scenicplus works

src/control_methods/pearson/script.py

@@ -22,6 +22,10 @@
 parser.add_argument('--tf_all', type=str, help='Path to the tf_all')
 parser.add_argument('--num_workers', type=str, help='Number of cores')
 parser.add_argument('--no_tf_subsetting', action='store_true', default=False, help='Whether to subset based on tf')
+parser.add_argument('--max_n_links', type=str, help='Number of top links to retain')
+parser.add_argument('--corr_t', type=str, help='Threshold cuttoff for correlation.')
+parser.add_argument('--layer', type=str, help='Which layer of adata to use.')
+
 
 args = parser.parse_args()
 
@@ -35,6 +39,12 @@
     par['num_workers'] = args.num_workers
 if args.no_tf_subsetting:
     par['no_tf_subsetting'] = args.no_tf_subsetting
+if args.max_n_links:
+    par['max_n_links'] = int(args.max_n_links)
+if args.corr_t:
+    par['corr_t'] = float(args.corr_t)
+if args.layer:
+    par['layer'] = args.layer
 
 if args.resources_dir:
     meta['resources_dir'] = args.resources_dir  

src/exp_analysis/helper.py

@@ -59,7 +59,7 @@ def calculate_feature_distance(sample: pd.DataFrame, control: pd.DataFrame, top_
 
     distance_df = pd.DataFrame({'distance_raw':distance_raw, 'distance_rank':distance_rank}, index=entries_common)
     return distance_df 
-def cosine_similarity(nets_dict, col_name='source', weight_col='weight', figsize=(4, 4)):
+def cosine_similarity(nets_dict, col_name='source', weight_col='weight', figsize=(4, 4), title='Cosine Similarity', ax=None):
     from itertools import combinations
     from sklearn.metrics.pairwise import cosine_similarity
     import seaborn as sns
@@ -91,20 +91,22 @@ def cosine_similarity(nets_dict, col_name='source', weight_col='weight', figsize
         for j in range(cosine_sim_matrix.shape[1]):
             if i==j:
                 cosine_sim_matrix[i,j]=np.NaN
-
-    # 5. Visualize the Cosine Similarity matrix as a heatmap
-    fig, ax = plt.subplots(1, 1, figsize=figsize)
+    if ax is None:
+        # 5. Visualize the Cosine Similarity matrix as a heatmap
+        fig, ax = plt.subplots(1, 1, figsize=figsize)
+    else:
+        fig = None
 
     sns.heatmap(cosine_sim_matrix, annot=True, cmap="coolwarm", xticklabels=nets_names, yticklabels=nets_names, ax=ax)
-    ax.grid(True)
-    ax.set_title('Cosine Similarity')
+    ax.grid(False)
+    ax.set_title(title)
 
     # Rotate x labels for readability
     plt.xticks(rotation=45, ha='right')
 
     return cosine_sim_matrix, fig
 
-def jaccard_similarity(nets_dict, col_name='link', figsize=(4, 4)):
+def jaccard_similarity(nets_dict, col_name='link', figsize=(4, 4), title='jaccard Similarity', ax=None):
     from itertools import combinations
     import seaborn as sns
     import matplotlib.pyplot as plt
@@ -132,10 +134,13 @@ def jaccard_similarity(nets_dict, col_name='link', figsize=(4, 4)):
     # Fill diagonal with 1s (as similarity of a network with itself is 1)
     np.fill_diagonal(jaccard_matrix, np.NaN)
     # 4. Visualize the Jaccard matrix as a heatmap
-    fig, ax = plt.subplots(1, 1, figsize=figsize)
+    if ax is None:
+        fig, ax = plt.subplots(1, 1, figsize=figsize)
+    else:
+        fig = None
     sns.heatmap(jaccard_matrix, annot=True, cmap="coolwarm", xticklabels=nets_names, yticklabels=nets_names, ax=ax)
-    ax.grid(True)
-    ax.set_title('Jaccard Similarity')
+    ax.grid(False)
+    ax.set_title(title)
     # Rotate x labels for readability
     plt.xticks(rotation=45, ha='right')
 
@@ -225,7 +230,7 @@ class Exp_analysis:
     '''
     def __init__(self, net, peak_gene_net=None):
         self.net = net 
-        self.net.weight = minmax_scale(self.net.weight)
+        # self.net.weight = minmax_scale(self.net.weight)
         self.net['link'] = self.net['source'].astype(str) + '_' + self.net['target'].astype(str)
 
         self.peak_gene_net = peak_gene_net
@@ -354,7 +359,7 @@ def plot_interactions(interaction_df: pd.DataFrame, min_subset_size=None, min_de
     out_dict = upsetplot.plot(upsetplot.from_indicators(indicators=lambda a: a==True, data=interaction_df), fig=fig, 
             show_counts=True, 
             show_percentages = '{:.0%}',
-            sort_by='cardinality', 
+            # sort_by='cardinality', 
             # min_subset_size =".1%", # min interaction to show
             min_subset_size = min_subset_size, # min interaction to show
             min_degree=min_degree,
@@ -372,9 +377,10 @@ def plot_interactions(interaction_df: pd.DataFrame, min_subset_size=None, min_de
     methods_order = [label.get_text() for label in matrix_ax.get_yticklabels()]
     # methods_order.reverse()
     print(methods_order)
+    colors = colors_blind+colors_blind
     for i_bar, bar in enumerate(totals_ax.patches):
         if color_map is None:
-            bar.set_facecolor(colors_blind[i_bar])
+            bar.set_facecolor(colors[i_bar])
         else:
             bar.set_facecolor(color_map[methods_order[i_bar]])
         bar.set_edgecolor('white')
@@ -384,7 +390,7 @@ def plot_interactions(interaction_df: pd.DataFrame, min_subset_size=None, min_de
         bar.set_edgecolor('black')
         bar.set_linewidth(.4)
 
-    for bar, new_color in zip(shading_ax.patches, colors_blind):
+    for bar, new_color in zip(shading_ax.patches, colors):
         bar.set_facecolor(new_color)
         bar.set_alpha(.1)
         bar.set_edgecolor('black')

src/methods/multi_omics/figr/script.R

@@ -11,7 +11,7 @@ par <- list(
   multiomics_rna_r = "resources_test/grn-benchmark/multiomics_rna.rds",
   multiomics_atac_r = "resources_test/grn-benchmark/multiomics_atac.rds",
   temp_dir =  "output/figr/",
-  cell_topic = "resources_test/prior/cell_topic.csv",
+  cell_topic = "resources/prior/cell_topic_d0_hvg.csv",
   num_workers = 2,
   n_topics = 48,
   peak_gene = "output/figr/peak_gene.csv",

src/methods/multi_omics/scenicplus/config.vsh.yaml

@@ -36,6 +36,7 @@ functionality:
   resources:
     - type: python_script
       path: script.py
+    - path: main.py
 
 platforms:
   - type: docker