Live and rendered notebooks

RNA-seq/02-gastric_cancer_tximeta.nb.html

@@ -3264,8 +3264,8 @@ <h2>Summarize to gene</h2>
 <pre class="r"><code># Summarize to the gene level
 gene_summarized &lt;- summarizeToGene(txi_data)</code></pre>
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-<pre><code>loading existing EnsDb created: 2024-08-08 16:10:27</code></pre>
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+<pre><code>loading existing EnsDb created: 2024-12-04 20:35:25</code></pre>
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 <pre><code>obtaining transcript-to-gene mapping from database</code></pre>

scRNA-seq-advanced/01-read_filter_normalize_scRNA-live.Rmd

@@ -90,9 +90,7 @@ Finally, we will set up the our output directory, creating it if it does not yet
 normalized_dir <- file.path(data_dir, "normalized")
 
 # create the directory if it does not exist
-if (!dir.exists(normalized_dir)) {
-  dir.create(normalized_dir, recursive = TRUE)
-}
+fs::dir_create(normalized_dir)
 
 # output RDS file for normalized data
 output_sce_file <- file.path(normalized_dir,
@@ -113,6 +111,8 @@ Some of these functions for other common data formats are discussed in [Chapter
 ```{r read SCE, live=TRUE}
 # read SCE from matrix directory
 
+  # ensure barcodes are set as column names in the SCE object
+
 ```
 
 Let's look at the contents of the object after reading it in:

scRNA-seq-advanced/02-dataset_integration-live.Rmd

@@ -447,6 +447,10 @@ To see how this looks, let's look at the UMAP when calculated from individual sa
 ```{r plot individual UMAPs, live = TRUE}
 # Plot UMAP calculated from individual samples with separate scaling
 
+  # Use a CVD-friendly color scheme and specify legend name
+
+  # Modify the legend key so its points are larger and easier to see
+
 ```
 
 
@@ -525,13 +529,12 @@ Now, let's see how this new `merged_UMAP` looks compared to the `UMAP` calculate
 # UMAPs scaled together when calculated from the merged SCE
 scater::plotReducedDim(merged_sce,
                        dimred = "merged_UMAP",
-                       colour_by = "sample",
+                       color_by = "sample",
                        # Some styling to help us see the points:
                        point_size = 0.5,
                        point_alpha = 0.2) +
-  # Modify the legend key so its points are larger and easier to see
-  guides(colour = guide_legend(override.aes = list(size = 3, alpha = 1))) +
-  # Add a plot title
+  scale_color_brewer(palette = "Dark2", name = "sample") +
+  guides(color = guide_legend(override.aes = list(size = 3, alpha = 1))) +
   ggtitle("UMAP calculated on merged_sce")
 ```
 
@@ -596,13 +599,12 @@ scater::plotReducedDim(merged_sce,
                        # plot the fastMNN coordinates
                        dimred = "fastmnn_UMAP",
                        # color by sample
-                       colour_by = "sample",
+                       color_by = "sample",
                        # Some styling to help us see the points:
                        point_size = 0.5,
                        point_alpha = 0.2) +
-  # Modify legend so they key is larger and easier to see
-  guides(colour = guide_legend(override.aes = list(size = 3, alpha = 1))) +
-  # add plot title
+  scale_color_brewer(palette = "Dark2", name = "sample") +
+  guides(color = guide_legend(override.aes = list(size = 3, alpha = 1))) +
   ggtitle("UMAP after integration with fastMNN")
 ```
 
