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yolksac_human.py

@@ -74,8 +74,11 @@ class RNAExp(datasets.ArrowBasedBuilder):
             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()}
+        # features = {"raw_counts": datasets.features.Sequence(feature=datasets.Value("int32"))}
+
+        # 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()}
+        features = {}
         for feature in self.config.features:
             if features.get(feature,None) is None:
                 features[feature] = datasets.Value("string")
@@ -84,16 +87,17 @@ class RNAExp(datasets.ArrowBasedBuilder):
 
         return datasets.DatasetInfo(
             description= self.config.description,
-            features=None,#datasets.Features(features),
+            features=None,#,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.gene_names_file = dl_manager.download_and_extract(
+            "./data/meta_data.csv"
+        )
         self.anndata_file = dl_manager.download_and_extract(self.config.data_url)
+        
 
         return [
             datasets.SplitGenerator(
@@ -107,6 +111,8 @@ class RNAExp(datasets.ArrowBasedBuilder):
         # 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:
@@ -124,20 +130,37 @@ class RNAExp(datasets.ArrowBasedBuilder):
     def _generate_tables(self, expression_file,batch_size,split):
         idx = 0
         adata = ad.read_h5ad(expression_file)
+        genes = adata.var_names.str.lower().to_list()
+
+        # features = {"raw_counts": datasets.features.Sequence(datasets.features.ClassLabel(names = genes))}
+        # for feature in self.config.features:
+        #     if features.get(feature,None) is None:
+        #         features[feature] = datasets.Value("string")
+        
+        # self.info.features = datasets.features.Features(features)
+
+        # 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"]]
+            # df["raw_counts"] = [x for x in df.to_numpy()]
+            # df.apply(lambda x: [(x,y) for x,y in zip(genes, x)],axis=1)
+
+            # [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[",".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