--- a
+++ b/compare.py
@@ -0,0 +1,46 @@
+import numpy as np
+import pandas as pd
+from sklearn import decomposition
+from sklearn.manifold import TSNE
+import umap
+
+print('Loading data...')
+input_path = 'data/PANCAN/GDC-PANCAN_'
+sample_id = np.loadtxt(input_path + 'both_samples.tsv', delimiter='\t', dtype='str')
+
+input_df = pd.read_csv(input_path + 'preprocessed_both.tsv', sep='\t', header=0, index_col=0)
+input_df = input_df.T
+
+latent_space_dimension = 2
+
+# PCA
+print('PCA')
+pca = decomposition.PCA(n_components=latent_space_dimension)
+z = pca.fit_transform(input_df.values)
+latent_code = pd.DataFrame(z, index=sample_id)
+output_path = 'results/GDC-PANCAN_' + str(latent_space_dimension) + 'D_PCA_latent_sapce.tsv'
+latent_code.to_csv(output_path, sep='\t')
+
+# KPCA
+print('KPCA')
+kpca = decomposition.KernelPCA(n_components=latent_space_dimension, kernel='rbf')
+z = kpca.fit_transform(input_df.values)
+latent_code = pd.DataFrame(z, index=sample_id)
+output_path = 'results/GDC-PANCAN_' + str(latent_space_dimension) + 'D_KPCA_latent_sapce.tsv'
+latent_code.to_csv(output_path, sep='\t')
+
+# TSNE
+print('TSNE')
+tsne = TSNE(n_components=latent_space_dimension)
+z = tsne.fit_transform(input_df.values)
+latent_code = pd.DataFrame(z, index=sample_id)
+output_path = 'results/GDC-PANCAN_' + str(latent_space_dimension) + 'D_TSNE_latent_sapce.tsv'
+latent_code.to_csv(output_path, sep='\t')
+
+# UMAP
+print('UMAP')
+umap_reducer = umap.UMAP()
+z = umap_reducer.fit_transform(input_df.values)
+latent_code = pd.DataFrame(z, index=sample_id)
+output_path = 'results/GDC-PANCAN_' + str(latent_space_dimension) + 'D_UMAP_latent_sapce.tsv'
+latent_code.to_csv(output_path, sep='\t')