v2.0

iaconogi · Nov 28, 2019 · 7071221 · 7071221
1 parent e0c4e77
commit 7071221
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Showing 2 changed files with 40 additions and 25 deletions.
diff --git a/R/Functions.R b/R/Functions.R
@@ -4452,8 +4452,6 @@ if ('counts' %in% assayNames(sce))
     {
     print('Computing cell to cell distances ...')
     sce=setDistances(sce,modality = modality)
-    print('Computing the clusters ...')
-    sce=setClusters(sce)
     }
   else
     {
@@ -4462,7 +4460,8 @@ if ('counts' %in% assayNames(sce))
     }
   sce=storeTsne(sce)
   }
-
+
+  sce=setClusters(sce)
   print('Computing the markers (slowest part) ...')
   sce=computeMarkers(sce,speed.preset=speed.preset)
   print('Organizing the markers ...')

diff --git a/R/SingleCellMethods.R b/R/SingleCellMethods.R
@@ -267,29 +267,45 @@ setMethod(f="setODgenes",
               return(object)
             }
 
-            if ('normcounts' %in% assayNames(object))
+            rounds=1
+            if (ncol(normcounts(object))>30000)
+              rounds=round(ncol(normcounts(object))/15000)
+            if (rounds>5) rounds=5
+            print(sprintf('Proceeding with %g rounds of search for driving genes',rounds))
+
+
+            for (k in 1:rounds) 
+            {
               out=calculate.ODgenes(expr.norm = normcounts(object),min_ODscore=min_ODscore,use.exp=use.exp)
-            else
-              out=calculate.ODgenes(expr.norm = object@int_metadata$expr.norm.big,min_ODscore=min_ODscore,use.exp=use.exp)
-
-            # print('Calculating ODgenes using normalized expression counts')
-            # tot.cells=ncol(normcounts(object))
-            # if (tot.cells>10000)
-            #   {
-            #   ix=sample(tot.cells,10000)
-            #   out=calculate.ODgenes(expr.norm = normcounts(object)[,ix],...)
-            #   }
-            # else
-            #   out=calculate.ODgenes(expr.norm = normcounts(object),...)
-
-
-            #    }
-            # else
-            #     {
-            #     print('Calculating ODgenes using expression counts NOT batch corrected')
-            #     out=calculate.ODgenes(expr.data = counts(object),...)
-            #     }  
+              dummy=as.matrix(out[[1]])
+              if (k==1)
+                {
+                ODgenes=dummy[,1]
+                ODscores=dummy[,2]
+                }
+                 else
+                {
+                ODgenes=cbind(ODgenes,dummy[,1])
+                ODscores=cbind(ODscores,dummy[,2])
+                }                 
+            }
+
+            #ODgenes.out<<-ODgenes
+            #ODscores.out<<-ODscores
 
+            if(rounds>1)
+            {
+              ODgenes=Rfast::rowsums(ODgenes)
+              ODgenes[which(ODgenes>0)]=1
+              ODscores=Rfast::rowmeans(ODscores) 
+              print(sprintf('Union results in %g driving genes',sum(ODgenes)))
+              object@int_elementMetadata$ODgenes=ODgenes
+              object@int_elementMetadata$ODscore=ODscores
+              return(object)
+            }
+
+
+
             object@int_metadata$ODgenes.plots=out[2:5]
             dummy=as.matrix(out[[1]])
             object@int_elementMetadata$ODgenes=dummy[,1]
@@ -506,7 +522,7 @@ setMethod(f="setClusters",
               print("Calculating the clusters")
 
 
-              if (is.null(sce@int_metadata$D))
+              if (is.null(object@int_metadata$D))
                 {
                 #knn.norm = FNN::get.knn(dummy$v, k = knn)
                 knn.norm = FNN::get.knn(reducedDim(object, 'UMAP'), k = k.num)