File size: 11,970 Bytes
89078a9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 |
from __future__ import annotations
import argparse
import os
from typing import Dict, Iterable, Iterator, List, Tuple, Optional
from tqdm.auto import tqdm
from datasets import Dataset, DatasetDict, Features, Sequence, Value, ClassLabel, disable_progress_bars
disable_progress_bars()
# --------------------------------------------------------------------
# Defaults: your file names (searched under base_dir if data_files unset)
# --------------------------------------------------------------------
DEFAULT_ELUT_FILES = [
"CB660_neural_stem_cell_IEX_Wan_2015.elut",
"G166_glioma_stem_cell_IEX_Wan_2015_Hs_HCW_2.elut",
"G166_glioma_stem_cell_IEX_Wan_2015_Hs_HCW_3.elut",
"IEX_Wan_2015_Hs_IEX_1.elut",
"IEX_Wan_2015_Hs_IEX_2.elut",
"IEX_Wan_2015_Hs_HCW_4.elut",
"IEX_Wan_2015_Hs_HCW_5.elut",
"IEX_Wan_2015_Hs_HCW_6.elut",
"IEX_Wan_2015_Hs_HCW_7.elut",
"IEX_Wan_2015_Hs_HCW_8.elut",
"IEX_Wan_2015_Hs_HCW_9.elut",
"HEK_293_T_cells_SEC_Mallam_2019_C1.elut",
"HEK_293_T_cells_SEC_Mallam_2019_C2.elut",
"NTera2_embryonal_carcinoma_stem_cells_IEX_Moutaoufik_2019_2_R1.elut",
"NTera2_embryonal_carcinoma_stem_cells_IEX_Moutaoufik_2019_2_R2.elut",
"NTera2_embryonal_carcinoma_stem_cells_IEX_Moutaoufik_2019_R1.elut",
"NTera2_embryonal_carcinoma_stem_cells_IEX_Moutaoufik_2019_R2.elut",
"NTera2_embryonal_carcinoma_stem_cells_SEC_Moutaoufik_2019_2_R1.elut",
"NTera2_embryonal_carcinoma_stem_cells_SEC_Moutaoufik_2019_2_R2.elut",
"NTera2_embryonal_carcinoma_stem_cells_SEC_Moutaoufik_2019_R1.elut",
"NTera2_embryonal_carcinoma_stem_cells_SEC_Moutaoufik_2019_R2.elut",
"T98G_glioblastoma_multiforme_cells_SEC_Conelly_2018_Bio1.elut",
"T98G_glioblastoma_multiforme_cells_SEC_Conelly_2018_Bio2.elut",
"U2OS_cells_SEC_Kirkwood_2013_rep1.elut",
"U2OS_cells_SEC_Kirkwood_2013_rep2.elut",
"U2OS_cells_SEC_Kirkwood_2013_rep3.elut",
"U2OS_cells_SEC_Larance_2016_PT3281S1.elut",
"U2OS_cells_SEC_Larance_2016_PT3441S1.elut",
"U2OS_cells_SEC_Larance_2016_PT3442S1.elut",
"U2OS_cells_SEC_Larance_2016_PT3701S1.elut",
"U2OS_cells_SEC_Larance_2016_PTSS3801.elut",
"U2OS_cells_SEC_Larance_2016_PTSS3802.elut",
]
DEFAULT_PAIR_FILES = {
# split -> (pos, neg)
"train": (
"intact_complex_merge_20230309.train_ppis.txt",
"intact_complex_merge_20230309.neg_train_ppis.txt",
),
"test": (
"intact_complex_merge_20230309.test_ppis.txt",
"intact_complex_merge_20230309.neg_test_ppis.txt",
),
}
def read_elut_table(path: str) -> Tuple[str, List[str], Dict[str, List[int]]]:
experiment_id = os.path.splitext(os.path.basename(path))[0]
table: Dict[str, List[int]] = {}
with open(path, "r", encoding="utf-8", errors="ignore") as f:
first = f.readline().rstrip("\n\r")
if not first:
return experiment_id, [], {}
cols = first.split("\t")
fraction_names = cols[1:]
for line in f:
line = line.rstrip("\n\r")
if not line:
continue
toks = line.split("\t")
if not toks:
continue
uniprot_id = toks[0]
trace: Li8st[int] = []
for x in toks[1:]:
if x == "" or x is None:
trace.append(0)
else:
try:
trace.append(int(float(x)))
except ValueError:
trace.append(0)
table[uniprot_id] = trace
return experiment_id, fraction_names, table
