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Dockerfile

@@ -1,14 +1,67 @@
-FROM rocker/r-base:latest
-
-WORKDIR /code
-
-RUN install2.r --error \
-    shiny \
-    dplyr \
-    ggplot2 \
-    readr \
-    ggExtra
-    
-COPY . .
-
-CMD ["R", "--quiet", "-e", "shiny::runApp(host='0.0.0.0', port=7860)"]
+FROM bioconductor/bioconductor_docker:RELEASE_3_19
+
+# Update apt-get and install necessary libraries
+RUN apt-get update \
+    && apt-get install -y \
+        libcurl4-openssl-dev \
+        libxml2-dev \
+        openjdk-11-jdk \
+        libcairo2-dev \
+        libxt-dev \
+        nginx \
+    && apt-get clean \
+    && rm -rf /var/lib/apt/lists/*
+
+# Install R packages
+RUN R -e "install.packages(c('shinyjqui','BiocManager','bslib','rstatix','ggpubr','remotes', 'devtools', 'shiny','shinyscreenshot', 'readr', 'shinyAce', 'ggplot2', 'dplyr', 'shinythemes', 'openintro', 'plotly', 'DT', 'ggprism', 'ggbeeswarm', 'shinyFiles', 'stringr', 'shinyBS', 'shinydashboard', 'shinyWidgets', 'tidyr', 'pheatmap', 'grid', 'gridExtra', 'ggeasy', 'ggtext', 'colorspace', 'shinyjqui', 'tidyverse', 'gprofiler2', 'colourpicker', 'shinydashboardPlus', 'jsonlite', 'reshape2','shinyalert'))"
+
+RUN R -e 'BiocManager::install("graph")'
+RUN R -e "source('https://install-github.me/dreamRs/esquisse')"
+
+# Install plumber and cyjshiny
+RUN R -e "install.packages(c('plumber','cyjShiny','svglite'))"
+
+RUN mkdir -p /srv/app/modules \
+    && mkdir -p /srv/app/pathwaymap \
+    && mkdir -p /srv/app/svg \
+    && mkdir -p /srv/app/www \
+    && mkdir -p /srv/app/data
+
+RUN rm /etc/nginx/sites-enabled/default \
+    && mkdir -p /var/cache/nginx \
+    && mkdir -p /var/log/nginx \
+    && mkdir -p /var/lib/nginx \
+    && mkdir -p /var/lib/nginx/body \
+    #&& chown -R www-data:www-data /var/log/nginx \
+    #&& chown -R www-data:www-data /var/lib/nginx \
+    #&& chown -R www-data:www-data /var/cache/nginx \
+    && chmod -R 777 /var/log/nginx \
+    && chmod -R 777 /var/lib/nginx \
+    && chmod -R 777 /var/cache/nginx \
+    && touch /var/run/nginx.pid \
+    && chmod 777 /var/run/nginx.pid 
+    #&& chown -R www-data:www-data /var/run/nginx.pid
+
+COPY nginx.conf /etc/nginx/nginx.conf
+RUN chmod 666 /etc/nginx/nginx.conf
+
+COPY ./app/ui.R /srv/app/
+COPY ./app/server.R /srv/app/
+COPY ./app/plumber.R /srv/app/
+COPY ./app/R /srv/app/modules/
+COPY ./app/data /srv/app/pathwaymap/
+COPY ./app/svg /srv/app/svg/
+COPY ./app/www /srv/app/www/
+COPY ./scripts/start.sh /usr/local/bin/start.sh
+
+
+RUN chmod +x /usr/local/bin/start.sh
+
+RUN chmod -R 777 /srv/app \
+    && chmod -R 777 /srv/app/data
+
+EXPOSE 7860
+EXPOSE 8000
+EXPOSE 9000
+
+CMD ["sh", "/usr/local/bin/start.sh"]

LICENCE

@@ -0,0 +1,7 @@
+Copyright (c) 2024, Takaki OKA
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

README.md

@@ -1,10 +1,11 @@
----
-title: Test
-emoji: 📚
-colorFrom: blue
-colorTo: yellow
-sdk: docker
-pinned: false
----
-
+---
+title: msdial2cytoscape-for-lipidomics
+emoji: 📚
+colorFrom: blue
+colorTo: yellow
+sdk: docker
+pinned: false
+license: mit
+---
+
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app/R/data_processing.R

@@ -0,0 +1,350 @@
+##' Checking duplicated metabolites from alignment table
+##'
+##' @title Checking duplicated metabolites from alignment table
+##' @param data Lipidomics alignment file form MS-DIAL analysis
+##' @return The alignment table with duplicated metabolites removed.
