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yolksac_human.py

@@ -1,161 +1,89 @@
-# 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 numpy as np
-
 import datasets
-CITATION = """
-Test
-"""
 
-DESCRIPTION = """
-Test
+# parameters per dataset
+CITATION = """
+ Simone Webb, Muzlifah Haniffa & Emily Stephenson (2022). 
+ Human fetal yolk sac scRNA-seq data (sample ID: F158 for Haniffa Lab; 16099 for HDBR). 
+ BioStudies, E-MTAB-11673. Retrieved from https://www.ebi.ac.uk/biostudies/arrayexpress/studies/E-MTAB-11673 
 """
+URL = "https://www.ebi.ac.uk/biostudies/arrayexpress/studies/E-MTAB-11673"
+DESCRIPTION = """Investigating the blood, immune and stromal cells present in a human fetal embryo in a 
+world first single cell transcriptomic atlas. The embryo was dissected into 12 coronal sections, yolk 
+sac, and yolk sac stalk. Live single cells sorted, with cell suspension then undergoing 10x chromium 
+5 prime scRNA-seq. This accession contains the yolk sac and yolk sac stalk data from this embryo. 
+A matched accession contains the coronal section data. Lane "WS_wEMB12142156" (from yolk sac) was excluded
+from downstream analysis due to low fraction reads in cells post-CellRanger QC. Termination procedure for 
+this embryo was medical. The F158_[features...barcodes...matrix].[tsv...mtx].gz files attached to this 
+accession represent raw count data from all the 10x lanes in this accession combined, and as output from
+CellRanger filtered matrices (CellRanger version 6.0.1 using human reference genome GRCh38-2020-A). 
+One set of count matrices relates to the yolk sac data, and one set of count matrices relates to the yolk sac stalk data."""
+RAW_COUNTS = "X"
+DATA_URL = "./data/17_04_24_YolkSacRaw_F158_WE_annots.h5ad"
+FEATURES_TO_INCLUDE = ['LVL1', 'LVL2', 'LVL3']
 
-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
-        self.species = None
-
-
-
-
-# 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', 'LVL2', 'LVL3'],
-            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"))}
+        self.batch = 1000
 
-        # features = {"raw_counts": datasets.features.Sequence(feature={"gene":datasets.Value("string"),"count":datasets.Value("int32")})}
-        # features = {"raw_counts": 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:
+        # create a dictionary of features where raw_counts are ints and the rest are strings
+        features = {"raw_counts": datasets.features.Sequence(datasets.features.Value("int32")),"rows": datasets.features.Sequence(datasets.features.Value("int32")),"size":datasets.Value("int32")}
+        for feature in FEATURES_TO_INCLUDE:
+            if not features.get(feature):
                 features[feature] = datasets.Value("string")
 
-        # features["gene_names"] = datasets.Sequence(datasets.Value("string"))
-
         return datasets.DatasetInfo(
-            description= self.config.description,
-            features=None, #datasets.Features(features),
-            homepage=self.config.url,
-            citation=self.config.citation,
+            description = DESCRIPTION,
+            features = datasets.Features(features), 
+            homepage = URL,
+            citation = CITATION
         )
 
     def _split_generators(self, dl_manager):
-        self.anndata_file = dl_manager.download_and_extract(self.config.data_url)
+        self.anndata_file = dl_manager.download_and_extract(DATA_URL)
+        adata = ad.read_h5ad(self.anndata_file, backed = "r")
+        demarcation = int(len(adata)*80/100)
         
-
         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},    
+                name = datasets.Split.TRAIN,
+                gen_kwargs = {"split": "train", "adata": adata[:demarcation], "batch_size":self.batch},
+            ),
+            datasets.SplitGenerator(
+                name = datasets.Split.TEST,
+                gen_kwargs = {"split": "test", "adata": adata[demarcation:], "batch_size":self.batch},
             )
         ]
 
-    def _generate_examples(self, expression_file, split):
-
-        # genes = pd.read_csv(gene_names_file)
-        adata = ad.read_h5ad(expression_file)
-        self.genes_list = adata.var.index.str.lower().tolist()
-
-
-        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):
+    def _generate_tables(self, adata, batch_size, split):
         idx = 0
-        adata = ad.read_h5ad(expression_file,backed='r')
-        genes = adata.var_names.str.lower().to_list()
-
-        features = {"raw_counts": datasets.features.Sequence(datasets.features.Value("int32"),id = ",".join(adata.var.index.str.lower().tolist()))}
-        for feature in self.config.features:
-            if features.get(feature,None) is None:
-                features[feature] = datasets.Value("string")
-
-        self.info.features = datasets.Features(features)
         
+        # save the gene names as the id for the raw_counts feature
+        self.info.features["raw_counts"].id = f"{','.join(adata.var.index.tolist())}"
+
+        # iterate over the data in batches 
+        for batch in range(0, adata.shape[0], batch_size):
+
+            # raw counts
+            if RAW_COUNTS == "X":
+                chunk = adata.X[batch:batch+batch_size].tolil().astype('int32')
+            elif RAW_COUNTS == "raw.X":
+                chunk = adata.raw.X[batch:batch+batch_size].tolil().astype('int32')
+            else:
+                raise("Not valid raw_counts")
+            df = pd.DataFrame([chunk.data,chunk.rows]).T
+            df.columns = ['raw_counts','rows']  
+            df['size'] = chunk.shape[1]
+         
+            # other features are all mapped to a list of strings
+            for feature in FEATURES_TO_INCLUDE:
+                df[feature] = list(map(str, adata.obs[feature][batch:batch+batch_size].tolist()))
 
-        # self.info.features['gene_names'] = datasets.features.ClassLabel(names = genes)
-        
-        # self.info.description   = adata.var.index.str.lower().tolist() #"+".join(adata.var.index.str.lower().tolist())
-        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"]]
-            ## We create a dummy column with all the names of the genes as list. We don't use this as value since this would unnecessarily increase the size of the dataset
-            ## Another option would be to replace the description with the list of genes 
-            # df[",".join(adata.var.index.str.lower().tolist())] = True
-            # df['gene_names'] = True
-            
-            for feature in self.config.features:
-                if feature != "raw_counts":
-                    df[feature] = adata.obs[feature][batch:batch+batch_size].tolist()
-            
-            # df['gene_names'] = [adata.var.index.str.lower().tolist()]*batch_size
-            # print(df)
             pa_table = pa.Table.from_pandas(df)
             yield idx, pa_table
             idx += 1