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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
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(
nargs="+",
action="append",
metavar="PMID FILE, ... FILE",
default=[],
"--expression-item",
nargs="+",
action="append",
metavar="LABEL FILE, ... FILE",
default=[],
help="Expression data to curate: dataset label and list of files")
parser.add_argument(
"--ms-out",
metavar="OUT.csv",
help="Out file path (MS data)")
parser.add_argument(
"--expression-out",
parser.add_argument(
"--expression-metadata-out",
metavar="OUT.csv",
help="Out file path for expression metadata, i.e. which samples used")
parser.add_argument(
"--debug",
action="store_true",
default=False,
help="Leave user in pdb if PMID is unsupported")
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
"""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)
# Rename samples to avoid a collision with the JY sample in PMID 25576301.
result_df.sample_id = result_df.sample_id.map({
"JY": "JY.2015",
"HHC": "HHC.2015",
})
"""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["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
"""Bassani-Sternberg, ..., Mann Mol Cell Proteomics 2015 [PMID 25576301]"""
df = pandas.read_excel(filename, sheet_name="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_df = pandas.DataFrame(rows, columns=["peptide", "sample_id"])
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",
}
cell_line = {
'Fib': None,
'HCC1937': "HCC1937",
'SupB15WT': None,
'SupB15RT': None,
'HCT116': "HCT116",
'HCC1143': "HCC1143",
'JY': "JY",
}
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]"""
# Although this publication seems to suggest that HEK293 are C*07:02
# (figure 3B), in a subsequent publication [PMID 28834231] this group
# gives the HEK293 HLA type as HLA‐A*03:01, HLA‐B*07:02, and HLA‐C*07:01.
# We are therefore using the HLA‐C*07:01 (i.e. the latter) typing results
# here.
allele_text = """
Cell line HLA-A 1 HLA-A 2 HLA-B 1 HLA-B 2 HLA-C 1 HLA-C 2
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)
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",
"THP-1": "monocyte",
})
result_df["mhc_class"] = "I"
result_df["format"] = "multiallelic"
return result_df
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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-LCL 27841757 HLA-A*03:01 HLA-A*32:01 HLA-B*27:05 HLA-B*45:01
pat_C B-LCL 27841757 HLA-A*02:01 HLA-A*03:01 HLA-B*07:02 HLA-C*07:02
pat_CELG B-LCL 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-LCL 27841757 HLA-A*11:01 HLA-B*14:02 HLA-B*44:02
pat_FL B-LCL 27841757 HLA-A*03:01 HLA-A*11:01 HLA-B*44:03 HLA-B*50:01
pat_J B-LCL 27841757 HLA-A*02:01 HLA-A*03:01 HLA-B*07:02 HLA-C*07:02
pat_JPB3 B-LCL 27841757 HLA-A*02:01 HLA-A*11:01 HLA-B*27:05 HLA-B*56:01
pat_JT2 B-LCL 27841757 HLA-A*11:01 HLA-B*18:03 HLA-B*35:01
pat_M B-LCL 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-LCL 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-LCL 27841757 HLA-A*02:01 HLA-A*11:01 HLA-B*40:01 HLA-B*44:03
pat_NS2 B-LCL 27841757 HLA-A*02:01 HLA-B*13:02 HLA-B*41:01
pat_NT B-LCL 27841757 HLA-A*01:01 HLA-A*32:01 HLA-B*08:01
pat_PF1 B-LCL 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-LCL 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-LCL 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-LCL 27841757 HLA-A*02:01 HLA-A*23:01 HLA-B*18:01 HLA-B*44:03
pat_ST B-LCL 27841757 HLA-A*03:01 HLA-A*24:02 HLA-B*07:02 HLA-B*27:05
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"""
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
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PMID_31495665_SAMPLE_TYPES = {
"HLA-DR_Lung": "lung",
"HLA-DR_PBMC_HDSC": "pbmc",
"HLA-DR_PBMC_RG1095": "pbmc",