+##' @examples
+##' data <- read.csv("D:/100cells/20240222/lipid_metabolome.csv",header = F)
+##' result <- Metabolite_duplicate_check(data)
+##' @references 
+##' @author Takaki Oka
+##' @export
+Metabolite_duplicate_check <- function(data){
+  # convert rownames to column for row index
+  
+  ontology_column <- 12
+  batch_id_row <- 2
+  peakinfo_column <- 1:35
+  header_row <- 5
+  peaktable <- data[, !data[batch_id_row, ] %in% NA, drop = FALSE]
+  originalcolnum <- colnames(peaktable)
+  peaktable <- peaktable %>%
+    setNames(peaktable[5,]) %>%
+    rownames_to_column()
+  
+  # Find rows with max 'Fill %' for each 'Metabolite name'
+  check_duplicates <- peaktable %>%
+    group_by(`Metabolite name`) %>%
+	    slice_max(`Fill %`,n=1) %>%
+        ungroup()
+  
+  id <- check_duplicates$rowname
+  datav2 <- peaktable %>%
+    filter(rowname %in% c(id, 1:5)) %>%
+    dplyr::select(-rowname)
+  colnames(datav2) <- originalcolnum
+  return(datav2)
+}
+
+##' Converting alignment file
+##'
+##' @title Converting alignment file to dataframe
+##' @param data alignment file form MS-DIAL analysis
+##' @return dataframe of alignment file
+##' @examples
+##' process_alignment_file(data)
+##' @references 
+##' @author Takaki OKA
+##' @export
+process_alignment_file <- function(data) {
+  ontology_column <- 12
+  batch_id_row <- 2
+  peakinfo_column <- 1:35
+  header_row <- 5
+  data <- Metabolite_duplicate_check(data)
+  #data <- Median_normalize_to_alignment_format(data)
+  peaktable <- data[, !data[batch_id_row, ] %in% NA, drop = FALSE]
+  peaktable <- peaktable[, peaktable[batch_id_row, ] == "Sample", drop = FALSE]
+  sample_info <- data.frame(name = unlist(peaktable[5, ]),
+                            Class = unlist(peaktable[1, ]))
+  
+  peaktable <- cbind(data[, peakinfo_column], peaktable)
+  colnames(peaktable) <- peaktable[header_row, ]
+  peaktable <- peaktable[-c(1:header_row), ] 
+  
+  peaktable[, -peakinfo_column] <- data.frame(lapply(peaktable[, -peakinfo_column], as.numeric)) 
+  peaktable <-  distinct(peaktable,`Metabolite name`,.keep_all = TRUE)
+  lipid_info <- peaktable[, colnames(peaktable) %in% c("Metabolite name","Ontology",sample_info$name)]
+  peak_info <- peaktable[, !colnames(peaktable) %in% c("Metabolite name", sample_info$name)]
+  
+  return(list(lipid_info,sample_info,peak_info))
+}
+
+##' Median normalizeing of alignment table wuth retaining alignment format
+##'
+##' @title Median normalizeing of alignment table
+##' @param data Lipidomics alignment file form MS-DIAL analysis
+##' @return Median normalized data with alignment format
+##' @examples
+##' data <- read.csv("D:/100cells/20240222/lipid_metabolome.csv",header = F)
+##' result <- Median_normalize(data)
+##' @references 
+##' @author Nami Sakamoto, Takaki Oka
+##' @export
+Median_normalize_to_alignment_format <- function(data){
+  
+  # Extract lipidontinf
+  peakinfo <- data[5:nrow(data), c(1:35)]
+  lipidontinf <- data[5:nrow(data), c(4, 12)]
+  colnames(lipidontinf) <- lipidontinf[1,]
+  lipidontinf <- lipidontinf[-1,]
+  rownames(lipidontinf) <- NULL
+  
+  # Extract sample information
+  sampleinf <- t(data[1:4, 35:ncol(data)])
+  sampledata <- t(data[5:nrow(data), 36:ncol(data)])
+  Metabolitename <- t(data[-c(1:4), 4])
+  
+  # Combine sample information
+  colnames(sampledata) <- colnames(Metabolitename)
+  datav2_v2 <- cbind(data.frame(sampleinf, rbind(data.frame(Metabolitename), data.frame(sampledata))))
+  colnames(datav2_v2) <- datav2_v2[1,]
+  datav2_v2 <- datav2_v2[-1,]
+  rownames(datav2_v2) <- NULL
+  names(datav2_v2)[5] <- "name"
+  
+  # Filter out 'Batch ID' values and process numeric data
+  lipidmetabolomedata_all <- datav2_v2 %>%
+    filter(!`Batch ID` %in% c('Average', 'Stdev'))
+  numericdata <- lipidmetabolomedata_all[, -c(1:4)]
+  rownames(numericdata) <- NULL
+  numericdatax <- as.data.frame(sapply(numericdata[, -1], as.numeric))
+  rownames(numericdatax) <- lipidmetabolomedata_all$name
+  numericdatax2 <- numericdatax %>%
+    tibble::rownames_to_column() %>%
+    dplyr::rename(sampleid = rowname) %>%
+    pivot_longer(!sampleid, names_to = "lipidname", values_to = "value")
+  
+  log2value <- numericdatax2 %>%
+    mutate(log2value = log2(value)) %>%
+    dplyr::select(-value) %>%
+    pivot_wider(names_from = "lipidname", values_from = "log2value") %>%
+    column_to_rownames(var = "sampleid")
+  
+  samplemedian <- log2value %>% apply(1,median) %>% as.data.frame()
+  colnames(samplemedian) <- "median"
+  
+  mean_forsamplemedian <- mean(samplemedian$median)
+  
+  log2mediannormalizedata <- rownames_to_column(log2value) %>%
