|
|
|
from typing import Any |
|
import pyreadr |
|
import pandas as pd |
|
import numpy as np |
|
import sqlite3 |
|
import requests |
|
import datasets |
|
import tempfile |
|
import rdata |
|
import json |
|
from typing import Any |
|
|
|
|
|
sqlite_url = "https://experimenthub.bioconductor.org/metadata/experimenthub.sqlite3" |
|
DATA_URL = "https://bioconductorhubs.blob.core.windows.net/experimenthub/curatedMetagenomicData/" |
|
RDATA_URL = "https://huggingface.co/datasets/wwydmanski/metagenomic_curated/resolve/main/sampleMetadata.rda" |
|
|
|
CITATION = """\ |
|
Pasolli E, Schiffer L, Manghi P, Renson A, Obenchain V, Truong D, Beghini F, Malik F, Ramos M, Dowd J, Huttenhower C, Morgan M, Segata N, Waldron L (2017). Accessible, curated metagenomic data through ExperimentHub. Nat. Methods, 14 (11), 1023-1024. ISSN 1548-7091, 1548-7105, doi: 10.1038/nmeth.4468. |
|
""" |
|
|
|
|
|
|
|
def get_metadata(): |
|
ehids = [] |
|
descriptions = [] |
|
with tempfile.NamedTemporaryFile(delete=False) as tmpfname: |
|
r = requests.get("https://huggingface.co/datasets/wwydmanski/metagenomic_curated/raw/main/index.tsv", allow_redirects=True) |
|
open(tmpfname.name, 'wb').write(r.content) |
|
|
|
with open(tmpfname.name, "r") as f: |
|
for line in f: |
|
ehid, desc = line.split("\t") |
|
ehids.append(ehid) |
|
descriptions.append(desc) |
|
return ehids, descriptions |
|
|
|
|
|
class MetagenomicCurated(datasets.GeneratorBasedBuilder): |
|
"""Metagenomic Curated Data""" |
|
|
|
ehids, descriptions = get_metadata() |
|
BUILDER_CONFIGS = [ |
|
datasets.BuilderConfig(name=ehid, |
|
version=datasets.Version("1.0.0"), |
|
description=d.strip()) |
|
for ehid, d in zip(ehids, descriptions) |
|
] |
|
|
|
def __call__(self, *args: Any, **kwds: Any) -> Any: |
|
return super().__call__(*args, **kwds) |
|
|
|
def _info(self): |
|
try: |
|
features = { |
|
i: datasets.Value("float32") for i in self.features |
|
} |
|
except: |
|
features = {} |
|
return datasets.DatasetInfo( |
|
description=self.config.description, |
|
citation=CITATION, |
|
homepage="https://waldronlab.io/curatedMetagenomicData/index.html", |
|
license="https://www.r-project.org/Licenses/Artistic-2.0", |
|
|
|
) |
|
|
|
def _split_generators(self, dl_manager): |
|
json_url = f"https://experimenthub.bioconductor.org/ehid/{self.config.name}" |
|
r = requests.get(json_url, allow_redirects=True) |
|
metadata = json.loads(r.content) |
|
url = metadata['location_prefix']+metadata['rdatapaths'][0]['rdatapath'] |
|
|
|
data_fname: str = dl_manager.download(url) |
|
rdata_path: str = dl_manager.download(RDATA_URL) |
|
|
|
return [ |
|
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_fname, "rdata_path": rdata_path}), |
|
] |
|
|
|
def _generate_examples(self, filepath, rdata_path): |
|
parsed = rdata.parser.parse_file(filepath) |
|
converted = rdata.conversion.convert(parsed) |
|
expressions = list(converted.values())[0].assayData['exprs'] |
|
|
|
data_df = expressions.to_pandas().T |
|
self.features = data_df.columns |
|
|
|
study_name = list(converted.keys())[0].split(".")[0] |
|
|
|
meta = pyreadr.read_r(rdata_path)['sampleMetadata'] |
|
metadata = meta.loc[meta['study_name'] == study_name].set_index('sample_id') |
|
|
|
for idx, (i, row) in enumerate(data_df.iterrows()): |
|
try: |
|
md = {i: str(j) for i, j in metadata.loc[i].to_dict().items()} |
|
except KeyError: |
|
md = {} |
|
yield idx, { |
|
"features": row.to_dict(), |
|
"metadata": md |
|
} |
|
|
|
|
|
if __name__=="__main__": |
|
ds = datasets.load_dataset("./metagenomic_curated.py", "EH1726") |
|
X = np.array([list(i.values()) for i in ds['train']['features']]) |
|
y = np.array([x['study_condition'] for x in ds['train']['metadata']]) |
|
|
|
|
|
|
