<jupyter_start><jupyter_text>PC3<jupyter_code>raw_Y = pd.read_pickle('/home/shaoheng/Documents/data/McDavid/pc3_df.pkl').T

cpt = pd.read_pickle('/home/shaoheng/Documents/data/McDavid/pc3_cpt.pkl').values
print("Original dimesion %d cells x %d genes." % raw_Y.shape)
print(f"G0/G1 {sum(cpt == 'g0/g1')}, S {sum(cpt == 's')}, G2/M {sum(cpt == 'g2/m')}")

Y = scale(raw_Y)


pca_model = PCA(5)
pca_loads = pca_model.fit_transform(Y)
mat2hdf(pca_loads, '/home/shaoheng/Documents/data/McDavid/pc3-pca.h5')<jupyter_output>Original dimesion 361 cells x 253 genes.
G0/G1 [85], S [141], G2/M [135]<jupyter_text>MB<jupyter_code>raw_Y = pd.read_pickle('/home/shaoheng/Documents/data/McDavid/mb_df.pkl').T

cpt = pd.read_pickle('/home/shaoheng/Documents/data/McDavid/mb_cpt.pkl').values
print("Original dimesion %d cells x %d genes." % raw_Y.shape)
print(f"G0/G1 {sum(cpt == 'g0/g1')}, S {sum(cpt == 's')}, G2/M {sum(cpt == 'g2/m')}")

Y = scale(raw_Y)


pca_model = PCA(5)
pca_loads = pca_model.fit_transform(Y)
mat2hdf(pca_loads, '/home/shaoheng/Documents/data/McDavid/mb-pca.h5')<jupyter_output>Original dimesion 342 cells x 253 genes.
G0/G1 [123], S [103], G2/M [116]<jupyter_text>PC3<jupyter_code>raw_Y = pd.read_pickle('/home/shaoheng/Documents/data/McDavid/h9_df.pkl').T

cpt = pd.read_pickle('/home/shaoheng/Documents/data/McDavid/h9_cpt.pkl').values
print("Original dimesion %d cells x %d genes." % raw_Y.shape)
print(f"G0/G1 {sum(cpt == 'g0/g1')}, S {sum(cpt == 's')}, G2/M {sum(cpt == 'g2/m')}")

Y = scale(raw_Y)


pca_model = PCA(5)
pca_loads = pca_model.fit_transform(Y)
mat2hdf(pca_loads, '/home/shaoheng/Documents/data/McDavid/h9-pca.h5')<jupyter_output>Original dimesion 227 cells x 253 genes.
G0/G1 [66], S [68], G2/M [93]
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{'chain_length': '3', 'dir': 'jupyter-structured-clean-dedup', 'id': '615016', 'license': 'MIT', 'n_tokens_mistral': 859, 'n_tokens_neox': 820, 'n_words': 121, 'path': 'tests/comparisons/PCA-McDavid.ipynb', 'repo_name': 'lshh125/cyclum', 'size': '1729'}