Made sure ordering of matrices are ok.

test/workflows/affy_workflowtest.py

@@ -75,6 +75,13 @@ CEL\tsex\tage\tinfected
                            ['02-05-34', 'F', '9', 'N'],
                            ['02-05-35', 'M', '8', 'I']], dataset.get_phenotype_table())
 
+        # we can also get a sorted list
+        new_order = ['02-05-35', '02-05-33', '02-05-34']
+        self.assertEquals([['CEL', 'sex', 'age', 'infected'],
+                           ['02-05-35', 'M', '8', 'I'],
+                           ['02-05-33', 'F', '8', 'I'],
+                           ['02-05-34', 'F', '9', 'N']], dataset.get_phenotype_table(new_order))
+
     def testGetCategories(self):
         cel_data = """\
 CEL\tsex\tage\tinfected
@@ -110,6 +117,5 @@ CEL\tsex\tage\tinfected
         self.assertEquals([1, 0, 1], dataset.get_category_variable("I"))
         self.assertEquals([0, 1, 0], dataset.get_category_variable("N"))
 
-
 if __name__=='__main__':
     unittest.main()

workflows/affy_workflow.py

@@ -131,41 +131,41 @@ Example: Y-N, M-F""" % ", ".join(data.get_categories()))
         if not factors:
             logger.log("warning", "nothing to do, no factors")
 
-        table = data.get_phenotype_table()
+        table = data.get_phenotype_table([os.path.splitext(f)[0] for f in affy.get_identifiers('filename')])
         cn = table[0]
         entries = zip(*table[1:])
         rn = entries[0]
 
         import rpy
+        silent_eval = rpy.with_mode(rpy.NO_CONVERSION, rpy.r)
         rpy.r.library("limma")
-        rpy.r("a <- matrix('kalle', nrow=%d, ncol=%d)" % (len(rn), len(cn)))
+        silent_eval("a <- matrix('kalle', nrow=%d, ncol=%d)" % (len(rn), len(cn)))
         for i, row in enumerate(entries):
             for j, entry in enumerate(row):
-                rpy.r("a[%d, %d] <- '%s'" % (j+1, i+1, entry))
+                silent_eval("a[%d, %d] <- '%s'" % (j+1, i+1, entry))
         rpy.r.assign("rn", rn)
         rpy.r.assign("cn", cn)
-        rpy.r("rownames(a) <- rn")
-        rpy.r("colnames(a) <- cn")
+        silent_eval("rownames(a) <- rn")
+        silent_eval("colnames(a) <- cn")
 
         unique_categories = list(set(categories))
 
         # compose fancy list of factors for design matrix
-        rpy.r("design <- matrix(0, nrow=%d, ncol=%d)" % (len(rn), len(unique_categories)))
+        silent_eval("design <- matrix(0, nrow=%d, ncol=%d)" % (len(rn), len(unique_categories)))
         for i, category in enumerate(unique_categories):
             for j, value in enumerate(data.get_category_variable(category)):
-                rpy.r("design[%d, %d] <- %d" % (j+1, i+1, value))
+                silent_eval("design[%d, %d] <- %d" % (j+1, i+1, value))
 
         rpy.r.assign("colnames.design", unique_categories)
-        rpy.r("colnames(design) <- colnames.design")
+        silent_eval("colnames(design) <- colnames.design")
 
         rpy.r.assign("expr", affy.asarray())
-        rpy.r("fit <- lmFit(expr, design)")
-
+        silent_eval("fit <- lmFit(expr, design)")
         # FIXME: might be a case for code injection...
         string = "contrast.matrix <- makeContrasts(%s, levels=design)" % response
-        rpy.r(string)
-        rpy.r("fit2 <- contrasts.fit(fit, contrast.matrix)")
-        rpy.r("fit2 <- eBayes(fit2)")
+        silent_eval(string)
+        silent_eval("fit2 <- contrasts.fit(fit, contrast.matrix)")
+        silent_eval("fit2 <- eBayes(fit2)")
         coeff = rpy.r("fit2$coefficients")
         amean = rpy.r("fit2$Amean")
         padj = rpy.r("p.adjust(fit2$p.value, method='fdr')")
@@ -187,8 +187,10 @@ Example: Y-N, M-F""" % ", ".join(data.get_categories()))
                                          'contrast', response, response,
                                          name="Vulcano plot")
 
-        return [coeff_data, amean_data, padj_data, vulcano_plot]
+        # We should be nice and clean up after ourselves
+        rpy.r("rm(list=ls())")
 
+        return [coeff_data, amean_data, padj_data, vulcano_plot]
         
 
 class CelFileImportFunction(workflow.Function):
@@ -349,9 +351,34 @@ class PhenotypeDataset(dataset.Dataset):
                                                        ('phenotypes', phenotypes)],
                                  shape=(len(cel_names),len(phenotypes)), name="Phenotype Data")
 
-    def get_phenotype_table(self):
+    def sort_cels(self, cel_names):
+        self._dims = []
+
+        cels = {}
+        for row in self._table[1:]:
+            cels[row[0]] = row[1:]
+
+        new_table = [self._table[0]]
+        for name in cel_names:
+            new_table.append([name] + cels[name])
+
+        self._table = new_table
+        self._set_identifiers([('CEL', cel_names), ('phenotypes', self.get_identifiers('phenotypes'))], self._all_dims)
+            
+    def get_phenotype_table(self, cel_order=None):
         """Get string based table of phenotypes as read from file."""
-        return self._table
+        if not cel_order:
+            return self._table
+        else:
+            cels = {}
+            for row in self._table[1:]:
+                cels[row[0]] = row[1:]
+
+            new_table = [self._table[0]]
+            for name in cel_order:
+                new_table.append([name] + cels[name])
+
+            return new_table
 
     def get_categories(self):
         """Get categories of factors.