Little october fixes

scRNA-seq/05-clustering_markers_scRNA.Rmd

@@ -36,7 +36,11 @@ set.seed(12345)
 data_dir <- file.path("data", "hodgkins")
 
 # normalized data file
-normalized_rds <- file.path(data_dir, "normalized", "normalized_hodgkins_sce.rds")
+normalized_rds <- file.path(
+  data_dir,
+  "normalized",
+  "normalized_hodgkins_sce.rds"
+)
 
 # Output directory for markers
 marker_dir <- file.path("analysis", "hodgkins", "markers")
@@ -170,10 +174,12 @@ Now we can plot the results of our graph-based clustering.
 This time we will also use the `text_by` argument to include the cluster ids directly on the plot.
 
 ```{r plot_nnclust, live = TRUE}
-plotReducedDim(hodgkins_sce,
-               "UMAP",
-               color_by = "nncluster",
-               text_by = "nncluster")
+plotReducedDim(
+  hodgkins_sce,
+  "UMAP",
+  color_by = "nncluster",
+  text_by = "nncluster"
+)
 ```
 
 - How do these results compare to the k-means clustering result?
@@ -212,9 +218,11 @@ For now, we will use the `findMarkers()` function to rank the genes in each clus
 ```{r find_markers, live = TRUE}
 # use `findMarkers()` to calculate how well each gene
 #  differentiates each cluster from *all* other clusters
-markers <- scran::findMarkers(hodgkins_sce,
-                              groups = hodgkins_sce$nncluster,
-                              pval.type = "all")
+markers <- scran::findMarkers(
+  hodgkins_sce,
+  groups = hodgkins_sce$nncluster,
+  pval.type = "all"
+)
 ```
 
 Next we can look at one of those tables.
@@ -242,14 +250,14 @@ The results are returned as a new list.
 purrr::map(
   as.list(markers[1:3]), # select the first 3 clusters and convert to a 'regular' list for purrr
   head # the function to apply (note no parenthesis)
-  )
+)
 ```
 
 This returns a list of data frames, which isn't quite what we want.
 
 There is no built-in function that will give us just the first few _row names_, so we will have to define one.
-As of version 4.1, R introduced a new approach to defining _anonymous functions_ - that is, functions you can quickly define "on-the-fly" without formally assigning them to a function name.
-They are handy when you need to do a very short task that requires a function, but it isn't really a function you need beyond this context.
+We'll do this with an "anonymous" function (sometimes called a lambda expression).
+Anonymous functions are handy when you need to do a very short task that requires a function, but it isn't really a function you need beyond this context.
 This new anonymous syntax looks like this: `\(x)...` (or for slightly longer code, use curly braces as in `\(x) {...}`).
 This defines a function that takes one argument, `x`, with `...` indicating where you would put the expression to calculate.
 
@@ -261,7 +269,7 @@ purrr::map(
   # convert markers to a 'regular' list for purrr
   as.list(markers),
   # our custom function:
-  \(x) head( rownames(x) )
+  \(x) head(rownames(x))
 )
 ```
 
@@ -288,12 +296,13 @@ In addition to writing the tables out, we are saving the data frames we created
 ```{r write_tables}
 marker_df_list <- purrr::imap(
   as.list(markers), # convert markers to a 'regular' list for purrr
-  # purrr function: x is the list element, y is the element name (number here)
+  # anonymous function: x is the list element, y is the element name (number here)
   \(table, id) {
     as.data.frame(table) |> # first convert to a data frame
       tibble::rownames_to_column("gene") |> # make genes a column
       dplyr::arrange(FDR) |> # sort to be sure small FDR genes are first
-      readr::write_tsv( # write each data frame to a file
+      # write each data frame to a file
+      readr::write_tsv(
         file.path(
           marker_dir, # construct the output path
           sprintf("cluster%02d_markers.tsv", as.integer(id)) # format cluster numbers in file names with leading zeros
@@ -332,8 +341,7 @@ gene_id <- gene_info$ID[rank]
 symbol <- gene_info$Symbol[rank]
 
 # Plot UMAP results colored by expression
-plotReducedDim(hodgkins_sce, "UMAP",
-               color_by = gene_id) +
+plotReducedDim(hodgkins_sce, "UMAP", color_by = gene_id) +
   # label the guide with the gene symbol
   guides(color = guide_colorbar(title = symbol))
 ```

scRNA-seq/06-celltype_annotation.Rmd

@@ -152,7 +152,7 @@ While we have the ADT data, it is _not_ being used for this stage of the analysi
 ```{r cluster cells, live=TRUE}
 # perform clustering
 nn_clusters <- scran::clusterCells(
-  sce, 
+  sce,
   use.dimred = "PCA", # PCA input
   BLUSPARAM = bluster::NNGraphParam() # graph clustering & parameters
 )
@@ -470,9 +470,11 @@ Still annoyingly, however, when we change the palette, the legend title defaults
 
