Fix bugs for dealing with processed objects with only 1 cell

bin/add_celltypes_to_sce.R

@@ -191,13 +191,13 @@ if (!is.null(opt$cellassign_predictions)) {
   if (file.size(opt$cellassign_predictions) > 0) {
     predictions <- readr::read_tsv(opt$cellassign_predictions)
   } else {
-    # if it's empty, then sce could not be converted to anndata and cell assign was not run
-    sce$cellassign_celltype_annotation <- "Not run"
+    predictions <- NULL
   }
 
-  # if the only column is the barcode column then CellAssign didn't complete successfully
+  # if the only column is the barcode column or if the predictions file was empty
+  # then CellAssign didn't complete successfully
   # otherwise add in cell type annotations and metadata to SCE
-  if (all(colnames(predictions) == "barcode")) {
+  if (is.null(predictions) || all(colnames(predictions) == "barcode")) {
     # if failed then note that in the cell type column
     sce$cellassign_celltype_annotation <- "Not run"
   } else {

bin/sce_qc_report.R

@@ -285,17 +285,20 @@ if (opt$celltype_report_file != "") {
     stop("Supplemental cell types report template not found.")
   }
 
-  # render report
-  rmarkdown::render(
-    input = opt$celltype_report_template,
-    output_file = basename(opt$celltype_report_file),
-    output_dir = dirname(opt$celltype_report_file),
-    intermediates_dir = tempdir(),
-    knit_root_dir = tempdir(),
-    envir = new.env(),
-    params = list(
-      library = metadata_list$library_id,
-      processed_sce = processed_sce
+  # only render supplemental report if there's more than one cell
+  if (ncol(processed_sce) > 1) {
+    # render report
+    rmarkdown::render(
+      input = opt$celltype_report_template,
+      output_file = basename(opt$celltype_report_file),
+      output_dir = dirname(opt$celltype_report_file),
+      intermediates_dir = tempdir(),
+      knit_root_dir = tempdir(),
+      envir = new.env(),
+      params = list(
+        library = metadata_list$library_id,
+        processed_sce = processed_sce
+      )
     )
-  )
+  }
 }

templates/qc_report/celltypes_qc.rmd

@@ -93,7 +93,7 @@ plot_umap <- function(
     umap_df,
     color_variable,
     legend_title,
-    point_size = umap_point_size,
+    point_size = point_size,
     legend_nrow = 2) {
   ggplot(umap_df) +
     aes(
@@ -370,7 +370,7 @@ create_celltype_n_table(celltype_df, cellassign_celltype_annotation) |>
 ```
 
 
-```{r, eval = has_umap}
+```{r, eval = has_umap && has_clusters}
 knitr::asis_output(glue::glue("
 ## UMAPs
 
@@ -388,7 +388,7 @@ umap_df <- lump_wrap_celltypes(celltype_df)
 
 <!-- First UMAP: clusters -->
 
-```{r, eval = has_umap && has_multiplex, results='asis'}
+```{r, eval = has_umap && has_multiplex && has_clusters, results='asis'}
 glue::glue("
   <div class=\"alert alert-info\">
     This library contains multiple samples that have not been batch-corrected, which may confound clustering assignments.
@@ -398,7 +398,7 @@ glue::glue("
 ```
 
 
-```{r eval = has_umap, message=FALSE, warning=FALSE}
+```{r eval = has_umap && has_clusters, message=FALSE, warning=FALSE}
 clusters_plot <- plot_umap(
   umap_df,
   cluster,
@@ -418,7 +418,7 @@ if (length(levels(umap_df$cluster)) <= 8) {
 ```
 
 
-```{r, eval = has_umap}
+```{r, eval = has_umap && has_celltypes}
 knitr::asis_output(
   'Next, we show UMAPs colored by cell types.
 For each cell typing method, we show a separate faceted UMAP.

templates/qc_report/celltypes_supplemental_report.rmd

@@ -264,8 +264,12 @@ has_cellassign <- "cellassign" %in% metadata(processed_sce)$celltype_methods
 has_submitter <- "submitter" %in% metadata(processed_sce)$celltype_methods &&
   !all(is.na(processed_sce$submitter_celltype_annotation)) # make sure they aren't all NA
 
-# check for umap
+# If at least 1 is present, we have cell type annotations.
+has_celltypes <- any(has_singler, has_cellassign, has_submitter)
+
+# check for umap and clusters
 has_umap <- "UMAP" %in% reducedDimNames(processed_sce)
+has_clusters <- "cluster" %in% names(colData(processed_sce))
 
 # what celltypes are available?
 available_celltypes <- c(
@@ -416,7 +420,7 @@ glue::glue("
 ```
 
 <!-- If not multiplexed, show the header, text, and heatmap --> 
-```{r, eval = !has_multiplex, results='asis'}
+```{r, eval = !has_multiplex && has_clusters, results='asis'}
 glue::glue("
   ## Unsupervised clustering
 
@@ -448,7 +452,7 @@ plot_height <- calculate_plot_height(
 ```
 
 
-```{r, eval = !has_multiplex, fig.height = plot_height, fig.width = 8.5, warning = FALSE}
+```{r, eval = !has_multiplex && has_clusters, fig.height = plot_height, fig.width = 8.5, warning = FALSE}
 jaccard_cluster_matrices |>
   create_heatmap_list(
     column_title = "Clusters",

templates/qc_report/main_qc_report.rmd

@@ -117,7 +117,7 @@ if (has_cellhash) {
 # check for umap and celltypes, but need to be sure that processed_sce exists first
 if (has_processed) {
   has_umap <- "UMAP" %in% reducedDimNames(processed_sce)
-
+  has_clusters <- "cluster" %in% names(colData(processed_sce))
   has_singler <- "singler" %in% metadata(processed_sce)$celltype_methods
   has_cellassign <- "cellassign" %in% metadata(processed_sce)$celltype_methods
   has_submitter <- "submitter" %in% metadata(processed_sce)$celltype_methods &&
@@ -129,6 +129,7 @@ if (has_processed) {
   is_supplemental <- FALSE # this is not the celltype supp report
 } else {
   has_umap <- FALSE
+  has_clusters <- FALSE
   has_singler <- FALSE
   has_cellassign <- FALSE
   has_submitter <- FALSE