update spatial metric

Author: littlecabiria

src/metrics/ks_statistic_spatial/config.vsh.yaml

@@ -38,6 +38,36 @@ info:
       min: -Inf
       max: +Inf
       maximize: false
+    - name: ks_statistic_transition_scalef
+      label: Frobenius norm of transition matrix
+      summary: Frobenius norm of the transition matrix.
+      description: |
+        The Frobenius norm of the difference between two matrices is calculated to assess the closeness of the spatial pattern of cell types between the simulated and real data.
+      references:
+        doi: 10.1201/9780429485572
+      min: -Inf
+      max: +Inf
+      maximize: false
+    - name: ks_statistic_central_score_scalef
+      label: Frobenius norm of centralized score
+      summary: Frobenius norm of the centralized score matrix.
+      description: |
+        The Frobenius norm of the difference between two matrices is calculated to assess the closeness of the spatial pattern of cell types between the simulated and real data.
+      references:
+        doi: 10.1201/9780429485572
+      min: -Inf
+      max: +Inf
+      maximize: false
+    - name: ks_statistic_enrichment_scalef
+      label: Frobenius norm of neighborhood enrichment
+      summary: Frobenius norm of the neighborhood enrichment.
+      description: |
+        The Frobenius norm of the difference between two matrices is calculated to assess the closeness of the spatial pattern of cell types between the simulated and real data.
+      references:
+        doi: 10.1201/9780429485572
+      min: -Inf
+      max: +Inf
+      maximize: false
 
 resources:
   - type: python_script

src/metrics/ks_statistic_spatial/script.py

@@ -7,8 +7,8 @@
 
 ## VIASH START
 par = {
-  "input_spatial_dataset": "resources_test/spatialsimbench_mobnew/dataset_sp.h5ad",
-  "input_simulated_dataset": "resources_test/spatialsimbench_mobnew/simulated_dataset.h5ad",  
+  "input_spatial_dataset": "temp_brain.zinbwave.ks_statistic_sc_features/_viash_par/input_spatial_dataset_1/output_sp.h5ad",
+  "input_simulated_dataset": "temp_brain.zinbwave.ks_statistic_sc_features/_viash_par/input_simulated_dataset_1/brain.zinbwave.generate_sim_spatialcluster.output_sp.h5ad",  
   "output": "output.h5ad"
 }
 meta = {
@@ -86,8 +86,8 @@ def get_trans(adata=None, ct=None):
 target_enrich_sim = input_simulated_dataset.uns["celltype_nhood_enrichment"]["zscore"]
 target_enrich_scale_sim = target_enrich_sim/np.max(target_enrich_sim)
 
-#error_enrich = np.linalg.norm(target_enrich_sim - target_enrich_real)
-#error_enrich_scale = np.linalg.norm(target_enrich_scale_sim - target_enrich_scale_real)
+error_enrich = np.linalg.norm(target_enrich_sim - target_enrich_real)
+# error_enrich_scale = np.linalg.norm(target_enrich_scale_sim - target_enrich_scale_real)
 
 target_enrich_real_ds = target_enrich_real.flatten()
 target_enrich_sim_ds = target_enrich_sim.flatten()
@@ -97,7 +97,8 @@ def get_trans(adata=None, ct=None):
 
 real_central_real = np.array(input_spatial_dataset.uns["celltype_centrality_scores"])
 real_central_sim = np.array(input_simulated_dataset.uns["celltype_centrality_scores"])
-    
+
+error_central = np.linalg.norm(real_central_sim - real_central_real)
 real_central_real_ds = real_central_real.flatten()
 real_central_sim_ds = real_central_sim.flatten()
 ks_central, p_value = ks_2samp(real_central_real_ds, real_central_sim_ds)
@@ -110,7 +111,7 @@ def get_trans(adata=None, ct=None):
 transition_matrix_real = get_trans(adata=input_spatial_dataset, ct=real)
 transition_matrix_sim = get_trans(adata=input_simulated_dataset, ct=sim)
 
-# error = np.linalg.norm(transition_matrix_sim - transition_matrix_real)
+error_tran = np.linalg.norm(transition_matrix_sim - transition_matrix_real)
 transition_matrix_real_ds = transition_matrix_real.flatten()
 transition_matrix_sim_ds = transition_matrix_sim.flatten()
 ks_stat_error, p_value = ks_2samp(transition_matrix_real_ds, transition_matrix_sim_ds)
@@ -119,13 +120,21 @@ def get_trans(adata=None, ct=None):
 uns_metric_ids = [
   "ks_statistic_transition_matrix",
   "ks_statistic_central_score",
-  "ks_statistic_enrichment"
+  "ks_statistic_enrichment",
+
+  "ks_statistic_enrichment_scalef",
+  "ks_statistic_central_score_scalef",
+  "ks_statistic_transition_scalef"
 ]
 
 uns_metric_values = [
   ks_stat_error,
   ks_central,
-  ks_enrich
+  ks_enrich,
+
+  error_enrich,
+  error_central,
+  error_tran
 ]
 
 print("Write output AnnData to file", flush=True)