 ```{r plot celltype umap palette}
 scater::plotUMAP(sce, color_by = "celltype_main") +
-  scale_color_brewer(name = "Cell type", # legend title
-                     palette = "Dark2",      # color palette
-                     na.value = "gray80")    # use light gray for NA values
+  scale_color_brewer(
+    name = "Cell type", # legend title
+    palette = "Dark2", # color palette
+    na.value = "gray80"
+  ) # use light gray for NA values
 ```
 
 We seem to have a pretty good set of cell type assignments, with most falling into groupings consistent with what we see in the UMAP plot.
@@ -519,7 +521,7 @@ singler_finemodel <- SingleR::trainSingleR(
   monaco_ref, # reference dataset
   labels = monaco_ref$label.fine, # labels for training dataset
   # consider only genes in the sce object
-  test.genes = rownames(sce), 
+  test.genes = rownames(sce),
   # use DE to select genes (default)
   genes = "de",
   # parallel processing
@@ -569,28 +571,32 @@ After that, we will use `fct_relevel` to put the remaining factor levels in the
 collapsed_labels <- singler_result_fine$pruned.labels |>
   forcats::fct_collapse(
     "Monocytes" = c(
-        "Classical monocytes",
-        "Intermediate monocytes",
-        "Non classical monocytes"),
+      "Classical monocytes",
+      "Intermediate monocytes",
+      "Non classical monocytes"
+    ),
     "Dendritic cells" = c(
-        "Myeloid dendritic cells",
-        "Plasmacytoid dendritic cells"),
+      "Myeloid dendritic cells",
+      "Plasmacytoid dendritic cells"
+    ),
     "T cells" = c(
-        "MAIT cells",
-        "Non-Vd2 gd T cells",
-        "Vd2 gd T cells"),
+      "MAIT cells",
+      "Non-Vd2 gd T cells",
+      "Vd2 gd T cells"
+    ),
     "Helper T cells" = c(
-        "Th1 cells",
-        "Th1/Th17 cells",
-        "Th17 cells",
-        "Th2 cells",
-        "Follicular helper T cells"),
+      "Th1 cells",
+      "Th1/Th17 cells",
+      "Th17 cells",
+      "Th2 cells",
+      "Follicular helper T cells"
+    ),
     "B cells" = c(
-        "Naive B cells",
-        "Switched memory B cells",
-        "Non-switched memory B cells",
-        "Exhausted B cells",
-        "Plasmablasts"
+      "Naive B cells",
+      "Switched memory B cells",
+      "Non-switched memory B cells",
+      "Exhausted B cells",
+      "Plasmablasts"
     )
   ) |>
   # order for plotting
@@ -646,7 +652,7 @@ Since the total number of cells differs across clusters, we'd like to convert th
 We'll do this by going through the table column by column and dividing each value by the sum for that cluster.
 This will give us normalized values where the values in each column now sum to 1.
 To do that, we will use the `apply` function, which allows us to operate on a matrix row by row or column by column, applying a function to each "slice".
-Since the function we want to apply is very short, we will use R's new (as of  version 4.1) anonymous function shorthand:
+Since the function we want to apply is very short, we will use R's anonymous function shorthand:
 `\(x) ...` can be used to define a function that that takes as input values `x` (where the `...` is where you would put the expression to calculate).
 Here we will apply the expression `x/sum(x)`, which will divide each element of a vector `x` by the sum of its values.
 
@@ -655,7 +661,7 @@ Here we will apply the expression `x/sum(x)`, which will divide each element of
 type_cluster_tab <- apply(
   type_cluster_tab,
   2, # apply function to columns
-  \(x) x/sum(x) # function to apply
+  \(x) x / sum(x) # function to apply
 )
 # print the normalized values
 type_cluster_tab[1:5, 1:5]

scripts/link-data.sh

@@ -76,7 +76,7 @@ link_locs=(
   spatial/data/crc-hd/crc_single_cell_normalized_sce.rds
   spatial/data/crc-hd/single_cell
   spatial/data/crc-hd/visium_hd
-  spatial/data/crc-v1/crc_normalized_spe.rds
+  spatial/data/crc-v1/normalized/crc_normalized_spe.rds
   spatial/data/osteo/GSM8478586/normalized/osteo_normalized_spe.rds
   spatial/data/ovarian-carcinoma/spaceranger
   spatial/data/reference

spatial/02-spatial_clustering.Rmd

@@ -189,7 +189,7 @@ gene_variance <- scran::modelGeneVar(spe)
 # calculate the top 2000 HVGs
 hvg_vector <- scran::getTopHVGs(gene_variance, n = 2000)
 
-# calculate PCE using the top HVGs, saving back to the SPE object
+# calculate PCA using the top HVGs, saving back to the SPE object
 spe <- scater::runPCA(spe, subset_row = hvg_vector)
 ```
 

spatial/03-deconvolution.Rmd

@@ -445,8 +445,8 @@ We'll save the normalized values to a new assay called `proportions`.
 assay(rctd_full, "proportions") <- assay(rctd_full, "weights") |>
   # convert to regular matrix for normalization
   as.matrix() |>
-  # new in R 4.1, a function to normalize!
-  # use 2 for columns
+  # normalize columns so they sum to 1
+  # use 2 for columns (1 would do this for rows)
   proportions(margin = 2) |>
   # convert back to sparse matrix
   as("CsparseMatrix")