Merge pull request #266 from AlexsLemonade/jashapiro/reformat

pre-commit reformats

.github/workflows/build-docker.yaml

@@ -48,4 +48,3 @@ jobs:
           labels: ${{ steps.meta.outputs.labels }}
           cache-from: type=gha
           cache-to: type=gha
-

R/sce_to_anndata.R

@@ -24,12 +24,11 @@
 #' )
 #' }
 sce_to_anndata <- function(
-  sce, 
-  anndata_file, 
-  x_assay_name = "counts", 
-  compression = c("gzip", "none", "lzf"), 
-  ...
-) {
+    sce,
+    anndata_file,
+    x_assay_name = "counts",
+    compression = c("gzip", "none", "lzf"),
+    ...) {
   if (!requireNamespace("zellkonverter", quietly = TRUE)) {
     stop("The zellkonverter package must be installed to convert objects to AnnData. No output file written.")
   }

inst/rmd/cite_qc.rmd

@@ -4,32 +4,34 @@
 
 ```{r}
 # add rowData if missing
-if (is.null(rowData(cite_exp)$detected)){
+if (is.null(rowData(cite_exp)$detected)) {
   cite_exp <- scuttle::addPerFeatureQCMetrics(cite_exp)
 }
 
 cell_adt_counts <- Matrix::colSums(counts(cite_exp))
 
 cite_information <- tibble::tibble(
-  "Number of ADTs assayed" = 
-    format(nrow(cite_exp), big.mark = ',', scientific = FALSE),
-  "CITE-seq reads sequenced" = 
-    format(cite_meta$total_reads, big.mark = ',', scientific = FALSE),
-  "Percent CITE-seq reads mapped to ADTs" = 
-    paste0(round(cite_meta$mapped_reads/cite_meta$total_reads * 100, digits = 2), "%"),
-  "Percent of ADTs in cells" = 
-    paste0(round(sum(cell_adt_counts)/cite_meta$mapped_reads * 100, digits = 2), "%"),
-  "Percent of cells with ADTs" = 
-    paste0(round(sum(cell_adt_counts > 0)/length(cell_adt_counts) * 100, digits = 2), "%"),
-  "Median ADT UMIs per cell" = 
-    format(median(cell_adt_counts), big.mark = ',', scientific = FALSE)
-  )|>
+  "Number of ADTs assayed" =
+    format(nrow(cite_exp), big.mark = ",", scientific = FALSE),
+  "CITE-seq reads sequenced" =
+    format(cite_meta$total_reads, big.mark = ",", scientific = FALSE),
+  "Percent CITE-seq reads mapped to ADTs" =
+    paste0(round(cite_meta$mapped_reads / cite_meta$total_reads * 100, digits = 2), "%"),
+  "Percent of ADTs in cells" =
+    paste0(round(sum(cell_adt_counts) / cite_meta$mapped_reads * 100, digits = 2), "%"),
+  "Percent of cells with ADTs" =
+    paste0(round(sum(cell_adt_counts > 0) / length(cell_adt_counts) * 100, digits = 2), "%"),
+  "Median ADT UMIs per cell" =
+    format(median(cell_adt_counts), big.mark = ",", scientific = FALSE)
+) |>
   t()
 
-knitr::kable(cite_information, align = 'r') |>
-  kableExtra::kable_styling(bootstrap_options = "striped",
-                            full_width = FALSE,
-                            position = "left") |>
+knitr::kable(cite_information, align = "r") |>
+  kableExtra::kable_styling(
+    bootstrap_options = "striped",
+    full_width = FALSE,
+    position = "left"
+  ) |>
   kableExtra::column_spec(2, monospace = TRUE)
 ```
 
@@ -39,14 +41,15 @@ antibody_tags <- as.data.frame(rowData(cite_exp)) |>
   tibble::rownames_to_column("Antibody") |>
   arrange(desc(mean)) |>
   select("Antibody",
-         "Mean UMI count per cell" = mean,
-         "Percent of cells detected" = detected)
+    "Mean UMI count per cell" = mean,
+    "Percent of cells detected" = detected
+  )
 
 knitr::kable(antibody_tags, digits = 2) |>
-  kableExtra::kable_styling(bootstrap_options = c("striped", "condensed"),
-                            full_width = FALSE,
-                            position = "left",
-                            ) |>
-  kableExtra::column_spec(2:3, monospace = TRUE) 
-  
+  kableExtra::kable_styling(
+    bootstrap_options = c("striped", "condensed"),
+    full_width = FALSE,
+    position = "left",
+  ) |>
+  kableExtra::column_spec(2:3, monospace = TRUE)
 ```

inst/rmd/multiplex_qc.rmd

@@ -4,32 +4,34 @@
 