+    dplyr::rename(sampleid = rowname) %>%
+    left_join(rownames_to_column(samplemedian), by = c("sampleid" = "rowname")) %>%
+    pivot_longer(!c(sampleid, median), names_to = "lipidname", values_to = "log2value") %>%
+    mutate(mediannormvalue = log2value - median + mean_forsamplemedian) %>%
+    dplyr::select(-c(log2value, median)) %>%
+    pivot_wider(names_from = "lipidname", values_from = "mediannormvalue") %>%
+    column_to_rownames(var = "sampleid")
+  
+  log2_2xmediannormalizedata <- 2^log2mediannormalizedata
+  log2_2xmediannormalizedata_0 <- lipidmetabolomedata_all[, c(1:5)] %>%
+    left_join(rownames_to_column(log2_2xmediannormalizedata), by = c("name" = "rowname")) %>% t() %>% data.frame()%>% rownames_to_column("V0")
+  aligntable <- right_join(peakinfo,log2_2xmediannormalizedata_0,by = c("V4" = "V0"))
+  headerrow <- tail(aligntable,n = 5)
+  aligntable <- rbind(headerrow,aligntable)
+  aligntable[1:5,1:35] <- data[1:5,1:35]
+  aligntable <- filter(aligntable,is.na(V1)==F)
+  return(aligntable)
+}
+##' Converting alignment file to expression data of lipid class
+##'
+##' @title Converting alignment file to dataframe
+##' @param data alignment file form MS-DIAL analysis
+##' @return Expression data of lipid class and sample meta data 
+##' @examples
+##' convert_msdial_export_to_lipid_class_dataframe(data)
+##' @references 
+##' @author Takaki OKA
+##' @export
+convert_msdial_export_to_lipid_class_dataframe <- function(data) {
+  data_frame <- process_alignment_file(data)
+  lipid_info <- data_frame[[1]]
+  sample_info <- data_frame[[2]]
+  
+  lipidtable <- lipid_info[,colnames(lipid_info) %in% c("Metabolite name","Ontology", sample_info$name)] %>% pivot_longer(cols = -(1:2)) 
+  lipidtable <- lipidtable %>%
+    group_by(name, Ontology) %>%
+    mutate(mean = mean(value)) %>%
+    ungroup() %>%
+    distinct(name, Ontology, .keep_all = TRUE) %>%
+    select(-c("Metabolite name", value)) %>%
+    pivot_wider(values_from = mean, names_from = Ontology)
+  return(list(lipidtable,sample_info))
+}
+
+##' Converting alignment file to expression data of lipid molecules
+##'
+##' @title Converting alignment file to dataframe
+##' @param data alignment file form MS-DIAL analysis
+##' @return Expression data of lipid molecules and sample meta data 
+##' @examples
+##' convert_msdial_export_to_lipid_molecules_dataframe(data)
+##' @references 
+##' @author Takaki OKA
+##' @export
+convert_msdial_export_to_lipid_molecules_dataframe <- function(data) {
+  data_frame <- process_alignment_file(data)
+  lipid_info <- data_frame[[1]]
+  sample_info <- data_frame[[2]]
+  
+  lipidtable <- lipid_info[,colnames(lipid_info) %in% c("Metabolite name", sample_info$name)] %>% pivot_longer(cols = -(1))  %>%  pivot_wider(names_from = `Metabolite name`,values_from = value) 
+  return(list(lipidtable,sample_info))
+}
+
+##' Updating select input with file upload
+##'
+##' @title Updating select input with file upload
+##' @param data alignment file form MS-DIAL analysis
+##' @return Expression data of lipid molecules and sample meta data 
+##' @examples
+##' convert_msdial_export_to_lipid_molecules_dataframe(data)
+##' @references 
+##' @author Takaki OKA
+##' @export
+processAndUpdateInputs <- function(data, session, metadata, metainfocol) {
+  shiny::updateSelectInput(session, "y", selected = paste(colnames(data)[c(metainfocol + 1)]), choices = colnames(data)[-c(1:metainfocol)])
+  shiny::updateSelectInput(session, "w", selected = "Class", choices = colnames(data)[c(2:metainfocol)])
+  shiny::updateSelectInput(session, "z", selected = "Class", choices = colnames(data)[c(2:metainfocol)])
+}
+
+processAndUpdateInputs2 <- function(data, session, metadata, metainfocol) {
+  #shiny::updateSelectInput(session, "y", selected = paste(colnames(data)[c(metainfocol + 1)]), choices = colnames(data)[-c(1:metainfocol)])
+  shiny::updateSelectInput(session, "X1", selected = paste(colnames(data)[c(metainfocol + 1)]), choices = colnames(data)[-c(1:metainfocol)])
+  shiny::updateSelectInput(session, "X2", selected = paste(colnames(data)[c(metainfocol + 1)]), choices = colnames(data)[-c(1:metainfocol)])
+  #shiny::updateSelectInput(session, "z", selected = "Class", choices = colnames(data)[c(2:metainfocol)])
+}
+# update_select_input <- function(input,session) {
+# 
+#     
+#   
+# }
+
+scale_rows <- function(x) {
+  t(apply(x, 1, function(row) scales::rescale(row, to = c(-2, 2))))
+}
+
+lipidmeancalforgroupnode <- function(data, metadata, selectclass) {
+  Ontology_column <- 12
+  BatchID_row <- 2
+  peakinfo_column <- 1:35
+  header_row <- 5
+  processed_data <- process_alignment_file(data)
