Update README.md

README.md

@@ -333,7 +333,104 @@ sc.pl.violin(adata, ['PBX1', 'KRT18', 'COX6C'], groupby='Space')
 
 ### 3.3 How to selecte different methods by hand
 
+Sometimes, particularly when it involves known prior knowledge (such as morphological knowledge), users may wish to manually filter the results of de-identification. Space retains an interface for manual screening. Here, we provide a specific code example using the MERFISH dataset to demonstrate how to perform manual filtering.
+
+```python
+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
+slide_id = "19"
+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(f"./Data/Mouse_hippocampus_MERFISH/hip_adata-0.{slide_id}.h5ad")
+adata.var_names_make_unique()
+
+gt = adata.obs["ground_truth"]
+k = 8  # n_clusters
+epochs = 300
+seed = 666
+alpha = 4
+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, "random_seed": 4},
+    run_Leiden: {"input_adata": adata, "random_state": 0},
+    run_MENDER: {"input_adata": adata, "n_cluster": k, "scale": 2, "radius": 6, "random_seed": 2},
+    run_SCANPY: {"input_adata": adata, "resolution": 2, "random_state": 3},
+    run_SEDR: {"input_adata": adata, "n_cluster": k, "random_seed": 0},
+    run_SpaceFlow: {"input_adata": adata, "n_neighbors": 50, "random_seed": 7},
+    run_STAGATE: {"input_adata": adata, "n_cluster": k, "rad_cutoff": 60, "random_seed": 1},
+    run_stGCL: {
+        "input_adata": adata,
+        "n_cluster": k,
+        "radius": 70,
+        "epoch": 800,
+        "rad_cutoff": 60,
+        "seed": 0,
+    },  ##?
+    run_stLearn: {"input_adata": adata, "n_cluster": k, "random_state": 0},
+    run_SpaGCN: {"input_adata": adata, "seed": 2},
+}
+
+# To save time and ensure the stability of the results, we can use the results we obtained in advance:
+mul_reults = pd.read_csv(f"./Data/Mouse_hippocampus_MERFISH/result{slide_id}.csv", header=0, index_col=0)
+mul_reults = mul_reults.iloc[:, 2:]
+
+plot_results_ari(mul_reults)
+
+# remove methods by hand
+mul_reults = mul_reults.drop("SCANPY", axis=1)
+mul_reults = mul_reults.drop("SEDR", axis=1)
+mul_reults = mul_reults.drop("Graphst", axis=1)
+mul_reults = mul_reults.drop("Leiden", axis=1)
+mul_reults = mul_reults.drop("MENDER", axis=1)
+mul_reults = mul_reults.drop("SpaGCN", axis=1)
+mul_reults = mul_reults.drop("stLearn", axis=1)
+
+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()
+sc = SpectralClustering(n_clusters=k, affinity="precomputed", random_state=epochs)
+labels = sc.fit_predict(con_martix)
+adata.obs["consensus"] = labels
+ari = adjusted_rand_score(labels, adata.obs["ground_truth"].values)
+print(ari)
+```
+
+The final ARI is **0.674**.
 
 
 ## 4 Citation
 
+Coming soon...