init

README.md

@@ -1,3 +1,20 @@
 ---
 license: artistic-2.0
 ---
+
+# Metagenomic curated data
+This is a python repack of the curated data from the [Metagenomic Data Repository](https://waldronlab.io/curatedMetagenomicData/).
+
+Please refer to the [study list](https://experimenthub.bioconductor.org/package/curatedMetagenomicData) and [study metadata](https://waldronlab.io/curatedMetagenomicData/articles/available-studies.html) for the list of available datasets.
+
+## Sample usage
+```python
+ds = datasets.load_dataset("wwydmanski/metagenomic_curated", "EH1914")
+X = np.array(ds['train']['features'])
+y = np.array([x['study_condition'] for x in ds['train']['metadata']])
+```
+
+## Finding a relevant dataset EHID
+The easiest way to find an interesting study is via [study metadata](https://waldronlab.io/curatedMetagenomicData/articles/available-studies.html). After that, you can find corresponding EHIDs by referring on the https://experimenthub.bioconductor.org/title/{study_name} page.
+
+Let's say that the ThomasAM_2018a study piqued your curiosity - it means that you will be able to find all relevant datasets on the [https://experimenthub.bioconductor.org/title/ThomasAM_2018a](https://experimenthub.bioconductor.org/title/ThomasAM_2018a) website.

metagenomic_curated.py

@@ -0,0 +1,102 @@
+#%%
+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 open("index.tsv", "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']])
+
+# %%