switch mqc to 1.20, adjust prettier config again

.editorconfig

@@ -37,6 +37,6 @@ indent_style = unset
 indent_style = unset
 
 # ignore Rmd files
-[*.{Rmd}]
+[*.Rmd]
 indent_style = unset
 trim_trailing_whitespace = unset

.prettierignore

@@ -10,7 +10,6 @@ testing/
 testing*
 *.pyc
 bin/
-assets/rmarkdown-reports/
-reportDashboard.Rmd
-sampleAmplicons.Rmd
-sampleSubpage.Rmd
+
+# Rmd files
+**/*.Rmd

assets/rmarkdown-reports/reportDashboard.Rmd

@@ -47,16 +47,16 @@ control_substrings = c("neg", "ntc", "blank")
 summary_df <- read.table(params$summary_df, header = TRUE, sep = ",")
 
 # Genome completeness colour palette
-get_genome_completeness_color <- function(value) { 
+get_genome_completeness_color <- function(value) {
   # Create the Red to Yellow to Green color palette for 100 values
   red_to_green_palette <- colorRampPalette(c("#FF0039", "#FF7518", "#3FB618"))(100)
-  
+
   # Calculate the index of the color in the palette based on the input value
   if (value == 0) {
     value <- value + 0.00001
   }
   color_index <- ceiling(value * 100)
-  
+
   # Return the corresponding color with alpha 0.7
   selected_color <- red_to_green_palette[color_index]
   selected_color <- adjustcolor(selected_color, alpha = 0.7)
@@ -91,15 +91,15 @@ add_pass_value_int <- function(df, column) {
   for (i in 1:length(split_values)) {
     # Check if any key phrase is contained in the split values
     matching_phrases <- intersect(split_values[[i]], key_phrases)
-    
+
     if (length(matching_phrases) > 0) {
       # If matching phrases are found, assign the corresponding integer value
       results[i] <- min(values[match(matching_phrases, key_phrases)])
     } else {
       results[i] <- 0  # If none of the key phrases are found, assign 0
     }
   }
-  
+
   # Return results
   return(results)
 }
@@ -163,7 +163,7 @@ Row {dataset-height=250}
 df <- select(summary_df, sample, genome_completeness)
 
 # Creat plot, adding in titles and the PASS/WARN hlines (may need to figure out how to label these)
-p <- plot_ly(df, x = ~sample, y=~genome_completeness, type = 'bar', color = ~genome_completeness, colors = "RdYlGn", name="") %>% 
+p <- plot_ly(df, x = ~sample, y=~genome_completeness, type = 'bar', color = ~genome_completeness, colors = "RdYlGn", name="") %>%
   layout(
     title = "Sample Genome Completeness",
     xaxis = list(title = "Sample Name"),
@@ -203,7 +203,7 @@ df <- df %>%
 df$completeness_int_color <- add_pass_value_int(df, 'QC Pass')
 
 # Using datatable, removing the index row and adding in highlights for the status
-summary_dt <- df %>% 
+summary_dt <- df %>%
   DT::datatable(rownames = FALSE,
     options = list(
       scrollX = FALSE,
@@ -235,19 +235,19 @@ summary_dt
 subpages = NULL
 # Set knitr options to allow duplicate labels
 options(knitr.duplicate.label = 'allow')
-# Create temporary environment which we use for knitting subpages.RMD 
+# Create temporary environment which we use for knitting subpages.RMD
 subpage_env <- new.env()
 
 # Create subpages
 for ( samplev in summary_df$sample ) {
   # Filter data for sample
-  sample_df <- summary_df %>% 
+  sample_df <- summary_df %>%
     filter(sample == samplev)
-  
+
   # Assign data to subpage_env
   assign("sample_df", sample_df, subpage_env)
   assign("sample", samplev, subpage_env)
-  
+
   # Knit sample subpage using the subpage_env and add result to out vector
   subpages = c(subpages, knitr::knit_child('sampleSubpage.Rmd', envir = subpage_env))
 }
@@ -324,7 +324,7 @@ Row {data-height=500}
 # Have to concat the substrings to grep with
 substring_regex <- paste(control_substrings, collapse = "|")
 df <- select(summary_df, sample, neg_control_info, mean_sequencing_depth, median_sequencing_depth, num_aligned_reads, total_variants, genome_completeness, qc_pass)
-df <- df %>% 
+df <- df %>%
   filter(
     grepl(substring_regex, sample, ignore.case = TRUE)
   ) %>%
@@ -343,7 +343,7 @@ df <- df %>%
 df$completeness_int_color <- add_pass_value_int(df, 'QC Pass')
 
