Create cache folder

multi_omics_transcript_expression.py

@@ -125,17 +125,6 @@ LABELS_V2 = [
     "Whole Blood",
 ]
 
-# Add after LABELS_V2 definition
-LABELS_LIGHT = [
-    "Adipose Tissue",
-    "Brain",
-    "Heart",
-    "Liver",
-    "Lung",
-    "Muscle",
-    "Pancreas",
-    "Skin",
-]
 
 class GenomicLRATaskHandler(ABC):
     """
@@ -213,7 +202,6 @@ class GenomicLRATaskHandler(ABC):
 
         if not os.path.exists(file_complete_path):
             if not os.path.exists(file_complete_path + ".gz"):
-                os.makedirs(os.path.dirname(file_complete_path), exist_ok=True)
                 with tqdm(
                     unit="B",
                     unit_scale=True,
@@ -243,28 +231,31 @@ class TranscriptExpressionHandler(GenomicLRATaskHandler):
         sequence_length: int = DEFAULT_LENGTH,
         filter_out_sequence_length: int = DEFAULT_FILTER_OUT_LENGTH,
         expression_method: str = "read_counts_old",
-        light_version: bool = False,
         **kwargs,
     ):
         """
-        Creates a new handler for the Transcript Expression Prediction Task.
+        Creates a new handler for the Transcrpt Expression Prediction Task.
         Args:
             sequence_length: Length of the sequence around the TSS_CAGE start site
-            light_version: If True, uses a smaller subset of tissues and fewer samples
+        Instance Vars:
+            reference_genome: The Fasta extracted reference genome.
+            coordinate_csv_file: The csv file that stores the coordinates and filename of the target
+            labels_csv_file: The csv file that stores the labels with one sample per row.
+            sequence_length: Sequence length for this handler.
+                counts.
         """
         self.reference_genome = None
         self.coordinate_csv_file = None
         self.labels_csv_file = None
-        self.light_version = light_version
         self.sequence_length = sequence_length
         self.filter_out_sequence_length = filter_out_sequence_length
 
-        if self.filter_out_sequence_length is not None:
-            assert isinstance(self.filter_out_sequence_length, int)
+        if filter_out_sequence_length is not None:
+            assert isinstance(filter_out_sequence_length, int)
             assert (
-                self.sequence_length <= self.filter_out_sequence_length
-            ), f"{self.sequence_length=} > {self.filter_out_sequence_length=}"
-        assert isinstance(self.sequence_length, int)
+                sequence_length <= filter_out_sequence_length
+            ), f"{sequence_length=} > {filter_out_sequence_length=}"
+        assert isinstance(sequence_length, int)
 
     def get_info(self, description: str) -> DatasetInfo:
         """
@@ -294,7 +285,9 @@ class TranscriptExpressionHandler(GenomicLRATaskHandler):
             }
         )
         return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
             description=description,
+            # This defines the different columns of the dataset and their types
             features=features,
         )
 
@@ -327,30 +320,31 @@ class TranscriptExpressionHandler(GenomicLRATaskHandler):
         """
         df = pd.read_csv(self.df_csv_file)
         df = df.loc[df["chr"] != "chrMT"]
-        
-        # Use light version labels if specified
-        labels_name = LABELS_LIGHT if self.light_version else LABELS_V1
+        labels_name = LABELS_V1
 
         split_df = df.loc[df["split"] == split]
-        
-        # For light version, take only a subset of the data
-        if self.light_version:
-            split_df = split_df.sample(n=min(1000, len(split_df)), random_state=42)
 
         norm_values_df = pd.read_csv(self.normalization_values_csv_file)
-        
-        # Select appropriate columns based on version
-        label_columns = [f"m_t_{tissue}" for tissue in labels_name]
-        m_t = norm_values_df[label_columns].to_numpy().reshape(-1)
-        
-        label_columns = [f"sigma_t_{tissue}" for tissue in labels_name]
-        sigma_t = norm_values_df[label_columns].to_numpy().reshape(-1)
-        
-        label_columns = [f"m_g_{tissue}" for tissue in labels_name]
-        m_g = norm_values_df[label_columns].to_numpy().reshape(-1)
-        
-        label_columns = [f"sigma_g_{tissue}" for tissue in labels_name]
-        sigma_g = norm_values_df[label_columns].to_numpy().reshape(-1)
+        m_t = (
+            norm_values_df[[f"m_t_{tissue}" for tissue in LABELS_V1]]
+            .to_numpy()
+            .reshape(-1)
+        )
+        sigma_t = (
+            norm_values_df[[f"sigma_t_{tissue}" for tissue in LABELS_V1]]
+            .to_numpy()
+            .reshape(-1)
+        )
+        m_g = (
+            norm_values_df[[f"m_g_{tissue}" for tissue in LABELS_V1]]
+            .to_numpy()
+            .reshape(-1)
+        )
+        sigma_g = (
+            norm_values_df[[f"sigma_g_{tissue}" for tissue in LABELS_V1]]
+            .to_numpy()
+            .reshape(-1)
+        )
 
         key = 0
         for idx, coordinates_row in split_df.iterrows():
@@ -362,7 +356,7 @@ class TranscriptExpressionHandler(GenomicLRATaskHandler):
                 start = coordinates_row["start"] - 1  # -1 since vcf coords are 1-based
 
             chromosome = coordinates_row["chr"]
-            labels_row = coordinates_row[labels_name]
+            labels_row = coordinates_row[LABELS_V1]
             padded_sequence = pad_sequence(
                 chromosome=self.reference_genome[chromosome],
                 start=start,
@@ -508,4 +502,4 @@ def pad_sequence(
 
     if negative_strand:
         return chromosome[start:end].reverse.complement.seq
-    return chromosome[start:end].seq
+    return chromosome[start:end].seq