Fetch enriched modules when summary is generated

callbacks/summary/callbacks.py

@@ -68,9 +68,9 @@ def disable_summary_button_upon_run(n_clicks,  *_):
         State('coexpression-submitted-clustering-algo', 'data'),
         State('coexpression-submitted-parameter-slider', 'data'),
     )
-    def display_summary_submitted_input(summary_is_submitted,
-                                        coexpression_is_submitted, genes, network, algo, submitted_parameter_slider):
-        if coexpression_is_submitted:
+    def display_summary_submitted_input(summary_submitted,
+                                        coexpression_submitted, genes, network, algo, submitted_parameter_slider):
+        if coexpression_submitted:
             parameters = 0
             if submitted_parameter_slider and algo in submitted_parameter_slider:
                 parameters = submitted_parameter_slider[algo]['value']
@@ -87,7 +87,7 @@ def display_summary_submitted_input(summary_is_submitted,
             parameters = 30
             genes = 'None'
 
-        if summary_is_submitted:
+        if summary_submitted:
             return [
                 html.B('Co-Expression Network Analysis'),
                 html.Br(),
@@ -116,14 +116,38 @@ def display_summary_submitted_input(summary_is_submitted,
         Output('summary-results-table', 'page_current', allow_duplicate=True),
 
         State('homepage-submitted-genomic-intervals', 'data'),
+
+        State('coexpression-combined-genes', 'data'),
+        State('coexpression-submitted-addl-genes', 'data'),
+        State('coexpression-submitted-network', 'data'),
+        State('coexpression-submitted-clustering-algo', 'data'),
+        State('coexpression-submitted-parameter-slider', 'data'),
+
         State('homepage-is-submitted', 'data'),
         Input('summary-is-submitted', 'data'),
+        State('coexpression-is-submitted', 'data'),
         prevent_initial_call=True
     )
-    def display_summary_results(genomic_intervals, homepage_submitted, summary_submitted):
+    def display_summary_results(genomic_intervals,
+                                combined_gene_ids, submitted_addl_genes, submitted_network, submitted_algo, submitted_parameter_slider,
+                                homepage_submitted, summary_submitted, coexpression_submitted):
+        if coexpression_submitted:
+            parameters = 0
+            if submitted_parameter_slider and submitted_algo in submitted_parameter_slider:
+                parameters = submitted_parameter_slider[submitted_algo]['value']
+
+        else:
+            # Assume default coexpression network parameters
+            submitted_algo = 'clusterone'
+            submitted_network = 'OS-CX'
+            parameters = 30
+            combined_gene_ids = lift_over_util.get_genes_in_Nb(genomic_intervals)[
+                1]
+            submitted_addl_genes = []
+
         if homepage_submitted and summary_submitted:
             summary_results_df = make_summary_table(
-                genomic_intervals)
+                genomic_intervals, combined_gene_ids, submitted_addl_genes, submitted_network, submitted_algo, parameters)
 
             columns = [{'id': x, 'name': x, 'presentation': 'markdown'}
                        for x in summary_results_df.columns]

callbacks/summary/util.py

@@ -1,10 +1,12 @@
 from ..lift_over.util import *
 from ..general_util import *
+from ..coexpression.util import *
 
 from collections import defaultdict
 
 
 def get_liftover_summary(implicated_genes):
+    # The Nipponbare IDs are in the second column
     NB_IDX = 1
     NB_PREFIX = 'LOC_Os'
 
@@ -37,30 +39,44 @@ def get_num_pubmed_pubs(pubmed_str):
     return pubmed_str.count('pubmed')
 
 
-def get_qtl_summary(genomic_interval):
-    genes = get_genes_in_Nb(genomic_interval)[0]
+def get_qtl_summary(genomic_intervals):
+    genes = get_genes_in_Nb(genomic_intervals)[0]
     genes['# QTL Analyses'] = genes.apply(
         lambda x: get_num_qtl_pubs(x['QTL Analyses']), axis=1)
 
     return genes[['Name', '# QTL Analyses']]
 
 
-def get_pubmed_summary(genomic_interval):
-    genes = get_genes_in_Nb(genomic_interval)[0]
+def get_pubmed_summary(genomic_intervals):
+    genes = get_genes_in_Nb(genomic_intervals)[0]
     genes['# PubMed Article IDs'] = genes.apply(
         lambda x: get_num_pubmed_pubs(x['PubMed Article IDs']), axis=1)
 
     return genes[['Name', '# PubMed Article IDs']]
 
 
-def make_summary_table(genomic_interval):
+def get_module_summary(genomic_intervals, combined_gene_ids, submitted_addl_genes, network, algo, parameters):
+    enriched_modules = do_module_enrichment_analysis(
+        combined_gene_ids, genomic_intervals, submitted_addl_genes, network, algo, parameters)
+
+    print(enriched_modules)
+
+    return None
+
+
+def make_summary_table(genomic_intervals, combined_gene_ids, submitted_addl_genes, network, algo, parameters):
     implicated_genes = get_all_genes(other_ref_genomes.keys(),
-                                     genomic_interval).values.tolist()
+                                     genomic_intervals).values.tolist()
 
     liftover_summary = get_liftover_summary(implicated_genes)
-    qtl_summary = get_qtl_summary(genomic_interval)
-    pubmed_summary = get_pubmed_summary(genomic_interval)
 
+    qtl_summary = get_qtl_summary(genomic_intervals)
+    pubmed_summary = get_pubmed_summary(genomic_intervals)
+
+    module_summary = get_module_summary(
+        genomic_intervals, combined_gene_ids, submitted_addl_genes, network, algo, parameters)
+
+    # Merge the summaries
     summary = liftover_summary.merge(
         qtl_summary, on='Name', how='left', validate='one_to_one')
     summary = summary.merge(pubmed_summary, on='Name',