"HLA-DR_PBMC_RG1104": "pbmc",
"HLA-DR_PBMC_RG1248": "pbmc",
"HLA-DR_Spleen": "spleen",
"MAPTAC_A*02:01": "mix:a375,expi293,hek293,hela",
"MAPTAC_A*11:01": "mix:expi293,hela",
"MAPTAC_A*32:01": "mix:a375,expi293,hela",
"MAPTAC_B*07:02": "mix:a375,expi293,hela",
"MAPTAC_B*45:01": "expi293",
"MAPTAC_B*52:01": "mix:a375,expi293",
"MAPTAC_C*03:03": "expi293",
"MAPTAC_C*06:02": "mix:a375,expi293",
"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",
"MAPTAC_DQB1*06:04/DQA1*01:02_dm-": "expi293",
"MAPTAC_DRB1*01:01": "mix:a375,b721,expi293,kg1,k562",
"MAPTAC_DRB1*03:01": "expi293",
"MAPTAC_DRB1*04:01": "expi293",
"MAPTAC_DRB1*07:01": "mix:expi293,hek293",
"MAPTAC_DRB1*11:01": "mix:expi293,k562,kg1",
"MAPTAC_DRB1*12:01_dm+": "expi293",
"MAPTAC_DRB1*12:01_dm-": "expi293",
"MAPTAC_DRB1*15:01": "expi293",
"MAPTAC_DRB3*01:01_dm+": "expi293",
"MAPTAC_DRB3*01:01_dm-": "expi293",
}
CELL_LINE_MIXTURES = sorted(
set(
x for x in PMID_31495665_SAMPLE_TYPES.values()
if x.startswith("mix:")))
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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_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": "expi293",
"MAPTAC_DRB1*04:01": "expi293",
"MAPTAC_DRB1*12:01_dm+": "expi293",
"MAPTAC_DRB1*12:01_dm-": "expi293",
"MAPTAC_DRB1*15:01": "expi293",
"MAPTAC_DRB3*01:01_dm+": "expi293",
"MAPTAC_DRB3*01:01_dm-": "expi293",
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["cell_line"] = cell_line[sample_id]
results.append(result_df)
result_df = pandas.concat(results, ignore_index=True)
return result_df
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def handle_pmid_27869121(filename):
"""Bassani-Sternberg, ..., Krackhardt Nature Comm. 2016 [PMID 27869121]"""
# Although this dataset has class II data also, we are only extracting
# class I for now.
df = pandas.read_excel(filename, skiprows=1)
# Taking these from:
# Supplementary Table 2: Information of patients selected for neoepitope
# identification
# For the Mel5 sample, only two-digit alleles are shown (A*01, A*25,
# B*08, B*18) so we are skipping that sample for now.
hla_df = pandas.DataFrame([
("Mel-8", "HLA-A*01:01 HLA-A*03:01 HLA-B*07:02 HLA-B*08:01 HLA-C*07:01 HLA-C*07:02"),
("Mel-12", "HLA-A*01:01 HLA-B*08:01 HLA-C*07:01"),
("Mel-15", "HLA-A*03:01 HLA-A*68:01 HLA-B*27:05 HLA-B*35:03 HLA-C*02:02 HLA-C*04:01"),
("Mel-16", "HLA-A*01:01 HLA-A*24:02 HLA-B*07:02 HLA-B*08:01 HLA-C*07:01 HLA-C*07:02"),
], columns=["sample_id", "hla"]).set_index("sample_id")
# We assert below that none of the class I hit peptides were found in any
# of the class II pull downs.
class_ii_cols = [
c for c in df.columns if c.endswith("HLA-II (arbitrary units)")
]
class_ii_hits = set(df.loc[
(df[class_ii_cols].fillna(0.0).sum(1) > 0)
].Sequence.unique())
results = []
for (sample_id, hla) in hla_df.hla.items():
intensity_col = "Intensity %s_HLA-I (arbitrary units)" % sample_id
sub_df = df.loc[
(df[intensity_col].fillna(0.0) > 0)
]
filtered_sub_df = sub_df.loc[
(~sub_df.Sequence.isin(class_ii_hits))
]
peptides = filtered_sub_df.Sequence.unique()
assert not any(p in class_ii_hits for p in peptides)
result_df = pandas.DataFrame({
"peptide": peptides,
})
result_df["sample_id"] = sample_id
result_df["hla"] = hla_df.loc[sample_id, "hla"]
result_df["pulldown_antibody"] = "W6/32"
result_df["format"] = "multiallelic"
result_df["mhc_class"] = "I"
result_df["sample_type"] = "melanoma_met"
result_df["cell_line"] = None
results.append(result_df)
result_df = pandas.concat(results, ignore_index=True)
return result_df
def handle_pmid_31154438(*filenames):
"""Shraibman, ..., Admon Mol Cell Proteomics 2019 [PMID 31154438]"""
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# Note: this publication also includes analyses of the secreted HLA