 ```{r}
 # add rowData if missing
-if (is.null(rowData(multiplex_exp)$detected)){
+if (is.null(rowData(multiplex_exp)$detected)) {
   multiplex_exp <- scuttle::addPerFeatureQCMetrics(multiplex_exp)
 }
 
 cell_hto_counts <- Matrix::colSums(counts(multiplex_exp))
 
 hto_information <- tibble::tibble(
-  "Number of HTOs assayed" = 
-    format(nrow(multiplex_exp), big.mark = ',', scientific = FALSE),
-  "Multiplex reads sequenced" = 
-    format(multiplex_meta$total_reads, big.mark = ',', scientific = FALSE),
-  "Percent multiplex reads mapped to ADTs" = 
-    paste0(round(multiplex_meta$mapped_reads/multiplex_meta$total_reads * 100, digits = 2), "%"),
-  "Percent of HTOs in cells" = 
-    paste0(round(sum(cell_hto_counts)/multiplex_meta$mapped_reads * 100, digits = 2), "%"),
-  "Percent of cells with HTOs" = 
-    paste0(round(sum(cell_hto_counts > 0)/length(cell_hto_counts) * 100, digits = 2), "%"),
-  "Median HTO UMIs per cell" = 
-    format(median(cell_hto_counts), big.mark = ',', scientific = FALSE)
-  )|>
+  "Number of HTOs assayed" =
+    format(nrow(multiplex_exp), big.mark = ",", scientific = FALSE),
+  "Multiplex reads sequenced" =
+    format(multiplex_meta$total_reads, big.mark = ",", scientific = FALSE),
+  "Percent multiplex reads mapped to ADTs" =
+    paste0(round(multiplex_meta$mapped_reads / multiplex_meta$total_reads * 100, digits = 2), "%"),
+  "Percent of HTOs in cells" =
+    paste0(round(sum(cell_hto_counts) / multiplex_meta$mapped_reads * 100, digits = 2), "%"),
+  "Percent of cells with HTOs" =
+    paste0(round(sum(cell_hto_counts > 0) / length(cell_hto_counts) * 100, digits = 2), "%"),
+  "Median HTO UMIs per cell" =
+    format(median(cell_hto_counts), big.mark = ",", scientific = FALSE)
+) |>
   t()
 
-knitr::kable(hto_information, align = 'r') |>
-  kableExtra::kable_styling(bootstrap_options = "striped",
-                            full_width = FALSE,
-                            position = "left") |>
+knitr::kable(hto_information, align = "r") |>
+  kableExtra::kable_styling(
+    bootstrap_options = "striped",
+    full_width = FALSE,
+    position = "left"
+  ) |>
   kableExtra::column_spec(2, monospace = TRUE)
 ```
 
@@ -40,15 +42,17 @@ hto_tags <- as.data.frame(rowData(multiplex_exp)) |>
   tibble::rownames_to_column("Hashtag Oligo (HTO)") |>
   arrange(desc(mean)) |>
   select("Hashtag Oligo (HTO)",
-         "Mean UMI count per cell" = mean,
-         "Percent of cells detected" = detected)
+    "Mean UMI count per cell" = mean,
+    "Percent of cells detected" = detected
+  )
 
 knitr::kable(hto_tags, digits = 2) |>
-  kableExtra::kable_styling(bootstrap_options = c("striped", "condensed"),
-                            full_width = FALSE,
-                            position = "left",
-                            ) |>
-  kableExtra::column_spec(2:3, monospace = TRUE) 
+  kableExtra::kable_styling(
+    bootstrap_options = c("striped", "condensed"),
+    full_width = FALSE,
+    position = "left",
+  ) |>
+  kableExtra::column_spec(2:3, monospace = TRUE)
 ```
 
 ## Demultiplexing Sample Calls 
@@ -62,41 +66,43 @@ The genetic demultiplexing results are reported under the `vireo` column in the
 
 ```{r, results='asis'}
 # grab columns that have demuxing results as not all methods could be present
-# e.g. vireo is only used if a matching bulk RNA seq library is there, 
+# e.g. vireo is only used if a matching bulk RNA seq library is there,
 # otherwise those calls will not be present
 demux_methods <- c("hashedDrops_sampleid", "HTODemux_sampleid", "vireo_sampleid")
 demux_columns <- colnames(colData(filtered_sce)) %in% demux_methods
 
-if(any(demux_columns)){
- # create a table summarizing demuxing calls for each method used 
+if (any(demux_columns)) {
+  # create a table summarizing demuxing calls for each method used
   demux_calls <- as.data.frame(colData(filtered_sce)[, demux_columns]) |>
     # remove _sampleid at the end of the column names
-    dplyr::rename_with(~stringr::str_remove(., "_sampleid")) |>
-    tidyr::pivot_longer(cols = everything(),
-                        names_to = "demux_method",
-                        values_to = "Sample") |>
+    dplyr::rename_with(~ stringr::str_remove(., "_sampleid")) |>
+    tidyr::pivot_longer(
+      cols = everything(),
+      names_to = "demux_method",
+      values_to = "Sample"
+    ) |>
     dplyr::count(Sample, demux_method) |>
-    tidyr::pivot_wider(names_from = demux_method,
-                       values_from = n)
-  
-  
+    tidyr::pivot_wider(
+      names_from = demux_method,
+      values_from = n
+    )
+
+
   knitr::kable(demux_calls, digits = 2) |>
-    kableExtra::kable_styling(bootstrap_options = c("striped", "condensed"),
-                              full_width = FALSE,
-                              position = "left",
+    kableExtra::kable_styling(
+      bootstrap_options = c("striped", "condensed"),
+      full_width = FALSE,
+      position = "left",
     ) |>
-    kableExtra::column_spec(2:ncol(demux_calls), monospace = TRUE)  
-}else{
+    kableExtra::column_spec(2:ncol(demux_calls), monospace = TRUE)
+} else {
   glue::glue("
     <div class=\"alert alert-info\">
-    
+
     Demultiplexing was not applied to this library using any of the above mentioned methods.
     Sample calls cannot be reported.
-    
+
     </div>
   ")
 }
-
 ```
-
-