fixes some typos

3_normalization_scaling.qmd

@@ -102,7 +102,7 @@ SpatialPlot(seu_list$Anterior,
   theme(legend.position = "right")
 ```
 
-It's mainly expressed in the olfactory bulb (left of the Anterior slice) and cerebellum (right of the anterior slice). 
+It's mainly expressed in the olfactory bulb (left of the anterior slice) and cerebellum (right of the posterior slice). 
 
 :::
 

4_integration_clustering.qmd

@@ -47,7 +47,7 @@ Just by typing the object name we already see the number of variable features:
 seu
 ```
 
-So, we have `r VariableFeatures(seu) |> length()` variable features. Because we selected the features that are variable in both slices, it is fewer genes than the originally selected default. 
+So, we have `r Seurat::VariableFeatures(seu) |> length()` variable features. Because we selected the features that are variable in both slices, it is fewer genes than the originally selected default. 
 
 :::
 

5_marker_gene_identification.qmd

@@ -54,13 +54,13 @@ all_marks |>
 
 To get a broad overview of all marker genes we can do the following:
 
--  Find the top 3 marker genes for each cluster. Here, we take an approximation of the test statistic (`avg_log2FC * -log10(p_val_adj + 1e-323)`) to sort the genes.
+-  Find the top 3 marker genes for each cluster. Here, we take an approximation of the test statistic (`avg_log2FC * -log10(p_val_adj + 1e-300)`) to sort the genes.
 - Then we create a dotplot of the expression of each gene per cluster
 
 ```{r}
 #| fig-width: 9
 top_markers <- all_marks |>
-  mutate(order_value = avg_log2FC * -log10(p_val_adj + 1e-323)) |>
+  mutate(order_value = avg_log2FC * -log10(p_val_adj + 1e-300)) |>
   arrange(cluster, desc(order_value)) |>
   group_by(cluster) |>
   top_n(3)
@@ -74,12 +74,12 @@ Now, we can check whether the expression pattern corresponds with the cluster, e
 ```{r}
 SpatialDimPlot(seu, 
                cells.highlight = CellsByIdentities(object = seu,
-                                                   idents = 3)) + 
+                                                   idents = 4)) + 
   plot_layout(guides='collect') &
   theme(legend.position = "none") 
 
 SpatialPlot(seu,
-            features = top_markers$gene[top_markers$cluster == 3][1],
+            features = top_markers$gene[top_markers$cluster == 4][1],
             pt.size.factor = 2) 
 ```