Formatting

docs/episodes/9.2.md

@@ -36,6 +36,7 @@ This type of analysis is referred to as **differential abundance analysis**.
     ```r
     table(sce$label, sce$SampleName)
     ```
+
     - As these are count data, statistical methods used in flow-cytometry have been adapted to test for differences in cell abundance from such a matrix of counts. 
     - However, this approach relies on a fixed set of clusters determined by us beforehand, which can be limiting in cases where the changes in abundance are more continuous (e.g. along a developmental trajectory, or where cell states are not completely discrete). To address this limitation, Dann et al. (2022) developed a method where cell abundance differences are tested along neighbourhoods of cells on a KNN graph. This means that the results aren’t dependent on our clustering results, and are instead treated in a more “continuous” way.
 
@@ -276,4 +277,4 @@ We should expect the plot to be very biased towards 1, as is seen. This makes se
     # distribution of logFC across neighbourhood labels
     plotDAbeeswarm(da_results, group.by = "label")
     ```
-    ![image](../r_images/93-dabundance-DA-fold-changes.png)
+    ![image](../r_images/93-dabundance-DA-fold-changes.png)