Add optional scree plot to report

assets/differentialabundance_report.Rmd

@@ -24,6 +24,7 @@ params:
   report_title: NULL,
   report_author: NULL,
   report_description: NULL,
+  report_scree: NULL
   observations_type: NULL
   observations: NULL                                          # GSE156533.samplesheet.csv
   observations_id_col: NULL
@@ -495,10 +496,12 @@ cat(paste0("\n### ", ucfirst(params$observations_type), " relationships\n"))
 Principal components analysis was conducted based on the `r params$exploratory_n_features` most variable `r params$features_type`s. Each component was annotated with its percent contribution to variance. 
 
 ```{r, echo=FALSE, results='asis'}
+# Create nested list to save the percentVars for reusing in the scree plot
+percentVar_list <- list()
 for (assay_type in rev(names(assay_data))){
 
   pca_data <- pca_datas[[assay_type]]
-
+  percentVar_list[[assay_type]] <- list()
   for (iv in informative_variables){
 
     cat(paste0("\n##### ", prettifyVariablename(assay_type), " (", iv, ")\n"))
@@ -543,6 +546,31 @@ for (assay_type in rev(names(assay_data))){
 
       print(htmltools::tagList(do.call("plotly_scatterplot", plot_args)))
     }
+    percentVar_list[[assay_type]][[iv]] <- percentVar
+  }
+}
+```
+
+```{r, echo=FALSE, results='asis', eval=params$report_scree}
+cat(paste0("\n#### Scree plot {.tabset}"))
+cat(paste0("\nThe following scree plot visualizes what percentage of total variation in the data can be explained by each of the principal components computed.\n"))
+
+for (assay_type in names(percentVar_list)) {
+  for (iv in names(percentVar_list[[assay_type]])) {
+    percentVarData <- data.frame(percentVar_list[[assay_type]][[iv]])
+    colnames(percentVarData) <- c("var_explained")
+    percentVarData$PCA <- as.numeric(rownames(percentVarData))
+    write.table(percentVarData, file=paste0("/home-link/iivow01/git/save_differentialabundance/error/", assay_type, "_", iv, "_varex.tsv"), quote=F, sep="\t")
+    cat(paste0("\n##### ", prettifyVariablename(assay_type), " (", iv, ")\n"))
+    print(
+      ggplot(percentVarData, aes(x=factor(PCA),y=var_explained, group=1)) +
+      theme_bw() +
+      geom_point(size=4) +
+      geom_line(linetype="dashed") +
+      xlab("PC") +
+      ylab("Percent variance explained")
+    )
+    cat("\n")
   }
 }
 ```
@@ -902,4 +930,4 @@ print( htmltools::tagList(datatable(versions_table, caption = "Software versions
 
 ```{r, echo=FALSE, results='asis'}
 htmltools::includeMarkdown(params$citations)
-```
+```

nextflow.config

@@ -28,6 +28,7 @@ params {
     report_title               = null
     report_author              = null
     report_description         = null
+    report_scree               = false
 
     // Sample sheet options
     observations_type          = 'sample'

nextflow_schema.json

@@ -952,6 +952,11 @@
                     "default": "None",
                     "fa_icon": "fas fa-feather",
                     "description": "A description for reporting outputs"
+                },
+                "report_scree": {
+                    "type": "boolean",
+                    "default": "false",
+                    "description": "Whether to generate a scree plot in the report"
                 }
             },
             "required": ["report_file", "logo_file", "css_file"]