minor updates

1_setup.qmd

@@ -99,6 +99,6 @@ course_data/
 
 This data is provided by 10x genomics, and further information, like material and methods can be found here:
 
-- Antherior: [https://www.10xgenomics.com/datasets/mouse-brain-serial-section-1-sagittal-anterior-1-standard-1-1-0](https://www.10xgenomics.com/datasets/mouse-brain-serial-section-1-sagittal-anterior-1-standard-1-1-0)
+- Anterior: [https://www.10xgenomics.com/datasets/mouse-brain-serial-section-1-sagittal-anterior-1-standard-1-1-0](https://www.10xgenomics.com/datasets/mouse-brain-serial-section-1-sagittal-anterior-1-standard-1-1-0)
 - Posterior: [https://www.10xgenomics.com/datasets/mouse-brain-serial-section-1-sagittal-posterior-1-standard-1-1-0](https://www.10xgenomics.com/datasets/mouse-brain-serial-section-1-sagittal-posterior-1-standard-1-1-0)
 

2_quality_control.qmd

@@ -234,17 +234,25 @@ s
 # defining the high mt spot in anterior
 seu$percent_mt_keep <- !(seu$orig.ident == "Anterior" & seu$percent_mt > 38)
 
-SpatialPlot(seu, group.by = "percent_mt_keep") +
+cells_mt_keep <- colnames(seu)[seu$percent_mt_keep]
+
+SpatialPlot(seu, cells.highlight = cells_mt_keep,
+            cols.highlight = c("grey50", "red"),
+            pt.size.factor = 2.5) +
   plot_annotation(title = "Filter % mitochondrial UMI") +
   plot_layout(guides='collect') & theme(legend.position = "none") 
   
 # defining the low number of genes
 seu$nFeature_Spatial_keep <- seu$nFeature_Spatial > 100
 
-SpatialPlot(seu, group.by = "nFeature_Spatial_keep") +
-  plot_annotation(title = "Filter % low number of features") +
-  plot_layout(guides='collect') & theme(legend.position = "none")
+cells_nfeature_keep <- colnames(seu)[seu$nFeature_Spatial_keep]
 
+SpatialPlot(seu, cells.highlight = cells_nfeature_keep,
+            cols.highlight = c("grey50", "red"),
+            pt.size.factor = 2.5) +
+  plot_annotation(title = "Filter % low number of features") +
+  plot_layout(guides='collect') & theme(legend.position = "none") 
+  
 ```
 
 Now, we remove the spots, and visualize the spots that are left over:

3_normalization_scaling.qmd

@@ -33,17 +33,21 @@ We need to apply `SCTransform` on each individual slice. Therefore, we split the
 
 ```{r}
 #| message: FALSE
+#| warning: FALSE
 seu_list <- SplitObject(seu, split.by = "orig.ident")
 
-# images aren't split with SplitObject. Resetting the images. 
+# preparing both objects for SCTransform
 for(slice in names(seu_list)) {
+  
+  # images aren't split with SplitObject. Resetting the images. 
   seu_list[[slice]]@images <- setNames(
     list(seu_list[[slice]]@images[[slice]]),
     slice)
   
   # bugfix based on https://github.com/satijalab/seurat/issues/8216
   seu_list[[slice]][["RNA"]] <- seu_list[[slice]][["Spatial"]]
   DefaultAssay(seu_list[[slice]]) <- "RNA"
+  
 }
 
 seu_list <- lapply(X = seu_list, FUN = SCTransform, assay = "RNA",

4_integration_clustering.qmd

@@ -63,6 +63,9 @@ DimPlot(seu, reduction = "pca", group.by = "orig.ident") +
 Based on the PCA, we can create a UMAP to get a representation of all 50 dimensions in a two dimensional space:
 
 ```{r}
+#| warning: FALSE
+#| message: FALSE
+
 seu <- RunUMAP(seu, reduction = "pca", dims = 1:50)
 
 DimPlot(seu, reduction = "umap", group.by = "orig.ident") + 
@@ -176,7 +179,7 @@ There is not 'true' clustering, but based on the `clustree` plot, it seems that
 
 ```{r}
 #| warning: false
-res <- "integrated_snn_res.0.5"
+res <- "integrated_snn_res.0.4"
 seu <- SetIdent(seu, value = res)
 ```
 
@@ -187,13 +190,16 @@ The script below assumes that you have set object `res` to the column of your se
 
 ```{r}
 #| eval: false
+# this is not (necessarily) the correct answer to the previous question!
 res <- "integrated_snn_res.0.8"
 ```
 ::: 
 
 Now that we have selected a resolution, we can color both the UMAP and the slices accordingly. First we defnie some appropriate colors, then we plot the UMAP with `DimPlot` and the slices with `SpatialPlot`. 
 
 ```{r}
+#| warning: false
+#| message: false
 nclust <- seu[[res]] |> unique() |> nrow()
 cluster_cols <- viridis::viridis_pal(option = "H")(nclust)