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 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
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+data/17_04_24_YolkSacRaw_F158_WE_annots.h5ad filter=lfs diff=lfs merge=lfs -text

data/17_04_24_YolkSacRaw_F158_WE_annots.h5ad

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6b97cd1a83ed6ac94d40ce86f41c8d6298af9134768133eaa71f72ff548d85ba
+size 552929816

yolksac_human.py

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+# coding=utf-8
+# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Lint as: python3
+"""The RNA Expression Baseclass."""
+
+
+import json
+import os
+import anndata as ad
+import pyarrow as pa
+import pandas as pd
+
+import datasets
+CITATION = """
+Test
+"""
+
+DESCRIPTION = """
+Test
+"""
+
+class RNAExpConfig(datasets.BuilderConfig):
+    """BuilderConfig for RNAExpConfig."""
+
+    def __init__(self, features, data_url, citation, url, raw_counts="X", **kwargs):
+        """BuilderConfig for RNAExpConfig.
+        Args:
+          features: `list[string]`, list of the features that will appear in the
+            feature dict. Should not include "label".
+          data_url: `string`, url to download the zip file from.
+          citation: `string`, citation for the data set.
+          url: `string`, url for information about the data set.
+
+          **kwargs: keyword arguments forwarded to super.
+        """
+        # Version history:
+        # 0.0.1: Initial version.
+        super(RNAExpConfig, self).__init__(version=datasets.Version("0.0.1"), **kwargs)
+        self.features = features
+        self.data_url = data_url
+        self.citation = citation
+        self.url = url
+        self.raw_counts = raw_counts # Could be raw.X
+        self.batch = 1000
+
+
+
+
+# class RNAExp(datasets.GeneratorBasedBuilder):
+class RNAExp(datasets.ArrowBasedBuilder):
+    """RNA Expression Baseclass."""
+
+    def _info(self):
+        self.config = RNAExpConfig(
+            name="human_yolk_sac",
+            description = DESCRIPTION,
+            features=["raw_counts","LVL1"],
+            raw_counts = "X",
+            data_url="./data/17_04_24_YolkSacRaw_F158_WE_annots.h5ad",
+            citation=CITATION,
+            url="https://www.ebi.ac.uk/biostudies/arrayexpress/studies/E-MTAB-11673")
+
+        features = {"raw_counts": datasets.features.Sequence(feature=datasets.Value("int32"))}
+        # features = {gene: datasets.Value("int32") for gene in adata.var.index.str.lower().tolist()}
+        for feature in self.config.features:
+            if features.get(feature,None) is None:
+                features[feature] = datasets.Value("string")
+
+
+
+        return datasets.DatasetInfo(
+            description= self.config.description,
+            features=datasets.Features(features),
+            homepage=self.config.url,
+            citation=self.config.citation,
+        )
+
+    def _split_generators(self, dl_manager):
+        # self.gene_names_file = dl_manager.download_and_extract(
+        #     "data/gene_names.csv"
+        # )
+        self.anndata_file = dl_manager.download_and_extract(self.config.data_url)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"split": "train","expression_file": self.anndata_file,"batch_size":self.config.batch},#,"gene_names_file": self.gene_names_file},    
+            )
+        ]
+
+    def _generate_examples(self, expression_file, split):
+
+        # genes = pd.read_csv(gene_names_file)
+        adata = ad.read_h5ad(expression_file)
+        if self.config.raw_counts =="X":
+            X = adata.X
+        else:
+            X = adata.var[raw_counts]
+        num_cells = X.shape[0]
+        for _id,cell in enumerate(X):
+            example = {"raw_counts": cell.toarray().flatten()}
+
+            for feature in self.config.features:
+                if example.get(feature,None) is None:
+                    example[feature] = adata.obs[feature][_id]
+
+            yield _id,example
+
+    def _generate_tables(self, expression_file,batch_size,split):
+        idx = 0
+        adata = ad.read_h5ad(expression_file)
+        for batch in range(0,adata.shape[0],batch_size):
+            chunk = adata.X[batch:batch+batch_size].todense().astype('int32')
+            df = pd.DataFrame(chunk,columns=adata.var.index.str.lower())
+            df["raw_counts"] = [x for x in df.to_numpy()]
+            df = df[["raw_counts"]]
+            for feature in self.config.features:
+                if feature != "raw_counts":
+                    df[feature] = adata.obs[feature][batch:batch+batch_size].tolist()
+            print(df.shape)
+            pa_table = pa.Table.from_pandas(df)
+            yield idx, pa_table
+            idx += 1