Merge pull request #18 from MonashBioinformaticsPlatform/dev_for_R4.2.1

Updated to R 4.2.1

Dockerfile

@@ -7,10 +7,10 @@ RUN apt-get update && apt-get install -yq \
 
 RUN Rscript -e 'install.packages(c("devtools", "tidyverse", "ggrepel", "httr", "rjson", "mvtnorm", "tmvtnorm","impute", \
 "pcaMethods", "imputeLCMD", "plotly", "DT", "BiocManager","testthat", "RColorBrewer", "shiny","shinyalert","shinydashboard", \
-"shinyjs", "svglite", "rhandsontable", "shinyBS", "shinyWidgets", "ggVennDiagram"), dependencies=TRUE)'
+"shinyjs", "svglite", "rhandsontable", "shinyBS", "shinyWidgets", "ggVennDiagram", "shinycssloaders"), dependencies=TRUE)'
 
 #FROM bioconductor/bioconductor_docker:RELEASE_3_15
-RUN Rscript -e 'BiocManager::install(pkgs=c("DEP", "DT", "SummarizedExperiment", "limma", "ComplexHeatmap", ask=F))'
+RUN Rscript -e 'BiocManager::install(pkgs=c("DEP", "DT", "SummarizedExperiment", "limma", "ComplexHeatmap", ask=F), dependencies=TRUE)'
 
 COPY ./ /srv/shiny-server/lfq-analyst
 COPY shiny-server.conf /etc/shiny-server/shiny-server.conf

LFQ_report.Rmd

@@ -43,32 +43,31 @@ Statistical analysis was performed using an in-house generated R script based on
 
 #### MaxQuant result output contains `r nrow(params$data)` proteins groups of which _`r nrow(params$dep())`_ proteins were reproducibly quantified.
 
-#### `r params$num_signif` proteins differ significantly between samples.
+#### `r params$num_signif` proteins differ significantly between samples.  
 
 \pagebreak
 
 
-
 ## Exploratory Analysis (QC Plots)
 
 #### Principle Component Analysis (PCA) plot
 
 
-```{r pca_plot, echo=FALSE, fig.height= 4}
+```{r pca_plot, echo=FALSE, fig.height= 4, fig.align='center', warning=FALSE}
 print(params$pca_input())
 ```
 \pagebreak
 
 #### Sample Correlation matrix
 
-```{r correlation_heatmap, echo=FALSE, fig.keep='first'}
+```{r correlation_heatmap, echo=FALSE, fig.keep='first',fig.align='center'}
 print(params$correlation_input())
 ```
 \pagebreak
 
 #### Sample Coefficient of variation (CVs)
 
-```{r sample_cv,  echo=FALSE, warning=FALSE }
+```{r sample_cv,  echo=FALSE, warning=FALSE, message=FALSE, fig.align='center'}
 print(params$cvs_input())
 ```
 \pagebreak
@@ -117,13 +116,15 @@ print(params$imputation_input())
   A plot representing an overview of expression of all significant (differencially expressed) proteins (rows) in all samples (columns).
 
 
-```{r heatmap_2,  echo=FALSE, warning=FALSE, results='hide', fig.keep='all'}
+```{r heatmap_2,  echo=FALSE, warning=FALSE, results='hide', fig.keep='first',fig.align='center'}
 print(params$heatmap_input())
 ```
 
+\pagebreak
+
 #### Volcano Plots
 
-```{r volcano,  echo=FALSE, warning=FALSE, comment=NA}
+```{r volcano,  echo=FALSE, warning=FALSE, comment=NA,fig.align='center'}
 for(i in params$tested_contrasts){
  # print(paste0('volcano_plot_',i,sep=""))
   print(plot_volcano_new(params$dep(),contrast = i,label_size = 2, add_names = F))

R/functions.R

@@ -342,7 +342,7 @@ get_cluster_heatmap <- function(dep, type = c("contrast", "centered"),
                 ...)
   # return (row_order(ht1))
   # Return data.frame
-  draw(ht1, heatmap_legend_side = "top")
+  p <- draw(ht1, heatmap_legend_side = "top")
   row_clusters<- row_order(ht1)
   #mat<-as.matrix(df)
 
@@ -358,7 +358,8 @@ get_cluster_heatmap <- function(dep, type = c("contrast", "centered"),
   #     out <- cbind(out, clu)
   #   }
   # }
-  return(row_clusters)
+  heatmap_list <- list(p, row_clusters)
+  return(heatmap_list)
 }
 
 # Internal function to get ComplexHeatmap::HeatmapAnnotation object
@@ -632,7 +633,7 @@ get_results_proteins <- function(dep) {
 
   # Obtain average enrichments of conditions versus the control condition
   ratio <- as.data.frame(row_data) %>%
-    tibble::column_to_rownames("name") %>%
+    # tibble::column_to_rownames("name") %>%
     dplyr::select(dplyr::ends_with("diff")) %>%
     signif(., digits = 3) %>%
     tibble::rownames_to_column()
@@ -642,7 +643,7 @@ get_results_proteins <- function(dep) {
 
   # Select the adjusted p-values and significance columns
   pval <- as.data.frame(row_data) %>%
-    tibble::column_to_rownames("name") %>%
+    # tibble::column_to_rownames("name") %>%
     dplyr::select(dplyr::ends_with("p.val"),
                   dplyr::ends_with("p.adj"),
                   dplyr::ends_with("significant")) %>%
@@ -665,7 +666,11 @@ get_results_proteins <- function(dep) {
   table<-table %>% dplyr::arrange(desc(significant))
   colnames(table)[1]<-c("Gene Name")
   colnames(table)[2]<-c("Protein IDs")
+<<<<<<< HEAD
+  table <- table %>% dplyr::relocate(Protein.names, .after = last_col())
+=======
   table <- table %>% dplyr::select(grep("[^Protein.names]",colnames(table)), "Protein.names")
+>>>>>>> 6bb35366647de1666a48c9cef7053d813e3e756b
   # table$Gene_name<-table$name
   return(table)
 }

R/volcano_function.R

@@ -200,7 +200,7 @@ plot_protein<-function(dep, protein, type){
     rownames_to_column() %>%
     gather(ID, val, -rowname) %>%
     left_join(., data.frame(colData(subset)), by = "ID")
-  df_reps$rowname <- parse_factor(df_reps$rowname, levels = protein)
+  df_reps$rowname <- parse_factor(as.character(df_reps$rowname), levels = protein)
 
   df_CI<- df_reps %>%
     group_by(condition, rowname) %>%
@@ -211,7 +211,7 @@ plot_protein<-function(dep, protein, type){
            CI.L = mean - error,
            CI.R = mean + error) %>%
     as.data.frame()
-  df_CI$rowname <- parse_factor(df_CI$rowname, levels = protein)
+  df_CI$rowname <- parse_factor(as.character(df_CI$rowname), levels = protein)
 
   if(type=="violin"){
     p<-ggplot(df_reps, aes(condition, val))+

global.R

@@ -13,6 +13,7 @@ library("ggrepel")
 library("httr")
 library("rjson")
 library("svglite")
+library("shinycssloaders")  # new added
 source("R/functions.R")
 source("R/volcano_function.R")
 source("R/tests.R")