minor updates

src/control_methods/random_proportions/script.py

@@ -16,7 +16,6 @@
 print('Reading input files', flush=True)
 input_single_cell = ad.read_h5ad(par['input_single_cell'])
 input_spatial_masked = ad.read_h5ad(par['input_spatial_masked'])
-input_solution = ad.read_h5ad(par['input_solution'])
 
 print('Generate predictions', flush=True)
 label_distribution = input_single_cell.obs["cell_type"].value_counts()

src/metrics/r2/config.vsh.yaml

@@ -10,7 +10,7 @@ info:
         R2, or the “coefficient of determination”, reports the fraction of the true proportion values' variance that can be explained by the predicted proportion values. The best score, and upper bound, is 1.0. There is no fixed lower bound for the metric. The uniform/non-weighted average across all cell types/states is used to summarise performance. By default, cases resulting in a score of NaN (perfect predictions) or -Inf (imperfect predictions) are replaced with 1.0 (perfect predictions) or 0.0 (imperfect predictions) respectively.
       reference: miles2005rsquared
       documentation_url: https://scikit-learn.org/stable/modules/generated/sklearn.metrics.r2_score.html
-      repository_url: https://github.com/scikit-learn/scikit-learn/tree/5c4aa5d0d90ba66247d675d4c3fc2fdfba3c39ff
+      repository_url: https://github.com/scikit-learn/scikit-learn
       min: -inf
       max: 1
       maximize: true

src/process_dataset/dataset_simulator/config.vsh.yaml

@@ -14,43 +14,49 @@ info:
         alpha: 5
       alpha_0_5:
         alpha: 0.5
-arguments:
-  - name: "--input"
-    type: file
-    description: Single-cell reference dataset
-    direction: input
-    required: true
-    __merge__: ../../api/file_common_dataset.yaml
-  - name: "--alpha"
-    type: double
-    description: Alpha value to use for generating synthetic dataset
-    default: 1.0
-  - name: "--n_obs"
-    type: integer
-    description: Number of spatial observations to generate. Default value is 100.
-    default: 100
-  - name: "--cell_lb"
-    type: integer
-    description: Lower bound for number of cells at each spot. Default value is 10.
-    default: 10
-  - name: "--cell_ub"
-    type: integer
-    description: Upper bound for number of cells at each spot. Default value is 30.
-    default: 30
-  - name: "--umi_lb"
-    type: integer
-    description: Lower bound for number of UMIs at each spot. Default value is 1000.
-    default: 1000
-  - name: "--umi_ub"
-    type: integer
-    description: Upper bound for number of UMIs at each spot. Default value is 5000.
-    default: 5000
-  - name: "--simulated_data"
-    type: file
-    direction: output
-    description: Simulated dataset
-    required: true
-    __merge__: ../../api/file_simulated_dataset.yaml
+argument_groups:
+  - name: Input
+    arguments:
+      - name: "--input"
+        type: file
+        description: Single-cell reference dataset
+        direction: input
+        required: true
+        __merge__: ../../api/file_common_dataset.yaml
+  - name: Simulation options
+    arguments: 
+      - name: "--alpha"
+        type: double
+        description: Alpha value to use for generating synthetic dataset
+        default: 1.0
+      - name: "--n_obs"
+        type: integer
+        description: Number of spatial observations to generate. Default value is 100.
+        default: 100
+      - name: "--cell_lb"
+        type: integer
+        description: Lower bound for number of cells at each spot. Default value is 10.
+        default: 10
+      - name: "--cell_ub"
+        type: integer
+        description: Upper bound for number of cells at each spot. Default value is 30.
+        default: 30
+      - name: "--umi_lb"
+        type: integer
+        description: Lower bound for number of UMIs at each spot. Default value is 1000.
+        default: 1000
+      - name: "--umi_ub"
+        type: integer
+        description: Upper bound for number of UMIs at each spot. Default value is 5000.
+        default: 5000
+  - name: Output
+    arguments:
+      - name: "--simulated_data"
+        type: file
+        direction: output
+        description: Simulated dataset
+        required: true
+        __merge__: ../../api/file_simulated_dataset.yaml
 resources:
   - type: python_script
     path: script.py