Create demo_pipeline.py

Demo/Reproduce_Scripts/demo_pipeline.py

@@ -0,0 +1,106 @@
+import os
+import scanpy as sc
+import pandas as pd
+
+os.environ["R_HOME"] = "/home/zw/software/miniforge-pypy3/envs/space/lib/R"
+import Space
+from Space.cons_func import (
+    run_GraphST,
+    run_Leiden,
+    run_MENDER,
+    run_SCANPY,
+    run_SEDR,
+    run_SpaceFlow,
+    run_SpaGCN,
+    run_STAGATE,
+    run_stGCL,
+    run_stLearn,
+)
+from sklearn.metrics import adjusted_rand_score
+from sklearn.cluster import SpectralClustering
+from Space.cons_func import get_results, get_domains
+from Space.utils import (
+    calculate_location_adj,
+    plot_results_ari,
+    get_bool_martix,
+    plot_ari_with_removal,
+)
+
+adata = sc.read_visium(
+    path="./Data/V1_Breast_Cancer_Block_A_Section_1", count_file="filtered_feature_bc_matrix.h5"
+)
+Ann_df = pd.read_csv(
+    "./Data/V1_Breast_Cancer_Block_A_Section_1/metadata.tsv",
+    sep="	",
+    header=0,
+    na_filter=False,
+    index_col=0,
+)
+adata.var_names_make_unique()
+im_re = pd.read_csv(
+    "./Data/V1_Breast_Cancer_Block_A_Section_1/image_representation/ViT_pca_representation.csv",
+    header=0,
+    index_col=0,
+    sep=",",
+)
+adata.obsm["im_re"] = im_re
+adata.obs["gt"] = Ann_df["fine_annot_type"]
+gt = adata.obs["gt"]
+k = 20
+epochs = 1000
+seed = 666
+alpha = 1
+learning_rate = 0.0001
+
+# Define a dictionary where the key is a subfunction and the value is a dictionary of parameters
+func_dict = {
+    run_GraphST: {"input_adata": adata, "n_cluster": k, "radius": 50, "random_seed": 200},
+    run_Leiden: {"input_adata": adata, "random_state": 0},
+    run_MENDER: {"input_adata": adata, "n_cluster": k, "scale": 2, "radius": 6, "random_seed": 1},
+    run_SCANPY: {"input_adata": adata, "resolution": 0.5, "random_state": 0},
+    run_SEDR: {"input_adata": adata, "n_cluster": k, "random_seed": 0},
+    run_SpaceFlow: {"input_adata": adata, "resolution": 1.5, "n_neighbors": 50, "random_seed": 8},
+    run_STAGATE: {"input_adata": adata, "n_cluster": k, "random_seed": 6},
+    run_stGCL: {
+        "input_adata": adata,
+        "n_cluster": k,
+        "radius": 70,
+        "epoch": 100,
+        "use_image": True,
+        "seed": 0,
+    },
+    run_stLearn: {
+        "input_adata": adata,
+        "n_cluster": k,
+    },
+    run_SpaGCN: {"input_adata": adata, "max_epochs": 20, "seed": 4},
+}
+
+
+collected_results = get_results(func_dict, use_multithreading=False, monitor_performance=True)
+mul_reults = get_domains(adata, collected_results)
+
+mul_reults = plot_ari_with_removal(mul_reults, 7)
+
+
+pos_similarity = calculate_location_adj(adata.obsm["spatial"], l=123)
+
+model = Space.Space(
+    get_bool_martix(mul_reults),
+    pos_similarity,
+    epochs=epochs,
+    gt=gt.values,
+    k=k,
+    seed=seed,
+    alpha=alpha,
+    beta=1,
+    learning_rate=learning_rate,
+)
+con_martix = model.train()
+sClustering = SpectralClustering(n_clusters=k, affinity="precomputed", random_state=666)
+labels = sClustering.fit_predict(con_martix)
+adata.obs["Space"] = labels
+adata.obs["Space"] = adata.obs["Space"].astype("str")
+ari = adjusted_rand_score(labels, gt.values)
+print(ari)
+