minor errors fixed

src/api/comp_control_method.yaml

@@ -1,3 +1,4 @@
+
 functionality:
   namespace: "control_methods"
   info:
@@ -15,9 +16,9 @@ functionality:
     - name: --layer
       type: string
       direction: input
-      default: lognorm
+      default: scgen_pearson
       description: Which layer of pertubation data to use to find tf-gene relationships.
-      required: true
+      required: false
     - name: --prior_data
       __merge__: file_prior.yaml
       direction: input

src/api/comp_metric.yaml

@@ -25,12 +25,16 @@ functionality:
       direction: input
       default: ridge
       description: name of regretion to use
-      multiple: true
+      multiple: false
+      info:
+        test_default: ridge
     - name: --subsample
       type: integer
       direction: input
       default: -1
       description: number of samples randomly drawn from perturbation data
+      info:
+        test_default: 200
 
   test_resources:
     - type: python_script

src/api/file_multiomics_atac_h5ad.yaml

@@ -1,5 +1,5 @@
 type: file
-example: resources/grn-benchmark/multiomics_atac.h5ad
+example: resources_test/grn-benchmark/multiomics_atac.h5ad
 info:
   label: multiomics atac
   summary: "Peak data for multiomics data."

src/api/file_multiomics_rna_h5ad.yaml

@@ -1,5 +1,5 @@
 type: file
-example: resources/grn-benchmark/multiomics_rna.h5ad
+example: resources_test/grn-benchmark/multiomics_rna.h5ad
 info:
   label: multiomics rna
   summary: "RNA expression for multiomics data."

src/api/file_perturbation_h5ad.yaml

@@ -46,9 +46,9 @@ info:
       - name: pearson
         type: double
         description: "Normalized values using pearson residuals"
-        required: true
+        required: false
       - name: lognorm
         type: double
         description: "Normalized values using shifted logarithm "
-        required: true
+        required: false
 

src/control_methods/negative_control/script.py

@@ -6,9 +6,9 @@
 ## VIASH START
 par = {
   "perturbation_data": "resources/grn-benchmark/perturbation_data.h5ad",
-  "layer": "lognorm",
+  "layer": "scgen_pearson",
   "prior_data":  "resources/grn-benchmark/prior_data.h5ad",
-  "output": "resources/grn-benchmark/negative_control.csv",
+  "prediction": "output/negative_control.csv",
 }
 ## VIASH END
 print('Reading input data')
@@ -30,5 +30,5 @@ def create_negative_control(gene_names) -> np.ndarray:
 pivoted_net = pivoted_net[pivoted_net['weight'] != 0]
 
 print('Saving')
-pivoted_net.to_csv(par["output"])
+pivoted_net.to_csv(par["prediction"])
 

src/control_methods/positive_control/script.py

@@ -7,9 +7,9 @@
 ## VIASH START
 par = {
   "perturbation_data": "resources/grn-benchmark/perturbation_data.h5ad",
-  "layer": "lognorm",
+  "layer": "scgen_pearson",
   "prior_data":  "resources/grn-benchmark/prior_data.h5ad",
-  "prediction": "resources/grn-benchmark/positive_control.csv",
+  "prediction": "output/positive_control.csv",
 }
 ## VIASH END
 print('Reading input data')

src/metrics/regression_1/main.py

@@ -53,7 +53,7 @@ def cv_5(genes_n):
 def run_method_1(
             net: pd.DataFrame, 
             train_df: pd.DataFrame,
-            reg_type: str = 'GRB',
+            reg_type: str = 'GB',
             exclude_missing_genes: bool = False,
             verbose: int = 0) -> None: 
     """
@@ -73,8 +73,8 @@ def run_method_1(
         regr = lightgbm_wrapper(dict(random_state=32, n_estimators=100, min_samples_leaf=2, min_child_samples=1, feature_fraction=0.05, verbosity=-1))
     elif reg_type=='RF':
         regr = lightgbm_wrapper(dict(boosting_type='rf',random_state=32, n_estimators=100,  feature_fraction=0.05, verbosity=-1))
-
     else:
+        print(f'{reg_type} is not defined')
         raise ValueError("define first")        
 
     n_tfs = net.shape[1]