+  lipid_data_classmean <- processed_data[[1]]
+  sampleinfo <- processed_data[[2]]
+  lipid_data_lipidclassmean <- pivot_longer(lipid_data_classmean, cols = -c(1:2)) %>%
+    select(`Metabolite name`, Ontology, name, value) %>%
+    inner_join(metadata, by = "name") %>%
+    select(`Metabolite name`,Ontology ,selectclass, name, value) %>%
+    group_by(`Metabolite name`, across(all_of(selectclass))) %>%
+    mutate(mean = mean(value)) %>%
+    ungroup() %>%
+    distinct(`Metabolite name`,across(all_of(selectclass)), .keep_all = TRUE) %>%
+    select(`Metabolite name`, Ontology, selectclass, mean) %>%
+    pivot_wider(names_from = selectclass, values_from = mean)
+  return(lipid_data_lipidclassmean)
+}
+
+processSampleInRows <- function(originaldata, session, input) {
+  colnames(originaldata) <- originaldata[1,]
+  originaldata <- originaldata[-1,]
+  originaldata[, -c(1:2)] <- apply(originaldata[, -c(1:2)], 2, as.numeric)
+  lipidont <- read.csv(input$ontfile$datapath, check.names = FALSE)
+  colnames(lipidont)[1] <- "lipid"
+  colnames(originaldata)[1] <- "name"
+  if (length(input$file2) != 0) {
+    metadata <- read.csv(input$file2$datapath)
+    colnames(metadata)[1] <- "name"
+    data <- originaldata %>%
+      pivot_longer(cols = -c(1:2), names_to = "lipid") %>%
+      inner_join(lipidont, by = c("lipid")) %>%
+      group_by(name, Ontology) %>%
+      mutate(mean = mean(value)) %>%
+      ungroup() %>%
+      distinct(name, Ontology, .keep_all = TRUE) %>%
+      select(1, 2, 5, 6) %>%
+      pivot_wider(names_from = "Ontology", values_from = "mean")
+    data <- inner_join(metadata, data, by = c("name" = "name"))
+  } else {
+    metadata <- data.frame(name = originaldata[,1],Class = originaldata[,2])
+    data <- originaldata %>%
+      pivot_longer(cols = -c(1:2), names_to = "lipid") %>%
+      inner_join(lipidont, by = c("lipid")) %>%
+      group_by(name, Ontology) %>%
+      mutate(mean = mean(value)) %>%
+      ungroup() %>%
+      distinct(name, Ontology, .keep_all = TRUE) %>%
+      select(1, 5, 6) %>%
+      pivot_wider(names_from = "Ontology", values_from = "mean")
+    data <- inner_join(metadata, data, by = c("name" = "name"))
+  }
+  return(list(data,metadata))
+}
+
+processMSDIALExport <- function(originaldata, session, input) {
+  if (length(input$file2) != 0) {
+  tablelist <- originaldata %>% convert_msdial_export_to_lipid_class_dataframe()
+  data <- tablelist[[1]]
+  metadata <- read.csv(input$file2$datapath)
+  colnames(metadata)[1] <- "name"
+  metadata <- inner_join(tablelist[[2]], metadata, by = c("name"))
+  data <- inner_join(metadata, data, by = c("name" = "name"))
+  metainfocol <- ncol(metadata)
+  print(data)
+  } else {
+  tablelist <- originaldata %>% convert_msdial_export_to_lipid_class_dataframe()
+  data <- tablelist[[1]]
+  metadata <- tablelist[[2]]
+  metainfocol <- ncol(metadata)
+  data <- inner_join(metadata, data, by = c("name" = "name"))
+  }
+  return(list(data,metadata))
+}
+
+processMSDIALExporttomoldata <- function(originaldata, session, input) {
+  if (length(input$file2) != 0) {
+  tablelist <- originaldata %>% convert_msdial_export_to_lipid_molecules_dataframe()
+  data <- tablelist[[1]]
+  metadata <- read.csv(input$file2$datapath)
+  colnames(metadata)[1] <- "name"
+  metadata <- inner_join(tablelist[[2]], metadata, by = c("name"))
+  data <- inner_join(metadata, data, by = c("name" = "name"))
+  metainfocol <- ncol(metadata)
+  } else {
+  tablelist <- originaldata %>% convert_msdial_export_to_lipid_molecules_dataframe()
+  data <- tablelist[[1]]
+  metadata <- tablelist[[2]]
+  metainfocol <- ncol(metadata)
+  data <- inner_join(metadata, data, by = c("name" = "name"))
+  }
+  return(list(data,metadata))
+}
+
+processSampleInRowstomoldata <- function(originaldata, session, input) {
+  moldata <- originaldata 
+  colnames(moldata) <- moldata[1,]
+  moldata <- moldata[, !duplicated(colnames(moldata))]
+  moldata <- moldata[-1,]
+  moldata[,-c(1,2)] <- apply(moldata[,-c(1,2)],2,as.numeric) %>% data.frame()
+  colnames(moldata)[1] <- "name"
+  if (length(input$file2) != 0) {
+    metadata <- read.csv(input$file2$datapath)
+    colnames(metadata)[1] <- "name"
+    data <- inner_join(metadata, moldata, by = c("name" = "name"))
+  } else {
+    metadata <- data.frame(name = originaldata[,1],Class = originaldata[,2])
+    data <- moldata
+  }
+  return(list(data,metadata))
+}
+
+read_graph_json <- function(file_path) {
+  tryCatch({
+    paste(readLines(file_path), collapse = "")
+  }, error = function(e) {
+    message("Error reading graph JSON file: ", e$message)
+    return(NULL)
+  })