def read_pair_file(path: str) -> Iterable[Tuple[str, str]]:
with open(path, "r", encoding="utf-8", errors="ignore") as f:
for line in f:
line = line.strip()
if not line or line.startswith("#"):
continue
parts = line.split()
if len(parts) != 2:
continue
yield parts[0], parts[1]
def build_proteins_dataset(elut_files: List[str]) -> Dataset:
features = Features(
{
"experiment_id": Value("string"),
"uniprot_id": Value("string"),
"fraction_names": Sequence(Value("string")),
"trace": Sequence(Value("int32")),
}
)
def gen() -> Iterator[dict]:
with tqdm(total=None, unit="ex",
desc="proteins: examples") as pbar:
for elut_path in elut_files:
exp_id, frac_names, table = read_elut_table(elut_path)
for uid, trace in tqdm(table.items(),
desc=f"{os.path.basename(elut_path)}",
leave=False):
yield {
"experiment_id": exp_id,
"uniprot_id": uid,
"fraction_names": frac_names,
"trace": trace,
}
pbar.update(1)
return Dataset.from_generator(gen, features=features)
def build_pairs_dataset(
elut_files: List[str],
split_posneg: Dict[str, Tuple[List[str], List[str]]]
) -> DatasetDict:
features = Features(
{
"experiment_id": Value("string"),
"uniprot_id1": Value("string"),
"uniprot_id2": Value("string"),
"elut_trace1": Sequence(Value("int32")),
"elut_trace2": Sequence(Value("int32")),
"label": ClassLabel(names=["neg", "pos"]),
}
)
elut_tables: Dict[str, Tuple[List[str], Dict[str, List[int]]]] = {}
for elut_path in tqdm(elut_files, desc="pairs: elution files"):
try:
exp_id, frac_names, table = read_elut_table(elut_path)
elut_tables[exp_id] = (frac_names, table)
except Exception as e:
tqdm.write(f"WARNING: Failed to read {elut_path}: {e}")
def make_split_gen(split: str, pos_files: List[str], neg_files: List[str]) -> Iterator[dict]:
with tqdm(unit="ex", desc=f"{split}: examples") as pbar:
def emit_for_pair(a: str, b: str, label: str) -> Iterator[dict]:
for exp_id, (_fracs, table) in elut_tables.items():
ta = table.get(a)
tb = table.get(b)
if ta is None or tb is None:
continue
yield {
"experiment_id": exp_id,
"uniprot_id1": a,
"uniprot_id2": b,
"elut_trace1": ta,
"elut_trace2": tb,
"label": label,
}
pbar.update(1)
#for pf in tqdm(pos_files, desc=f"{split}: positive files"):
for pf in pos_files:
#for a, b in tqdm(read_pair_file(pf), desc=os.path.basename(pf), leave=False):
for a, b in read_pair_file(pf):
yield from emit_for_pair(a, b, "pos")
#for nf in tqdm(neg_files, desc=f"{split}: negative files"):
for nf in neg_files:
#for a, b in tqdm(read_pair_file(nf), desc=os.path.basename(nf), leave=False):
for a, b in read_pair_file(nf):
yield from emit_for_pair(a, b, "neg")
dataset_dict = {}
for split, (pos_paths, neg_paths) in split_posneg.items():
dataset_dict[split] = Dataset.from_generator(
make_split_gen,
gen_kwargs={"split": split, "pos_files": pos_paths, "neg_files": neg_paths},
features=features,
)
return DatasetDict(dataset_dict)
def existing(paths: List[str]) -> List[str]:
return [p for p in paths if p and os.path.exists(p)]
def resolve_elut_files(base_dir: str, user_elut: Optional[List[str]]):
if user_elut:
return existing([os.path.abspath(p) for p in user_elut])
# Fall back to defaults under base_dir
candidates = [os.path.join(base_dir, f) for f in DEFAULT_ELUT_FILES]
files = existing(candidates)