# peptidedome (sHLA) but we are using only the data from membrane-bound
# HLA.
(xls, txt) = sorted(filenames, key=lambda s: not s.endswith(".xlsx"))
info = pandas.read_excel(xls, skiprows=1)
df = pandas.read_csv(txt, sep="\t", skiprows=1)
hla_df = info.loc[
~info["mHLA tissue sample"].isnull()
].set_index("mHLA tissue sample")[["HLA typing"]]
def fix_hla(string):
result = []
alleles = string.split(";")
for a in alleles:
a = a.strip()
if "/" in a:
(a1, a2) = a.split("/")
a2 = a1[:2] + a2
lst = [a1, a2]
else:
lst = [a]
for a in lst:
normalized = normalize_allele_name(a)
# Ignore class II
if normalized[4] in ("A", "B", "C"):
result.append(normalized)
return " ".join(result)
hla_df["hla"] = hla_df["HLA typing"].map(fix_hla)
results = []
for (sample_id, hla) in hla_df.hla.items():
intensity_col = "Intensity %s" % sample_id
sub_df = df.loc[
(df[intensity_col].fillna(0.0) > 0)
]
peptides = sub_df.Sequence.unique()
result_df = pandas.DataFrame({
"peptide": peptides,
})
result_df["sample_id"] = sample_id
result_df["hla"] = hla_df.loc[sample_id, "hla"]
result_df["pulldown_antibody"] = "W6/32"
result_df["format"] = "multiallelic"
result_df["mhc_class"] = "I"
result_df["sample_type"] = "glioblastoma_tissue"
result_df["cell_line"] = None
results.append(result_df)
result_df = pandas.concat(results, ignore_index=True)
return result_df
result_df = pandas.DataFrame(index=df.index)
for (label, columns) in groups.items():
for col in columns:
if col not in df.columns:
raise ValueError(
"Missing: %s. Available: %s" % (col, df.columns.tolist()))
EXPRESSION_GROUPS_ROWS.append((dataset_identifier, label, columns))
return result_df
def handle_expression_GSE113126(*filenames):
"""
Barry, ..., Krummel Nature Medicine 2018 [PMID 29942093]
This is the melanoma met RNA-seq dataset.
"""
df = pandas.read_csv(filenames[0], sep="\t", index_col=0)
df = df[[]] # no columns
for filename in filenames:
df[os.path.basename(filename)] = pandas.read_csv(
filename, sep="\t", index_col=0)["TPM"]
assert len(df.columns) == len(filenames)
groups = {
"sample_type:MELANOMA_MET": df.columns.tolist(),
}
def handle_expression_expression_atlas_22460905(filename):
df = pandas.read_csv(filename, sep="\t", skiprows=4, index_col=0)
del df["Gene Name"]
df.columns = df.columns.str.lower()
df = df.fillna(0.0)
def matches(*strings):
return [c for c in df.columns if all(s in c for s in strings)]