+}
+
+
+pvaluecheckbox =reactiveVal()
+pvaluecheckbox <<- ""

app/R/heatmap_utils.R

@@ -0,0 +1,568 @@
+groupnodeheatmap <- function(lipidclassproperties,data,metadata,class,levels,inputclass,input){
+  # lipidclassproperties
+  if(input$filetype == "MS-DIAL export"){
+  lipid_data_classmean <<- right_join(lipidclassproperties,lipidmeancalforgroupnode(data,metadata,input$w) ,by = c("LbmClass"="Ontology"))
+  }
+  
+  if(input$filetype == "Sample in rows"){
+    colnames(data) <- data[1,]
+    data <- data[-1,]
+    data[, -c(1:2)] <- apply(data[, -c(1:2)], 2, as.numeric)
+	colnames(data)[1] <- "name"
+	colnames(metadata)[1] <- "name"
+    lipid <- data %>% pivot_longer(cols = -c(1:2),names_to = "Metabolite name") %>%  
+      inner_join(metadata,by = c("name","Class")) %>% 
+      select(c(paste(input$w,"",sep=""),`Metabolite name`,name,value)) %>%
+      group_by_at(c("Metabolite name",input$w)) %>%
+      mutate(mean = mean(value)) %>%
+      ungroup() %>%
+      distinct(`Metabolite name`,!!as.symbol(input$w),.keep_all = T) %>%
+      select(c(1,2,5)) %>%
+      pivot_wider(names_from = paste(input$w,"",sep=""),values_from = mean)
+    lipidont <- read.csv(input$ontfile$datapath,check.names = F)
+    lipid <- inner_join(lipidont,lipid,by =c("lipid" = "Metabolite name"))
+    lipid <- rename(lipid,`Metabolite name`  = lipid)
+    lipid_data_classmean <- right_join(lipidclassproperties,lipid,by = c("LbmClass"="Ontology")) %>% filter(TotalChain != 0)# %>% filter(`Annotation tag (VS1.0)` == "410")
+  }
+  
+  #lipid_data_classmean <- right_join(lipidclassproperties,lipidmeancalforgroupnode(data,metadata,paste(class,"",sep="")) ,by = c("LbmClass"="Ontology")) # %>% filter(`Annotation tag (VS1.0)` == "410")
+  lipid_data_classmean <- filter(lipid_data_classmean,LbmClass == inputclass)
+  if(unique(lipid_data_classmean$TotalChain) == "1"){
+    targetmonoacylchain <- c("16:0","16:1","18:0","18:1","18:2","18:3","20:3","20:4","20:5","22:4","22:5","22:6")
+    monoacyl <- lipid_data_classmean %>% filter(TotalChain == "1") %>% filter(!LbmClass %in% c("AHexCS","AHexSTS"))
+    targetlipidclass <- monoacyl$LbmClass %>% unique()
+    monoacyl <- monoacyl[,-c(2:7)]
+    if(input$acylfilter == T){
+      
+      acyllist <- list()
+      for (i in 1:nrow(monoacyl)) {
+        a <- unlist(str_split(monoacyl$`Metabolite name`[i],pattern = " "))[2]
+        
+        if (str_detect(a,pattern = ";") == TRUE){
+          acyl <- a %>% str_split(pattern = ";") %>% unlist()
+          if(str_detect(acyl[1],pattern = "-")){
+            acyl <- acyl[1] %>% str_split(pattern = "-") %>% unlist()
+            acyllist[i] <- a[2]
+          }else{
+            acyllist[i] <- acyl[1]
+          }
+          
+        }else if(str_detect(a,pattern = "/")==TRUE){
+          acyl <- a %>% str_split(pattern = "/") %>% unlist()
+          acyllist[i] <- acyl[2]
+        }else{
+          if(str_detect(a,pattern = "-")){
+            acyl <- a %>% str_split(pattern = "-") %>% unlist()
+            acyllist[i] <- acyl[2]
+          }else{
+            acyllist[i] <- a
+          }
+        }
+      }
+      
+      
+      
+      monoacyl$acyl <- unlist(acyllist)
+      #
+      targetmonoacyllipid <- monoacyl %>% filter(acyl %in% targetmonoacylchain) %>% select(-c(acyl))
+    }
+    else{
+      targetmonoacyllipid <- monoacyl
+    }
+    targetlipidclass <- unique(targetmonoacyllipid$LbmClass)
+    monoacyl <- targetmonoacyllipid
+    
+    Lipidclassdata<- filter(monoacyl , LbmClass == inputclass) %>% distinct(`Metabolite name`,.keep_all = TRUE)
+    if(length(Lipidclassdata$LbmClass) != 0){
+      d <- Lipidclassdata
+      rowname <- as.matrix((d$`Metabolite name`))
+      d <- select(d,-c(LbmClass))
+      n <- nrow(d)
+      BreaksList <- seq(-2,2, by = 0.5)
+      height <- n*0.9
+      list <- c(-2,0,2)
+      lipid <- d
+      lipid <- lipid[,-1]
+      rownames(lipid) <- rowname
+      lipid <- scale_rows(lipid) %>% as.data.frame()
+      lipid <- tibble::rownames_to_column(lipid, "Metabolite name")
+      lipid <- pivot_longer(lipid,cols = -c(`Metabolite name`))
+      lipid <- mutate(lipid, name = name %>% factor(levels = input$levels))
+      
+      ghm <- ggplot(lipid, aes(x = name, y =`Metabolite name` , fill = value))
+      ghm <- ghm + geom_tile(color = "black") + scale_fill_gradientn(
+        colors=c("navy","white","red"),limits=c(min(lipid$value), max(lipid$value))
+      ) + theme(axis.text.x = element_text(angle = 90, hjust = 0),axis.text = element_text(size = input$Fontsize))
+      print(ghm)
+    }}
