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#%%
import pyreadr
import pandas as pd
import numpy as np
import sqlite3
import requests
import datasets
import tempfile
import rdata
import json
#%%
sqlite_url = "https://experimenthub.bioconductor.org/metadata/experimenthub.sqlite3"
DATA_URL = "https://bioconductorhubs.blob.core.windows.net/experimenthub/curatedMetagenomicData/"
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():
# with tempfile.NamedTemporaryFile(delete=False) as tmpfname:
# r = requests.get(sqlite_url, allow_redirects=True)
# open(tmpfname.name, 'wb').write(r.content)
# db = sqlite3.connect(tmpfname.name)
# cursor = db.cursor()
# cur = cursor.execute("""SELECT * FROM resources""")
# ehid = []
# descriptions = []
# for row in cur.fetchall():
# if "curatedMetagenomicData" in str(row[-1]):
# ehid.append(row[1])
# descriptions.append(row[7])
# return ehid, descriptions
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 _info(self):
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)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_fname}),
]
def _generate_examples(self, filepath):
parsed = rdata.parser.parse_file(filepath)
converted = rdata.conversion.convert(parsed)
expressions = list(converted.values())[0].assayData['exprs']
data_df = expressions.to_pandas().T
study_name = list(converted.keys())[0].split(".")[0]
meta = pyreadr.read_r("sampleMetadata.rda")['sampleMetadata']
metadata = meta.loc[meta['study_name'] == study_name].set_index('sample_id')
for idx, (i, row) in enumerate(data_df.iterrows()):
yield idx, {
"features": row.values,
"metadata": {i: str(j) for i, j in metadata.loc[i].to_dict().items()}
}
# %%
if __name__=="__main__":
ds = datasets.load_dataset("./metagenomic_curated.py", "EH1914")
X = np.array(ds['train']['features'])
y = np.array([x['study_condition'] for x in ds['train']['metadata']])
# %%
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