update

README.md

@@ -118,8 +118,7 @@ from huggingface_hub import hf_hub_download
 import os
 
 # Define the Hugging Face repository ID and the local directory for downloads
-# REMINDER: This is the suggested new repository name for Human Cornea data.
-HF_REPO_ID = "longevity-db/human-cornea-snRNAseq" # <<<--- CONFIRM THIS IS YOUR ACTUAL REPO NAME
+HF_REPO_ID = "longevity-db/human-cornea-snRNAseq"
 LOCAL_DATA_DIR = "downloaded_human_cornea_data"
 
 os.makedirs(LOCAL_DATA_DIR, exist_ok=True)
@@ -167,37 +166,6 @@ print("Gene metadata shape:", df_gene_metadata.shape)
 print("PCA explained variance shape:", df_pca_explained_variance.shape)
 print("HVG metadata shape:", df_hvg_metadata.shape)
 print("Gene statistics shape:", df_gene_stats.shape)
-
-
-# Example: Prepare data for an age prediction model
-# IMPORTANT: You need to inspect `df_cell_metadata.columns` to find the actual age and cell type columns.
-print("\nAvailable columns in cell_metadata.parquet (df_cell_metadata.columns):")
-print(df_cell_metadata.columns.tolist())
-
-# --- USER ACTION REQUIRED ---
-# Based on your inspection of GSE186433_metadata_percell.csv, update these names:
-age_column_name = 'age' # <<<--- VERIFY THIS NAME (e.g., 'age_years', 'donor_age_in_years')
-cell_type_column_name = 'cell_type' # <<<--- VERIFY THIS NAME (e.g., 'CellType', 'Annotation', 'sub_type')
-# --- END USER ACTION REQUIRED ---
-
-
-# Example: Using age for a prediction task
-if age_column_name in df_cell_metadata.columns:
-    X_features_age_prediction = df_pca_embeddings # Or df_umap_embeddings, or df_expression (if manageable)
-    y_labels_age_prediction = df_cell_metadata[age_column_name]
-    print(f"\nPrepared X (features) for age prediction with shape {X_features_age_prediction.shape} and y (labels) with shape {y_labels_age_prediction.shape}")
-else:
-    print(f"\nWarning: Column '{age_column_name}' not found in cell metadata for age prediction example. Please check your data.")
-
-# Example: Using cell type for a classification task
-if cell_type_column_name in df_cell_metadata.columns:
-    X_features_cell_type = df_pca_embeddings # Or df_umap_embeddings, or df_expression
-    y_labels_cell_type = df_cell_metadata[cell_type_column_name]
-    print(f"Prepared X (features) for cell type classification with shape {X_features_cell_type.shape} and y (labels) with shape {y_labels_cell_type.shape}")
-else:
-    print(f"Warning: Column '{cell_type_column_name}' not found in cell metadata for cell type classification example. Please check your data.")
-
-# This data can then be split into train/test sets and used to train various ML models.
 ```
 
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