groups = {
"sample_type:B-LCL": (
matches("b-cell", "lymphoblast") + matches("b acute lymphoblastic")),
"sample_type:B-CELL": matches("b-cell"),
"sample_type:A375-LIKE": matches("melanoma"),
"sample_type:KG1-LIKE": matches("myeloid leukemia"),
# Using a fibrosarcoma cell line for our fibroblast sample.
"sample_type:FIBROBLAST": ['fibrosarcoma, ht-1080'],
# For GBM tissue we are just using a mixture of cell lines.
"sample_type:GLIOBLASTOMA_TISSUE": matches("glioblastoma"),
"cell_line:THP-1": ["childhood acute monocytic leukemia, thp-1"],
"cell_line:HL-60": ["adult acute myeloid leukemia, hl-60"],
"cell_line:U-87": ['glioblastoma, u-87 mg'],
"cell_line:LNT-229": ['glioblastoma, ln-229'],
"cell_line:T98G": ['glioblastoma, t98g'],
"cell_line:SK-MEL-5": ['cutaneous melanoma, sk-mel-5'],
'cell_line:MEWO': ['melanoma, mewo'],
"cell_line:HCC1937": ['breast ductal adenocarcinoma, hcc1937'],
"cell_line:HCT116": ['colon carcinoma, hct 116'],
"cell_line:HCC1143": ['breast ductal adenocarcinoma, hcc1143'],
}
return [make_expression_groups("expression_atlas_22460905", df, groups)]
def handle_expression_human_protein_atlas(*filenames):
(cell_line_filename,) = [f for f in filenames if "celline" in f]
(blood_filename,) = [f for f in filenames if "blood" in f]
(gtex_filename,) = [f for f in filenames if "gtex" in f]
cell_line_df = pandas.read_csv(cell_line_filename, sep="\t")
blood_df = pandas.read_csv(blood_filename, sep="\t", index_col=0)
gtex_df = pandas.read_csv(gtex_filename, sep="\t")
cell_line_df = cell_line_df.pivot(
index="Gene", columns="Cell line", values="TPM")
gtex_df = gtex_df.pivot(
index="Gene", columns="Tissue", values="TPM")
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return [
make_expression_groups(
"human_protein_atlas:%s" % os.path.basename(blood_filename),
blood_df,
groups={
"sample_type:PBMC": [
c for c in blood_df.columns if "total PBMC" in c
],
# for samples labeled leukapheresis we also use PBMC
"sample_type:LEUKAPHERESIS": [
c for c in blood_df.columns if "total PBMC" in c
],
# for samples labeled TIL we are also using PBMC
"sample_type:TIL": [
c for c in blood_df.columns if "total PBMC" in c
],
}),
make_expression_groups(
"human_protein_atlas:%s" % os.path.basename(cell_line_filename),
cell_line_df,
groups={
"cell_line:HELA": ['HeLa'],
"cell_line:K562": ["K-562"],
"cell_line:HEK293": ['HEK 293'],
"cell_line:RPMI8226": ['RPMI-8226'],
"cell_line:EXPI293": ['HEK 293'], # EXPI293 derived from HEK293
}),
make_expression_groups(
"human_protein_atlas:%s" % os.path.basename(gtex_filename),
gtex_df,
groups={
"sample_type:LUNG": ["lung"],
"sample_type:SPLEEN": ["spleen"],
}),
]
def make_expression_mixtures(expression_df):
global CELL_LINE_MIXTURES
groups = {}
for mix in CELL_LINE_MIXTURES:
components = []
for item in mix.replace("mix:", "").upper().split(","):
if "cell_line:%s" % item in expression_df.columns:
components.append("cell_line:%s" % item)
else:
print("No cell line, falling back on similar: ", item)
components.append("sample_type:%s-LIKE" % item)
groups["sample_type:" + mix.upper()] = components
missing = set()
for some in groups.values():
for item in some:
if item not in expression_df.columns:
missing.add(item)
if missing:
raise ValueError(
"Missing [%d]: %s. Available: %s" % (
len(missing), missing, expression_df.columns.tolist()))
return make_expression_groups("mixtures", expression_df, groups)
# Add all functions with names like handle_pmid_XXXX to PMID_HANDLERS dict.
for (key, value) in list(locals().items()):
if key.startswith("handle_pmid_"):
PMID_HANDLERS[key.replace("handle_pmid_", "")] = value
elif key.startswith("handle_expression_"):
EXPRESSION_HANDLERS[key.replace("handle_expression_", "")] = value
def run():
args = parser.parse_args(sys.argv[1:])
expression_dfs = []
for (i, item_tpl) in enumerate(args.expression_item):
(label, filenames) = (item_tpl[0], item_tpl[1:])
label = label.replace("-", "_")
print(
"Processing expression item %d of %d" % (i + 1, len(args.expression_item)),
label,
*[os.path.abspath(f) for f in filenames])
handler = None
if label in EXPRESSION_HANDLERS:
handler = EXPRESSION_HANDLERS[label]
elif args.debug:
debug(*filenames)
else:
raise NotImplementedError(label)
"result dataframes",
len(expression_dfs_for_item))
print(*[e.columns for e in expression_dfs_for_item])
expression_dfs.extend(expression_dfs_for_item)
expression_df = expression_dfs[0]
for other in expression_dfs[1:]:
expression_df = pandas.merge(
expression_df, other, how='outer', left_index=True, right_index=True)
*[len(e) for e in expression_dfs])
print("Genes in merged expression dataframe", len(expression_df))
if CELL_LINE_MIXTURES:
print("Generating cell line mixtures.")
expression_mixture_df = make_expression_mixtures(expression_df)
expression_df = pandas.merge(
expression_df,
expression_mixture_df,
how='outer',
left_index=True,
right_index=True)
if pmid in PMID_HANDLERS:
handler = PMID_HANDLERS[pmid]
ms_df = handler(*filenames)