--- a
+++ b/main.py
@@ -0,0 +1,40 @@
+import numpy as np
+import pandas as pd
+from MultiOmiVAE import MultiOmiVAE
+from MethyOmiVAE import MethyOmiVAE
+from ExprOmiVAE import ExprOmiVAE
+from plot_sactter import plot_scatter
+from classification import classification
+
+
+if __name__ == "__main__":
+    input_path = 'data/OmiVAE/PANCAN/GDC-PANCAN_'
+
+    expr_path = input_path + 'htseq_fpkm_'
+    methy_path = input_path + 'methylation450_'
+
+    # Loading data
+
+    print('Loading gene expression data...')
+    expr_df = pd.read_csv(expr_path + 'preprocessed_both.tsv', sep='\t', header=0, index_col=0)
+
+    print('Loading DNA methylation data...')
+    methy_chr_df_list = []
+    chr_id = list(range(1, 23))
+    chr_id.append('X')
+    # Loop among different chromosomes
+    for chrom in chr_id:
+        print('Loading methylation data on chromosome ' + str(chrom) + '...')
+        methy_chr_path = methy_path + 'preprocessed_both_chr' + str(chrom) + '.tsv'
+        # methy_chr_df = pd.read_csv(methy_chr_path, sep='\t', header=0, index_col=0, dtype=all_cols_f32)
+        methy_chr_df = pd.read_csv(methy_chr_path, sep='\t', header=0, index_col=0)
+        methy_chr_df_list.append(methy_chr_df)
+
+    e_num_1 = 50
+    e_num_2 = 200
+    l_dim = 128
+
+    # Example
+    latent_code, train_acc, val_acc = MultiOmiVAE(input_path=input_path, expr_df=expr_df,
+                                                  methy_chr_df_list=methy_chr_df_list, p1_epoch_num=e_num_1,
+                                                  p2_epoch_num=e_num_2, latent_dim=l_dim, early_stopping=False)
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