:01": "",
"MAPTAC_DRB1*03:01": "",
"MAPTAC_DRB1*04:01": "",
"MAPTAC_DRB1*07:01": "",
"MAPTAC_DRB1*11:01": "",
"MAPTAC_DRB1*12:01_dm+": "",
"MAPTAC_DRB1*12:01_dm-": "",
"MAPTAC_DRB1*15:01": "",
"MAPTAC_DRB3*01:01_dm+": "",
"MAPTAC_DRB3*01:01_dm-": "",
}
sample_type = {
"HLA-DR_Lung": "lung",
"HLA-DR_PBMC_HDSC": "lung",
"HLA-DR_PBMC_RG1095": "lung",
"HLA-DR_PBMC_RG1104": "lung",
"HLA-DR_PBMC_RG1248": "lung",
"HLA-DR_Spleen": "spleen",
"MAPTAC_A*02:01": "mixed",
"MAPTAC_A*11:01": "mixed",
"MAPTAC_A*32:01": "mixed",
"MAPTAC_B*07:02": "mixed",
"MAPTAC_B*45:01": "mixed",
"MAPTAC_B*52:01": "mixed",
"MAPTAC_C*03:03": "mixed",
"MAPTAC_C*06:02": "mixed",
"MAPTAC_DPB1*06:01/DPA1*01:03_dm+": "mixed",
"MAPTAC_DPB1*06:01/DPA1*01:03_dm-": "mixed",
"MAPTAC_DQB1*06:04/DQA1*01:02_dm+": "mixed",
"MAPTAC_DQB1*06:04/DQA1*01:02_dm-": "mixed",
"MAPTAC_DRB1*01:01": "mixed",
"MAPTAC_DRB1*03:01": "mixed",
"MAPTAC_DRB1*04:01": "mixed",
"MAPTAC_DRB1*07:01": "mixed",
"MAPTAC_DRB1*11:01": "mixed",
"MAPTAC_DRB1*12:01_dm+": "mixed",
"MAPTAC_DRB1*12:01_dm-": "mixed",
"MAPTAC_DRB1*15:01": "mixed",
"MAPTAC_DRB3*01:01_dm+": "mixed",
"MAPTAC_DRB3*01:01_dm-": "mixed",
}
df = pandas.read_excel(filename, sheetname="DataS1B")
results = []
for sample_id in df.columns:
if hla_type[sample_id] is None:
print("Intentionally skipping", sample_id)
continue
result_df = pandas.DataFrame({
"peptide": df[sample_id].dropna().values,
})
result_df["sample_id"] = sample_id
result_df["hla"] = hla_type[sample_id]
result_df["pulldown_antibody"] = pulldown_antibody[sample_id]
result_df["format"] = format[sample_id]
result_df["mhc_class"] = mhc_class[sample_id]
result_df["sample_type"] = sample_type[sample_id]
result_df["cell_line"] = cell_line[sample_id]
results.append(result_df)
result_df = pandas.concat(results, ignore_index=True)
return result_df
# 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 (i, item_tpl) in enumerate(args.item):
(pmid, filenames) = (item_tpl[0], item_tpl[1:])
print(
"Processing item %d / %d" % (i + 1, len(args.item)),
pmid,
*[os.path.abspath(f) for f in filenames])
df = None
handler = None
if pmid in HANDLERS:
handler = HANDLERS[pmid]
df = handler(*filenames)
elif args.debug:
debug(*filenames)
else:
raise NotImplementedError(args.pmid)
if df is not None:
df["pmid"] = pmid
if "original_pmid" not in df.columns:
df["original_pmid"] = pmid
df = df.applymap(str).applymap(str.upper)
print("*** PMID %s: %d peptides ***" % (pmid, len(df)))
if handler is not None:
print(handler.__doc__)
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, sort=False)
df["cell_line"] = df["cell_line"].fillna("")
cols = ["pmid", "sample_id", "peptide", "format", "mhc_class", "hla", ]
cols += [c for c in sorted(df.columns) if c not in cols]
df = df[cols]
null_df = df.loc[df.isnull().any(1)]
if len(null_df) > 0:
print("Nulls:")
print(null_df)
else:
print("No nulls.")
df.to_csv(args.out, index=False)
print("Wrote: %s" % os.path.abspath(args.out))
if __name__ == '__main__':
run()