diff --git a/situr/image/situ_tile.py b/situr/image/situ_tile.py
index bb7db9e..353836e 100644
--- a/situr/image/situ_tile.py
+++ b/situr/image/situ_tile.py
@@ -1,22 +1,32 @@
 import numpy as np
 
 from situr.image.situ_image import SituImage
+from situr.transformation import IdentityRoundTransform
 
 
 class Tile:
     '''
-    * Rounds 5
-    * Channels 4+1 - spot colours + nuclei
-    * Z 1 to 30 - focus level
-    * Y 2048
-    * X 2048
+    Rounds 5
+    Channels 4+1 - spot colours + nuclei
+    Z 1 to 30 - focus level
+    Y 2048
+    X 2048
     '''
 
     def __init__(self, file_list, nucleaus_channel=4):
         self.images = []
+        self.round_transformations = []
         for situ_image_list in file_list:
             self.images.append(
                 SituImage(situ_image_list, nucleaus_channel=nucleaus_channel))
+            self.round_transformations.append(IdentityRoundTransform())
+
+    def apply_transformations():
+        # TODO: implement (first apply channel transformations then round transformations)
+        pass
+
+    def set_round_transformation(self, round, transformation):
+        self.round_transformations[round] = transformation
 
     def get_round_count(self):
         return len(self.images)
diff --git a/situr/transformation/__init__.py b/situr/transformation/__init__.py
index 71b3be3..fd0d4c7 100644
--- a/situr/transformation/__init__.py
+++ b/situr/transformation/__init__.py
@@ -1 +1,2 @@
 from .channel_transformation import ChannelTransform, IdentityChannelTransform, ScaleRotateTranslateChannelTransform
+from .round_transformation import RoundTransform, IdentityRoundTransform
diff --git a/situr/transformation/round_transformation.py b/situr/transformation/round_transformation.py
new file mode 100644
index 0000000..245f691
--- /dev/null
+++ b/situr/transformation/round_transformation.py
@@ -0,0 +1,16 @@
+import abc
+
+
+class RoundTransform:
+    __metaclass__ = abc.ABCMeta
+
+    @abc.abstractmethod
+    def apply_transformation(self, situ_tile, channel):
+        """Performs a transformation on one channel, all focus_levels are transformed the same way"""
+        raise NotImplementedError(
+            self.__class__.__name__ + '.apply_transformation')
+
+
+class IdentityRoundTransform(RoundTransform):
+    def apply_transformation(self, situ_tile, channel):
+        pass