diff --git a/EnsembleEnrichment/ComputeAllCategoryNulls.m b/EnsembleEnrichment/ComputeAllCategoryNulls.m
index 3299252..5fa21e5 100644
--- a/EnsembleEnrichment/ComputeAllCategoryNulls.m
+++ b/EnsembleEnrichment/ComputeAllCategoryNulls.m
@@ -114,12 +114,17 @@
         end
     end
 
+    %---------------------------------------------------------------------------
     % Aggregate gene-wise scores into an overall GO category score
     switch enrichmentParams.aggregateHow
     case 'mean'
         categoryScores{i} = nanmean(scoresHere,1);
+    case 'absmean'
+        categoryScores{i} = nanmean(abs(scoresHere),1);
     case 'median'
         categoryScores{i} = nanmedian(scoresHere,1);
+    case 'absmedian'
+        categoryScores{i} = nanmedian(abs(scoresHere),1);
     otherwise
         error('Unknown aggregation option: ''%s''',enrichmentParams.aggregateHow);
     end
diff --git a/Peripheral/GiveMeDefaultEnrichmentParams.m b/Peripheral/GiveMeDefaultEnrichmentParams.m
index 2ef1e6d..f362655 100644
--- a/Peripheral/GiveMeDefaultEnrichmentParams.m
+++ b/Peripheral/GiveMeDefaultEnrichmentParams.m
@@ -28,13 +28,14 @@
 % What type of correlation to use
 enrichmentParams.whatCorr = 'Spearman'; % 'Pearson', 'Spearman'
 
-% How to agglomerate scores within a GO category:
-enrichmentParams.aggregateHow = 'mean'; % 'mean', 'median'
+% How to aggregate scores within a GO category:
+enrichmentParams.aggregateHow = 'mean'; % 'mean', 'absmean', 'median', 'absmedian'
 
 % What type of null:
 enrichmentParams.whatEnsemble = 'randomMap'; % 'randomMap', 'customEnsemble'
 
 % Specify a custom data file in the case of running 'customEnsemble' enrichment:
+% (file containing the matrix of null phenotypes):
 enrichmentParams.dataFileSurrogate = [];
 
 % Map parameters on to an appropriate file name to save results to: