Merge pull request #67 from daisybio/transcriptInteraction_fix

transcript filter like gene filter

app/controllers/transcriptInteraction.py

@@ -883,30 +883,30 @@ def getTranscriptCounts(dataset_ID: int = None, disease_name=None, enst_number=N
 
 def get_transcript_network(dataset_ID: int = None, disease_name=None,
                             minBetweenness:float = None, minNodeDegree:float = None, minEigenvector:float = None,
-                            pValue=0.05, pValueDirection="<",
-                            mscor=None, mscorDirection="<", 
-                            correlation=None, correlationDirection="<",
-                            sorting=None, descending=True, limit=100, offset=0, sponge_db_version: int = LATEST):
+                            maxPValue=0.05, minMscor=None, minCorrelation=None,
+                            edgeSorting: str = None, nodeSorting: str = None,
+                            maxNodes: int = 100, maxEdges: int = 100, 
+                            offsetNodes: int = None, offsetEdges: int = None,
+                            sponge_db_version: int = LATEST):
     """
     Optimized function for fetching ceRNA network nodes and edges with applied filters and sorting. Handles route /ceRNAInteraction/getTranscriptNetwork
     :param dataset_ID: dataset_ID of interest
     :param disease_name: disease_name of interest
     :param minBetweenness: betweenness cutoff (>)
     :param minNodeDegree: degree cutoff (>)
     :param minEigenvector: eigenvector cutoff (>)
-    :param pValue: pValue cutoff
-    :param pValueDirection: < or >
-    :param mscor:  multiple miRNA sensitivity correlation
-    :param mscorDirection: < or >
-    :param correlation: correlation cutoff
-    :param correlationDirection: < or >
-    :param sorting: how the results of the db query should be sorted, one of 'pValue', 'mscor', 'correlation', 'betweenness', 'degree', 'eigenvector'
-    :param descending: should the results be sorted in descending or ascending order
-    :param limit: number of results that should be shown
-    :param offset: startpoint from where results should be shown
+    :param maxPValue: p-value cutoff (<)
+    :param minMscor: mscor cutoff (>)
+    :param minCorrelation: correlation cutoff (>)
+    :param edgeSorting: sorting key for edges
+    :param nodeSorting: sorting key for nodes
+    :param maxNodes: maximum number of nodes
+    :param maxEdges: maximum number of edges
+    :param offsetNodes: offset for node pagination
+    :param offsetEdges: offset for edge pagination
     :param sponge_db_version: version of the sponge database
     :return: all ceRNAInteractions in the dataset of interest that satisfy the given filters
-    
+     
     """
 
     # Step 1: Filter for SpongeRun IDs
@@ -926,16 +926,16 @@ def get_transcript_network(dataset_ID: int = None, disease_name=None,
     edge_query = db.select(models.TranscriptInteraction).filter(
         models.TranscriptInteraction.sponge_run_ID.in_(run_query),
     )
-    if pValue: 
+    if maxPValue: 
         edge_query = edge_query.filter(
-            models.TranscriptInteraction.p_value < pValue if pValueDirection == "<" else models.TranscriptInteraction.p_value > pValue
+            models.TranscriptInteraction.p_value <= maxPValue
         )
 
     # Get prefiltered edges 
     edges = db.session.execute(edge_query).scalars().all()
     tr_ids_in_edges = set([edge.transcript_ID_1 for edge in edges] + [edge.transcript_ID_2 for edge in edges])
 
-    # Step 3: Filter nodes by edges that pass the p-value filter
+    # Filter nodes by edges that pass the p-value filter
     node_query = db.select(models.networkAnalysisTranscript).filter(
         models.networkAnalysisTranscript.sponge_run_ID.in_(run_query),
         models.networkAnalysisTranscript.transcript_ID.in_(tr_ids_in_edges)
@@ -949,7 +949,20 @@ def get_transcript_network(dataset_ID: int = None, disease_name=None,
     if minEigenvector:
         node_query = node_query.filter(models.networkAnalysisTranscript.eigenvector >= minEigenvector)
 
-    # Step 4: Finalize edges by filtering based on filtered nodes
+    # Sorting nodes 
+    if nodeSorting == "betweenness":
+        node_query = node_query.order_by(models.networkAnalysisTranscript.betweenness.desc())
+    elif nodeSorting == "degree":
+        node_query = node_query.order_by(models.networkAnalysisTranscript.node_degree.desc())
+    elif nodeSorting == "eigenvector":
+        node_query = node_query.order_by(models.networkAnalysisTranscript.eigenvector.desc())
+    else: 
+        raise ValueError("Invalid node sorting key. Choose one of 'betweenness', 'degree', 'eigenvector'")
+
+    # node pagination
+    node_query = node_query.offset(offsetNodes).limit(maxNodes)    
+
+    # filter edges based on filtered nodes
     nodes = db.session.execute(node_query).scalars().all()
     node_tr_ids = set([node.transcript_ID for node in nodes])
     edge_query = db.select(models.TranscriptInteraction).filter(
@@ -960,30 +973,23 @@ def get_transcript_network(dataset_ID: int = None, disease_name=None,
     )
 
     # Apply edge-specific filters
-    if mscor:
-        edge_query = edge_query.filter(models.TranscriptInteraction.mscor <= mscor if mscorDirection == "<" else models.TranscriptInteraction.mscor >= mscor)
-    if correlation:
-        edge_query = edge_query.filter(models.TranscriptInteraction.correlation <= correlation if correlationDirection == "<" else models.TranscriptInteraction.correlation >= correlation)
-
-    # Apply sorting
-    # Sorting
-    if sorting:
-        sort_column = {
-            "pValue": models.GeneInteraction.p_value,
-            "mscor": models.GeneInteraction.mscor,
-            "correlation": models.GeneInteraction.correlation,
-            "betweenness": models.networkAnalysisTranscript.betweenness, 
-            "degree": models.networkAnalysisTranscript.node_degree,      
-            "eigenvector": models.networkAnalysisTranscript.eigenvector  
-        }.get(sorting)
-        if sorting in ["pValue", "mscor", "correlation"]:
-            edge_query = edge_query.order_by(sort_column.desc() if descending else sort_column.asc())
-        elif sorting in ['betweenness', 'degree', 'eigenvector']:
-            node_query = node_query.order_by(sort_column.desc() if descending else sort_column.asc())
-
-    # Pagination
-    edge_query = edge_query.offset(offset).limit(limit)
-    node_query = node_query.offset(offset).limit(limit)
+    if minMscor:
+        edge_query = edge_query.filter(models.TranscriptInteraction.mscor >= minMscor)
+    if minCorrelation:
+        edge_query = edge_query.filter(models.TranscriptInteraction.correlation >= minCorrelation)
+
+    # Sort edges
+    if edgeSorting == "pValue":
+        edge_query = edge_query.order_by(models.TranscriptInteraction.p_value.asc())
+    elif edgeSorting == "mscor":
+        edge_query = edge_query.order_by(models.TranscriptInteraction.mscor.desc())
+    elif edgeSorting == "correlation":
+        edge_query = edge_query.order_by(models.TranscriptInteraction.correlation.desc())
+    else:
+        raise ValueError("Invalid edge sorting key. Choose one of 'pValue', 'mscor', 'correlation'")    
+
+    # edge pagination
+    edge_query = edge_query.offset(offsetEdges).limit(maxEdges)
 
     # Execute queries
     node_results = db.session.execute(node_query).scalars().all()