+  if(unique(lipid_data_classmean$TotalChain) == "2"&& unique(lipid_data_classmean$Categories) != "Sphingolipids" && unique(lipid_data_classmean$Categories) != "Sterol Lipids"){
+    targetdiacylchain <- c("16:0","16:1","18:0","18:1","18:2","18:3","20:3","20:4","20:5","22:4","22:5","22:6")
+    diacyl <- lipid_data_classmean %>% filter(TotalChain == "2", Categories != "Sphingolipids")
+    targetlipidclass <- diacyl$LbmClass %>% unique()
+    diacyl <- diacyl[,-c(3:7)]
+    if(input$acylfilter == T){
+      acyl1list <- list()
+      acyl2list <- list()
+      for (i in 1:nrow(diacyl) ) {
+        if(str_detect(diacyl$`Metabolite name`[i],pattern = "\\|") == TRUE){
+          a <-  unlist(str_split(diacyl$`Metabolite name`[i],pattern = "\\|"))[2] %>% str_split(pattern = " ") %>% unlist()
+          if(str_detect(a[2],pattern = "_") == TRUE){
+            a <- a[2] %>% str_split(pattern = "_") %>% unlist()
+            if(str_detect(a[1],pattern = "-") == TRUE){
+              acyl1 <- a[1] %>% str_split(pattern = "-") %>% unlist()
+              acyl1list[i] <- acyl1[2]
+            }else{
+              acyl1list[i] <- a[1]
+            }
+            if(str_detect(a[2],pattern = ";") == TRUE){
+              acyl2 <- a[2] %>% str_split(pattern = ";") %>% unlist()
+              acyl2list[i] <- acyl2[1]
+            }else{
+              acyl2list[i] <- a[2]
+            }
+          }else{
+            a <- unlist(a)[2] %>% str_split(pattern = "\\)") %>% unlist()
+            acyl1 <- a[1] %>%  str_split(pattern = " ") %>% unlist()
+            acyl1list[i] <- acyl1[2]
+            acyl2list[i] <- a[2]
+          }
+        }
+        else{
+          acyl1list[i] <- NA
+          acyl2list[i] <- NA
+        }
+      }
+      
+      diacyl$acyl1 <- unlist(acyl1list)
+      diacyl$acyl2 <- unlist(acyl2list)
+      
+      targetdiacyllipid <- diacyl %>% filter(acyl1 %in% targetdiacylchain, acyl2 %in% targetdiacylchain)
+      
+      # if(input$sn == T){
+      #   targetdiacyllipid$acyl1 <- paste(targetdiacyllipid$LbmClass," ",targetdiacyllipid$acyl1," (sn-1)",sep ="")
+      #   targetdiacyllipid$acyl2 <- paste(targetdiacyllipid$LbmClass," ",targetdiacyllipid$acyl2," (sn-2)",sep ="")
+      #   targetdiacyllipid <- targetdiacyllipid %>% select( `Metabolite name`,LbmClass,acyl1,acyl2,everything()) %>% pivot_longer(cols = -c(1:4)) %>% group_by(name,acyl1) %>% mutate(mean1 = mean(value)) %>% ungroup() %>% group_by(name,acyl2) %>% mutate(mean2 = mean(value)) %>% ungroup()
+      #   sn1 <- select(targetdiacyllipid,c(LbmClass,name,acyl1,mean1)) %>% distinct(name,acyl1,.keep_all = T)
+      #   colnames(sn1) <- c("LbmClass","name","Metabolite name","value")
+      #   sn2 <- select(targetdiacyllipid,c(LbmClass,name,acyl2,mean2))%>% distinct(name,acyl2,.keep_all = T)
+      #   colnames(sn2) <- c("LbmClass","name","Metabolite name","value")
+      #   diacyl <- rbind(sn1,sn2) %>% pivot_wider(names_from = "name",values_from = "value") #%>% data.frame()
+      # }
+      
+      
+      diacyl <- targetdiacyllipid %>% select(-c(acyl1,acyl2))
+      
+    }
+    else{}
+    Lipidclassdata<- filter(diacyl , LbmClass == inputclass) %>% distinct(`Metabolite name`,.keep_all = TRUE)
+    if(length(Lipidclassdata$LbmClass) != 0){
+      d <- Lipidclassdata
+      rowname <- as.matrix((d$`Metabolite name`))
+      d <- select(d,-c(`Metabolite name`,LbmClass))
+      BreaksList <- seq(-2,2, by = 0.5)
+      # print(d)
+      n <- nrow(d)
+      height <- n/10*3
+      list <- c(-2,0,2)
+      lipid <- d
+      lipid <- lipid[,-1]
+      rownames(lipid) <- rowname
+      lipid <- scale_rows(lipid) %>% as.data.frame()
+      lipid <- tibble::rownames_to_column(lipid, "Metabolite name")
+      lipid <- pivot_longer(lipid,cols = -c(`Metabolite name`))
+      
+      lipid <- mutate(lipid, name = name %>% factor(levels = input$levels))
+      ghm <- ggplot(lipid, aes(x = name, y =`Metabolite name` , fill = value))
+      ghm <- ghm + geom_tile(color = "black") + scale_fill_gradientn(
+        colors=c("navy","white","red"),limits=c(min(lipid$value), max(lipid$value))
+      ) + theme(axis.text.x = element_text(angle = 90, hjust = 1),axis.text = element_text(size = input$Fontsize))
+      print(ghm)
+    }}
+  
+  if(unique(lipid_data_classmean$TotalChain) == "2"&& unique(lipid_data_classmean$Categories) == "Sterol Lipids" && unique(lipid_data_classmean$LbmClass) != "WE"){
+    targetdiacylchain <- c("16:0","16:1","18:0","18:1","18:2","18:3","20:3","20:4","20:5","22:4","22:5","22:6")
+    diacyl <- lipid_data_classmean %>% filter(TotalChain == "2", LbmClass != "WE")# %>%
+    targetlipidclass <- diacyl$LbmClass %>% unique()