 # Using datatable, removing the index row and adding in highlights for the status
-summary_dt <- df %>% 
+summary_dt <- df %>%
   DT::datatable(rownames = FALSE,
     options = list(
       scrollX = FALSE,
@@ -402,7 +402,7 @@ for (key_process in names(yaml_data)) {
 
 # DF
 df <- data.frame("Process Name" = process, "Software" = softwares, "Version" = versions)
-ddt <- df %>% 
+ddt <- df %>%
   DT::datatable(rownames = FALSE, class = 'cell-border stripe',
     options = list(
       scrollX = FALSE,

assets/rmarkdown-reports/sampleAmplicons.Rmd

@@ -12,7 +12,7 @@ region based on the primer scheme. If there are no N's, the completeness is cons
 to be 100% (1.0 on the heatmap).
 
 Amplicon depth is calculated using bedtools coverage looking for reads in the bam file
-that overlap 85% of the amplicon region. 
+that overlap 85% of the amplicon region.
 
 These plots are most useful for determining if there are any amplicons with consistent
 dropouts or low sequencing depth across the run that can be adjusted for better results

assets/rmarkdown-reports/sampleSubpage.Rmd

@@ -22,7 +22,7 @@ valueBox(sample, icon = "")
 completeness <- sample_df$genome_completeness[1]
 sec_color <- get_genome_completeness_color(completeness)
 valueBox(
-    completeness, 
+    completeness,
     icon = "",
     color = sec_color
 )
@@ -84,7 +84,7 @@ text_info <- paste(
 # Plot variation table
 # I want to make it so that the colors are always the same but was having some trouble with that
 p <- plot_ly(variants) %>%
-  add_trace(x = variants[['position']], y = variants[['percentage_nonref']], 
+  add_trace(x = variants[['position']], y = variants[['percentage_nonref']],
       type = 'scatter', mode = 'markers', color = variants[['variant_type']],
       marker = list(sizemode = 'diameter', size = 15.0, opacity = 0.7),
       text = text_info,
@@ -114,7 +114,7 @@ p
 var_f <- paste0("./variant_tsvs/", sample, ".vcf.tsv")
 if (file.exists(var_f)) {
   variants <- read.table(var_f, header = TRUE, sep = "\t")
-  ddt <- variants %>% 
+  ddt <- variants %>%
     DT::datatable(rownames = FALSE,
       options = list(
         scrollX = FALSE
@@ -123,7 +123,7 @@ if (file.exists(var_f)) {
   variants <- data.frame(matrix(ncol = 4, nrow = 0))
   colv <- c("Pos", "Ref", "Alt", "Qual")
   colnames(variants) <- colv
-  ddt <- variants %>% 
+  ddt <- variants %>%
     DT::datatable(rownames = FALSE)
 }
 ddt
@@ -166,7 +166,7 @@ ggplot(coverage) +
   geom_line(aes(x=position, y=depth)) +
   scale_y_log10() +
   xlab("Genome Position (nt)") +
-  ylab("Sequencing Depth") + 
+  ylab("Sequencing Depth") +
   geom_rect(data=rect_data, aes(xmin=xmin,xmax=xmax,ymin=ymin,ymax=ymax,fill=col),alpha=0.1) +
   scale_fill_identity()
 ```

modules/local/multiqc/environment.yml

@@ -4,4 +4,4 @@ channels:
   - bioconda
   - defaults
 dependencies:
-  - bioconda::multiqc=1.21
+  - bioconda::multiqc=1.20

modules/local/multiqc/main.nf

@@ -8,8 +8,8 @@ process MULTIQC_SAMPLE {
 
     conda "${moduleDir}/environment.yml"
     container "${ workflow.containerEngine == 'singularity' && !task.ext.singularity_pull_docker_container ?
-        'https://depot.galaxyproject.org/singularity/multiqc:1.21--pyhdfd78af_0' :
-        'biocontainers/multiqc:1.21--pyhdfd78af_0' }"
+        'https://depot.galaxyproject.org/singularity/multiqc:1.20--pyhdfd78af_0' :
+        'biocontainers/multiqc:1.20--pyhdfd78af_0' }"
 
     input:
     path multiqc_config