Added matrix-getter to Dataset.

Added rma-function to preprocessing incorporated in AffyMatrix-importer.
Added PCAFunction to go_workflow for processing loaded affymatrix data.

system/dataset.py

@@ -160,6 +160,10 @@ class Dataset:
         """Returns dataset name"""
         return self._name
 
+    def get_matrix(self):
+        """Returns internal numeric matrix for dataset."""
+        return self._array
+
     def get_all_dims(self):
         """Returns all dimensions in project"""
         return self._all_dims

workflows/go_workflow.py

@@ -36,6 +36,10 @@ class EinarsWorkflow (Workflow):
         regression.add_function(Function('pls', 'PLS'))
         self.add_stage(regression)
 
+        explore = Stage('explore', 'Explorative analysis')
+        explore.add_function(PCAFunction(self))
+        self.add_stage(explore)
+
         save = Stage('save', 'Save Data')
         save.add_function(DatasetSaveFunction())
         self.add_stage(save)
@@ -191,6 +195,7 @@ class CelFileImportFunction(Function):
     
                 silent_eval = rpy.with_mode(rpy.NO_CONVERSION, rpy.r)
                 silent_eval('E <- ReadAffy(filenames=c("%s"))' % '", "'.join(chooser.get_filenames()))
+                silent_eval('E <- rma(E)')
 
                 m = rpy.r('m <- E@exprs')
     
@@ -202,8 +207,40 @@ class CelFileImportFunction(Function):
                 rpy.r.rm(["E", "m"])
                 
                 if m:
-                    return [dataset.Dataset(m, (('ids', rownames), ('filename', colnames)), "AffyMatrix Data")] 
+                    return [dataset.Dataset(m, (('ids', rownames), ('filename', colnames)), name="AffyMatrix Data")] 
                 else:
                     logger.log("notice", "No data loaded from importer.")
         finally:
             chooser.destroy()
+
+
+class PCAFunction(Function):
+    """Generic PCA function."""
+    def __init__(self, workflow):
+        Function.__init__(self, 'pca', 'PCA')
+        self._workflow = workflow
+
+    def run(self, data):
+        import rpy
+        
+        dim_2, dim_1 = data.get_dim_names()
+        
+    
+        silent_eval = rpy.with_mode(rpy.NO_CONVERSION, rpy.r)
+        rpy.with_mode(rpy.NO_CONVERSION, rpy.r.assign)("m", data.get_matrix())
+        silent_eval("t = prcomp(t(m))")
+
+        T_ids = map(str, range(1, rpy.r("dim(t$x)")[1]+1))
+        T = dataset.Dataset(rpy.r("t$x"), [(dim_1, data.get_identifiers(dim_1)),
+                                   ("component", T_ids)], name="T")
+        P = dataset.Dataset(rpy.r("t$rotation"), [(dim_2, data.get_identifiers(dim_2)),
+                                          ("component", T_ids)], name="P")
+
+        # cleanup
+        rpy.r.rm(["t", "m"])
+
+        loading_plot1 = plots.ScatterPlot(self._workflow.project,P,'ids','component','1','2')
+        loading_plot2 = plots.ScatterPlot(self._workflow.project,P,'ids','component','3','4')
+        score_plot = plots.ScatterPlot(self._workflow.project,T,'filename','component','1','2')     
+        
+        return [T, P, loading_plot1, loading_plot2, score_plot]