+    diacyl <- diacyl[,-c(3:7)]
+    if(input$acylfilter == T){
+      acyl1list <- list()
+      acyl2list <- list()
+      for (i in 1:nrow(diacyl) ) {
+        
+        if(str_detect(diacyl$`Metabolite name`[i],pattern = "\\|") == TRUE){
+          a <-  unlist(str_split(diacyl$`Metabolite name`[i],pattern = "\\|"))[2] %>% str_split(pattern = " ") %>% unlist()
+          if(str_detect(a[2],pattern = "/") == TRUE){
+            a <- a[2] %>% str_split(pattern = "/") %>% unlist()
+            acyl1list[i] <- a[1]
+            acyl2list[i] <- a[2]
+          }
+        }
+        else{
+          acyl1list[i] <- NA
+          acyl2list[i] <- NA
+        }
+      }
+      diacyl$acyl1 <- unlist(acyl1list)
+      diacyl$acyl2 <- unlist(acyl2list)
+      
+      targetdiacyllipid <- diacyl %>% filter(acyl1 %in% targetdiacylchain, acyl2 %in% targetdiacylchain)
+      
+      # if(input$sn == T){
+      #   targetdiacyllipid$acyl1 <- paste(targetdiacyllipid$LbmClass," ",targetdiacyllipid$acyl1," (sn-1)",sep ="")
+      #   targetdiacyllipid$acyl2 <- paste(targetdiacyllipid$LbmClass," ",targetdiacyllipid$acyl2," (sn-2)",sep ="")
+      #   targetdiacyllipid <- targetdiacyllipid %>% select( `Metabolite name`,LbmClass,acyl1,acyl2,everything()) %>% pivot_longer(cols = -c(1:4)) %>% group_by(name,acyl1) %>% mutate(mean1 = mean(value)) %>% ungroup() %>% group_by(name,acyl2) %>% mutate(mean2 = mean(value)) %>% ungroup()
+      #   sn1 <- select(targetdiacyllipid,c(LbmClass,name,acyl1,mean1)) %>% distinct(name,acyl1,.keep_all = T)
+      #   colnames(sn1) <- c("LbmClass","name","Metabolite name","value")
+      #   sn2 <- select(targetdiacyllipid,c(LbmClass,name,acyl2,mean2))%>% distinct(name,acyl2,.keep_all = T)
+      #   colnames(sn2) <- c("LbmClass","name","Metabolite name","value")
+      #   diacyl <- rbind(sn1,sn2) %>% pivot_wider(names_from = "name",values_from = "value") #%>% data.frame()
+      # }
+      
+      
+      diacyl <- targetdiacyllipid %>% select(-c(acyl1,acyl2))
+      
+    }
+    else{}
+    
+    
+    Lipidclassdata<- filter(diacyl , LbmClass == inputclass) %>% distinct(`Metabolite name`,.keep_all = TRUE)
+    if(length(Lipidclassdata$LbmClass) != 0){
+      d <- Lipidclassdata
+      rowname <- as.matrix((d$`Metabolite name`))
+      d <- select(d,-c(`Metabolite name`,LbmClass))
+      
+      BreaksList <- seq(-2,2, by = 0.5)
+      n <- nrow(d)
+      height <- n/10*3
+      list <- c(-2,0,2)
+      lipid <- d
+      lipid <- lipid[,-1]
+      rownames(lipid) <- rowname
+      lipid <- scale_rows(lipid) %>% as.data.frame()
+      lipid <- tibble::rownames_to_column(lipid, "Metabolite name")
+      lipid <- pivot_longer(lipid,cols = -c(`Metabolite name`))
+      lipid <- mutate(lipid, name = name %>% factor(levels = input$levels))
+      ghm <- ggplot(lipid, aes(x = name, y =`Metabolite name` , fill = value))
+      ghm <- ghm + geom_tile(color = "black") + scale_fill_gradientn(
+        colors=c("navy","white","red"),limits=c(min(lipid$value), max(lipid$value))
+      ) + theme(axis.text.x = element_text(angle = 90, hjust = 1),axis.text = element_text(size = input$Fontsize))
+      print(ghm)
+    }}
+  
+  if(unique(lipid_data_classmean$TotalChain) == "2"&&unique(lipid_data_classmean$Categories) == "Sphingolipids"&&unique(lipid_data_classmean$LbmClass) != "ASM"){
+    targetSphingoidBase <- c("18:0;O2","18:1;O2","18:2;O2","18:0;O3","18:0;2O","18:1;2O","18:2;2O","18:0;3O")
+    targetnacylchain <- c("16:0","16:1","18:0", "20:0", "22:0", "24:0", "24:1", "26:0", "26:1")
+    epidermalacylceramide <- c("Cer_EODS","Cer_EOS","Cer_EBDS","HexCer_EOS")
+    epidermalacylceramidetargetacylchain <- c("18:2")
+    diacyl <- lipid_data_classmean %>% filter(TotalChain == "2",Categories == "Sphingolipids",LbmClass != "ASM")
+    targetlipidclass <- diacyl$LbmClass %>% unique()
+    diacyl <- diacyl[,-c(3:7)]
+    
+    if(input$acylfilter == T){
+      acyl1list <- list()
+      acyl2list <- list()
+      for (i in 1:nrow(diacyl)) {
+        if(str_detect(diacyl$`Metabolite name`[i],pattern = "\\|") == TRUE){
+          a <-  unlist(str_split(diacyl$`Metabolite name`[i],pattern = "\\|"))[2] %>% str_split(pattern = " ")
+          if(str_detect(unlist(a)[2],pattern = "/") == TRUE){
+            a <- unlist(a)[2] %>% str_split(pattern = "/") %>% unlist()
+            acyl1list[i] <- a[1]
+            if(str_detect(a[2],pattern = ";") == TRUE){
+              
+              acyl2 <- a[2] %>% str_split(pattern = ";") %>% unlist()
+              acyl2list[i] <- acyl2[1]
+            }
+            else if(str_detect(a[2],pattern = "\\(") == TRUE){
