Add meta command, to run meta-analyses

Author: adelavega

autonima/__init__.py

@@ -33,6 +33,9 @@
 # Import LLM module components
 from .llm.client import GenericLLMClient
 
+# Import meta-analysis module components
+from .meta import run_meta_analyses
+
 __all__ = [
     "AutonimaPipeline",
     "PipelineConfig",
@@ -44,5 +47,6 @@
     "ParseAnalysesOutput",
     "parse_tables",
     "CoordinateParsingClient",
-    "GenericLLMClient"
+    "GenericLLMClient",
+    "run_meta_analyses"
 ]

autonima/cli.py

@@ -216,6 +216,85 @@ def validate(config: str, output_folder: str, debug: bool):
         sys.exit(1)
 
 
+@click.command()
+@click.argument('output_folder', type=click.Path(exists=True))
+@click.option('--estimator',
+              type=click.Choice(['ale', 'mkdadensity', 'kda']),
+              default='mkdadensity',
+              help='CBMA estimator to use (default: mkdadensity)')
+@click.option('--estimator-args',
+              type=str,
+              default='{}',
+              help='JSON string of arguments for the estimator (default: {})')
+@click.option('--corrector',
+              type=click.Choice(['fdr', 'montecarlo', 'bonferroni']),
+              default='fdr',
+              help='Corrector to use (default: fdr)')
+@click.option('--corrector-args',
+              type=str,
+              default='{}',
+              help='JSON string of arguments for the corrector (default: {})')
+@click.option('--debug', is_flag=True,
+              help='Enable debug mode with post-mortem debugging on errors')
+def meta(output_folder: str, estimator: str, estimator_args: str,
+         corrector: str, corrector_args: str, debug: bool):
+    """
+    Run meta-analyses on Autonima output using NiMARE.
+
+    This command runs coordinate-based meta-analyses on the results
+    from an Autonima systematic review pipeline.
+
+    Arguments:
+        OUTPUT_FOLDER   Output folder containing NiMADS files
+
+    Options:
+        --estimator              CBMA estimator to use (ale, mkdadensity, kda)
+        --estimator-args         JSON string of arguments for the estimator
+        --corrector              Corrector to use (fdr, montecarlo, bonferroni)
+        --corrector-args         JSON string of arguments for the corrector
+
+    Examples:
+        autonima meta results/outputs
+        autonima meta results/outputs --estimator ale --corrector montecarlo
+        autonima meta results/outputs --estimator-args '{"n_iters": 1000}' --corrector-args '{"alpha": 0.01}'
+    """
+    try:
+        import json
+        from pathlib import Path
+        
+        # Parse estimator and corrector arguments
+        try:
+            estimator_args_dict = json.loads(estimator_args)
+            corrector_args_dict = json.loads(corrector_args)
+        except json.JSONDecodeError as e:
+            log_error_with_debug(logger, f"Error parsing JSON arguments: {e}")
+            if debug:
+                import pdb
+                pdb.post_mortem()
+            sys.exit(1)
+        
+        # Import the meta-analysis function
+        from .meta import run_meta_analyses
+        
+        # Run meta-analyses
+        results = run_meta_analyses(
+            output_folder,
+            estimator_name=estimator,
+            estimator_args=estimator_args_dict,
+            corrector_name=corrector,
+            corrector_args=corrector_args_dict
+        )
+        
+        print(f"Completed meta-analyses for {len(results)} columns")
+        
+    except Exception as e:
+        log_error_with_debug(logger, f"Meta-analysis execution failed: {e}")
+        if debug:
+            import pdb
+            pdb.post_mortem()
+        sys.exit(1)
+
+
 @click.command()
 def create_sample_config():
     """
@@ -261,6 +340,7 @@ def cli():
 cli.add_command(run)
 cli.add_command(validate)
 cli.add_command(create_sample_config)
+cli.add_command(meta)
 
 
 def main():

autonima/coordinates/nimads_models.py

@@ -28,10 +28,12 @@ class Point:
     label_id: Optional[str] = None
     values: List[PointValue] = field(default_factory=list)
     analysis_id: Optional[str] = None
+    id: Optional[str] = None
 
     def to_dict(self) -> dict:
         """Convert to dictionary representation."""
         return {
+            "id": self.id,
             "coordinates": self.coordinates,
             "space": self.space,
             "kind": self.kind,
@@ -155,7 +157,7 @@ def to_dict(self) -> dict:
         }
 
 
-def convert_to_nimads_point(analysis_id: str, point: CoordinatePoint, study_space: Optional[str] = None) -> Point:
+def convert_to_nimads_point(analysis_id: str, point: CoordinatePoint, study_space: Optional[str] = None, point_id: Optional[str] = None) -> Point:
     """Convert a coordinate point to a NiMADS point."""
     # Sanitize the coordinate space
     sanitized_space = sanitize_coordinate_space(point.space, study_space)
@@ -166,7 +168,8 @@ def convert_to_nimads_point(analysis_id: str, point: CoordinatePoint, study_spac
     nimads_point = Point(
         coordinates=sanitized_coordinates,
         space=sanitized_space,
-        analysis_id=analysis_id
+        analysis_id=analysis_id,
+        id=point_id
     )
 
     # Convert point values if they exist
@@ -193,8 +196,9 @@ def convert_to_nimads_analysis(analysis_id: str, analysis: Analysis, study_id: s
     )
 
     # Convert points
-    for point in analysis.points:
-        nimads_point = convert_to_nimads_point(analysis_id, point, study_space)
+    for i, point in enumerate(analysis.points):
+        point_id = f"{analysis_id}_point_{i}"
+        nimads_point = convert_to_nimads_point(analysis_id, point, study_space, point_id)
         nimads_analysis.points.append(nimads_point)
 
     return nimads_analysis
@@ -211,8 +215,11 @@ def convert_to_nimads_study(study_id: str, autonima_study: 'autonima.models.type
         except (ValueError, IndexError):
             pass
 
+    # Use the study's PMID as the study_id if available
+    nimads_study_id = autonima_study.pmid if autonima_study.pmid else study_id
+    
     nimads_study = Study(
-        id=study_id,
+        id=nimads_study_id,
         doi=autonima_study.doi,
         name=autonima_study.title,
         description=autonima_study.abstract,
@@ -227,8 +234,8 @@ def convert_to_nimads_study(study_id: str, autonima_study: 'autonima.models.type
 
     # Convert analyses
     for i, analysis in enumerate(autonima_study.analyses):
-        analysis_id = f"{study_id}_analysis_{i}"
-        nimads_analysis = convert_to_nimads_analysis(analysis_id, analysis, study_id, study_space)
+        analysis_id = f"{nimads_study_id}_analysis_{i}"
+        nimads_analysis = convert_to_nimads_analysis(analysis_id, analysis, nimads_study_id, study_space)
         nimads_study.analyses.append(nimads_analysis)
 
     return nimads_study
@@ -282,8 +289,9 @@ def convert_to_nimads_studyset(studyset_id: str, studies: List['autonima.models.
     )
 
     # Convert studies
-    for i, study in enumerate(studies):
-        study_id = f"study_{i}"
+    for study in studies:
+        # Use the study's PMID as the study_id if available, otherwise generate one
+        study_id = study.pmid if study.pmid else f"study_{len(studyset.studies)}"
         nimads_study = convert_to_nimads_study(study_id, study)
         studyset.studies.append(nimads_study)