@@ -642,10 +644,12 @@ Let's re-plot this UMAP to highlight cell types:
 scater::plotReducedDim(merged_sce,
                        dimred = "fastmnn_UMAP",
                        # color by broad celltypes
-                       colour_by = "celltype_broad",
+                       color_by = "celltype_broad",
                        point_size = 0.5,
                        point_alpha = 0.2) +
-  guides(colour = guide_legend(override.aes = list(size = 3, alpha = 1))) +
+  # include argument to specify color of NA values
+  scale_color_brewer(palette = "Dark2", name = "Broad celltype", na.value = "grey80") +
+  guides(color = guide_legend(override.aes = list(size = 3, alpha = 1))) +
   ggtitle("UMAP after integration with fastMNN")
 ```
 
@@ -658,12 +662,13 @@ One way we can see all the points a bit better is to facet the plot by sample, u
 ```{r plot fastmnn umap celltypes faceted}
 scater::plotReducedDim(merged_sce,
                        dimred = "fastmnn_UMAP",
-                       colour_by = "celltype_broad",
+                       color_by = "celltype_broad",
                        point_size = 0.5,
                        point_alpha = 0.2,
                        # Allow for faceting by a variable using `other_fields`:
                        other_fields = "sample") +
-  guides(colour = guide_legend(override.aes = list(size = 3, alpha = 1))) +
+  scale_color_brewer(palette = "Dark2", name = "Broad celltype", na.value = "grey80") +
+  guides(color = guide_legend(override.aes = list(size = 3, alpha = 1))) +
   ggtitle("UMAP after integration with fastMNN") +
   # Facet by sample
   facet_wrap(vars(sample)) +
@@ -743,11 +748,12 @@ Let's see how the `harmony` UMAP, colored by sample, looks compared to the `fast
 ```{r plot harmony umap batches}
 scater::plotReducedDim(merged_sce,
                        dimred = "harmony_UMAP",
-                       colour_by = "sample",
+                       color_by = "sample",
                        point_size = 0.5,
                        point_alpha = 0.2) +
-  ggtitle("UMAP after integration with harmony") +
-  guides(colour = guide_legend(override.aes = list(size = 3, alpha = 1)))
+  scale_color_brewer(palette = "Dark2", name = "sample") +
+  guides(color = guide_legend(override.aes = list(size = 3, alpha = 1))) +
+  ggtitle("UMAP after integration with harmony")
 ```
 
 How do you think this `harmony` UMAP compares to that from `fastMNN` integration?
@@ -757,19 +763,19 @@ Let's see how this UMAP looks colored by cell type, and faceted for visibility:
 ```{r plot harmony umap celltypes}
 scater::plotReducedDim(merged_sce,
                        dimred = "harmony_UMAP",
-                       colour_by = "celltype_broad",
+                       color_by = "celltype_broad",
                        point_size = 0.5,
                        point_alpha = 0.2,
                        # Specify variable for faceting
                        other_fields = "sample") +
+  scale_color_brewer(palette = "Dark2", name = "Broad celltype", na.value = "grey80") +
+  guides(color = guide_legend(override.aes = list(size = 3))) +
   ggtitle("UMAP after integration with harmony") +
-  guides(colour = guide_legend(override.aes = list(size = 3))) +
   facet_wrap(vars(sample))
 ```
 
-With the faceted view, we can now see that the small amounts of `Tumor_Myocyte` from other samples are "pulled" towards those cells in `SCPCL000481`.
-
-What other patterns do you see that are similar or different from the `fastMNN` UMAP?
+What do you now notice in this faceted view that wasn't clear previously?
+Are there other patterns you see that are similar or different from the `fastMNN` UMAP?
 How do you think `fastMNN` vs. `harmony` performed in integrating these samples?
 
 ### Export

scRNA-seq-advanced/03-differential_expression-live.Rmd

@@ -79,9 +79,7 @@ sample_metadata_file <- file.path(data_dir,
 
 # directory to store output
 deseq_dir <- file.path("analysis", "rms", "deseq")
-if(!dir.exists(deseq_dir)){
-  dir.create(deseq_dir, recursive = TRUE)
-}
+fs::dir_create(deseq_dir)
 
 # results file to output from DE analysis
 deseq_output_file <- file.path(deseq_dir, 
@@ -223,7 +221,7 @@ The samples which could be further classified have a mix of `Tumor_Mesoderm`, `T
 scater::plotReducedDim(integrated_sce,
                        dimred = "fastmnn_UMAP",
                        # color each point by cell type
-                       colour_by = "celltype_broad",
+                       color_by = "celltype_broad",
                        point_size= 0.5, 
                        point_alpha = 0.4)
 ```
@@ -267,6 +265,7 @@ ggplot(tumor_cells_df, aes(x = sample, fill = celltype_broad)) +
       y = "Proportion of cells", 
       fill = "Cell type"
     ) +
+  scale_fill_brewer(palette = "Dark2") +
   theme_bw() +
   theme(axis.text.x = element_text(angle = 90, vjust = 0.5))+
   # facet by diagnosis group 
@@ -694,7 +693,7 @@ scater::plotExpression(tumor_sce,
                        # a vector of genes to plot
                        features = genes_to_plot, 
                        x = "diagnosis_group", 
-                       colour_by = "diagnosis_group",
+                       color_by = "diagnosis_group",
                        other_fields = "celltype_broad",
                        point_size = 0.1) +
   # each celltype is its own column
@@ -703,7 +702,7 @@ scater::plotExpression(tumor_sce,
              rows = vars(Feature)) + 
   # change the font size of the facet labels
   theme(strip.text = element_text(size = 7)) + 
-  guides(colour = guide_legend(
+  guides(color = guide_legend(
     title = "Subtype", # update the legend title
     # change the size of the legend colors
     override.aes = list(size = 3, alpha = 1))