if not files:
raise FileNotFoundError("No .elut files found. Provide --elut or place defaults under --base_dir")
return files
def resolve_pair_files(
base_dir: str,
train_pos: Optional[List[str]],
train_neg: Optional[List[str]],
test_pos: Optional[List[str]],
test_neg: Optional[List[str]]
) -> Dict[str, Tuple[List[str], List[str]]]:
out = {}
if any([train_pos, train_neg, test_pos, test_neg]):
if train_pos or train_neg:
out["train"] = (
existing([os.path.abspath(p) for p in train_pos or []]),
existing([os.path.abspath(p) for p in train_neg or []])
)
if test_pos or test_neg:
out["test"] = (
existing([os.path.abspath(p) for p in test_pos or []]),
existing([os.path.abspath(p) for p in test_neg or []])
)
else:
# Use defaults under base_dir
for split, (pos_path, neg_path) in DEFAULT_PAIR_FILES.items():
out[split] = (
existing([os.path.join(base_dir, pos_path)]),
existing([os.path.join(base_dir, neg_path)])
)
if not out:
raise ValueError("No pair files specified for 'pairs'.")
return out
def main():
ap = argparse.ArgumentParser(
description="Build CF-MS dataset from .elut tables and .txt files. Uses both 'pairs' and 'proteins' configurations."
)
ap.add_argument("--view", choices=["proteins", "pairs"], default="pairs",
help="Dataset configuration to build (default: pairs).")
ap.add_argument("--base_dir", type=str, default=os.getcwd(),
help="Directory to search for default files if paths not provided via '--elut' or --{test/train}_{pos/neg}")
ap.add_argument("--elut", type=str, nargs="*",
help="Path(s) to .elut files. If omitted, defaults under '--base_dir' are used.")
ap.add_argument("--train_pos", type=str, nargs="*",
help="Path(s) to positive TRAINING pairs .txt")
ap.add_argument("--train_neg", type=str, nargs="*",
help="Path(s) to negative TRAINING pairs .txt")
ap.add_argument("--test_pos", type=str, nargs="*",
help="Path(s) to positive TEST pairs .txt")
ap.add_argument("--test_neg", type=str, nargs="*",
help="Path(s) to positive TEST pairs .txt")
ap.add_argument("--out_dir", type=str, default=os.getcwd(),
help="Output directory for dataset .parquet files")
args = ap.parse_args()
base_dir = os.path.abspath(args.base_dir)
os.makedirs(args.out_dir, exist_ok=True)
elut_files = resolve_elut_files(base_dir, args.elut)
# Create individual protein elutions dataset
if args.view == "proteins":
ds = build_proteins_dataset(elut_files)
# saved as single 'train' split for consistency with original behavior
#DatasetDict({"train": ds}).to_parquet(args.out_dir)
ds.to_parquet(os.path.join(args.out_dir, "proteins_indiv_elut.parquet"))
print(f"[ok] proteins dataset saved to {args.out_dir}")
else:
split_posneg = resolve_pair_files(
base_dir,
args.train_pos, args.train_neg,
args.test_pos, args.test_neg
)
dataset_dict = build_pairs_dataset(elut_files, split_posneg)
ds_train = dataset_dict["train"]
ds_test = dataset_dict["test"]
ds_train.to_parquet(os.path.join(args.out_dir, "pairs_elut_TRAIN.parquet"))
ds_test.to_parquet(os.path.join(args.out_dir, "pairs_elut_TEST.parquet"))
# quick counts
for split, ds in dataset_dict.items():
print(f"[ok] {split}: {len(ds):,} examples")
print(f"[ok] pairs dataset saved to {args.out_dir}")
if __name__ == "__main__":
main()
|