+              acyl2 <- a[2] %>% str_split(pattern = "\\(") %>% unlist()
+              acyl2list[i] <- acyl2[1]
+            }
+            else{
+              acyl2list[i] <- a[2]
+            }
+          }
+          else{
+            acyl1list[i] <- NA
+            acyl2list[i] <- NA
+          }
+        }
+        else{
+          acyl1list[i] <- NA
+          acyl2list[i] <- NA
+        }
+      }
+      diacyl$acyl1 <- unlist(acyl1list)
+      diacyl$acyl2 <- unlist(acyl2list)
+      targetdiacyllipidnotEOS <- diacyl %>% filter(!Categories %in% epidermalacylceramide) %>% filter(acyl1 %in% targetSphingoidBase, acyl2 %in% targetnacylchain)
+      targetdiacyllipidEOS  <- diacyl %>% filter(Categories %in% epidermalacylceramide) %>% filter(acyl1 %in% targetSphingoidBase, acyl2 %in% epidermalacylceramidetargetacylchain)
+      targetdiacyllipid <- rbind(targetdiacyllipidEOS,targetdiacyllipidnotEOS)
+      targetlipidclass <- unique(targetdiacyllipid$LbmClass)
+      
+      # if(input$sn == T){
+      #   targetdiacyllipid$acyl1 <- paste(targetdiacyllipid$LbmClass," C",targetdiacyllipid$acyl1,sep ="")
+      #   targetdiacyllipid$acyl2 <- paste(targetdiacyllipid$LbmClass," C",targetdiacyllipid$acyl2,sep ="")
+      #   targetdiacyllipid <- targetdiacyllipid %>% select( `Metabolite name`,LbmClass,acyl1,acyl2,everything()) %>% pivot_longer(cols = -c(1:4)) %>% group_by(name,acyl1) %>% mutate(mean1 = mean(value)) %>% ungroup() %>% group_by(name,acyl2) %>% mutate(mean2 = mean(value)) %>% ungroup()
+      #   sn1 <- select(targetdiacyllipid,c(LbmClass,name,acyl1,mean1)) %>% distinct(name,acyl1,.keep_all = T)
+      #   colnames(sn1) <- c("LbmClass","name","Metabolite name","value")
+      #   sn2 <- select(targetdiacyllipid,c(LbmClass,name,acyl2,mean2))%>% distinct(name,acyl2,.keep_all = T)
+      #   colnames(sn2) <- c("LbmClass","name","Metabolite name","value")
+      #   diacyl <- sn2 %>% pivot_wider(names_from = "name",values_from = "value")
+      # }
+      
+      
+      diacyl <- targetdiacyllipid %>% select(-c(acyl1,acyl2))
+      
+      
+    }
+    else{
+    }
+    
+    
+    Lipidclassdata<- filter(diacyl , LbmClass == inputclass) %>% distinct(`Metabolite name`,.keep_all = TRUE)
+    if(length(Lipidclassdata$LbmClass) != 0){
+      d <- Lipidclassdata
+      rowname <- as.matrix((d$`Metabolite name`))
+      d <- select(d,-c(`Metabolite name`,LbmClass))
+      n <- nrow(d)
+      BreaksList <- seq(-2,2, by = 0.5)
+      n <- nrow(d)
+      height <- n/10*3
+      list <- c(-2,0,2)
+      lipid <- d
+      lipid <- lipid[,-1]
+      rownames(lipid) <- rowname
+      lipid <- scale_rows(lipid) %>% as.data.frame()
+      lipid <- tibble::rownames_to_column(lipid, "Metabolite name")
+      lipid <- pivot_longer(lipid,cols = -c(`Metabolite name`))
+      lipid <- mutate(lipid, name = name %>% factor(levels = input$levels))
+      ghm <- ggplot(lipid, aes(x = name, y =`Metabolite name` , fill = value))
+      ghm <- ghm + geom_tile(color = "black") + scale_fill_gradientn(
+        colors=c("navy","white","red"),limits=c(min(lipid$value), max(lipid$value))
+      ) + theme(axis.text.x = element_text(angle = 90, hjust = 1),axis.text = element_text(size = input$Fontsize))
+      print(ghm)
+      
+    }}
+  
+  if(unique(lipid_data_classmean$LbmClass) == "ASM"){
+    targetnacylchain <- c("16:0","16:1","18:0", "20:0", "22:0", "24:0", "24:1", "26:0", "26:1")
+    diacyl <- lipid_data_classmean %>% filter(LbmClass == "ASM")# %>%
+    targetlipidclass <- diacyl$LbmClass %>% unique()
+    diacyl <- diacyl[,-c(3:7)]
+    
+    if(input$acylfilter == T){
+      
+      acyl1list <- list()
+      for (i in 1:nrow(diacyl)) {
+        if(str_detect(diacyl$`Metabolite name`[i],pattern = "\\(") == TRUE){
+          a <- str_split(diacyl$`Metabolite name`[i],pattern = "\\(") %>% unlist()
+          a1 <- a[2] %>%  str_split(pattern = "\\)") %>% unlist()
+          a2 <- a1[1] %>% str_split(pattern = " ") %>% unlist()
+          acyl1list[i] <- a2[2]
+        }
+        else{
+          acyl1list[i] <- NA
+        }
+      }
+      
+      diacyl$acyl1 <- unlist(acyl1list)
+      targetdiacyllipid <- diacyl %>%  filter(acyl1 %in% targetnacylchain) %>% select(-c(Categories))
+      
+      targetlipidclass <- unique(targetdiacyllipid$LbmClass)
+      
+      
+      
+      diacyl <- targetdiacyllipid %>% select(-c(acyl1))
+      
+      
+      
+    }
+    else{
+      
+    }
+    
+    
+    Lipidclassdata<- filter(diacyl , LbmClass == inputclass) %>% distinct(`Metabolite name`,.keep_all = TRUE)
+    if(length(Lipidclassdata$LbmClass) != 0){
+      d <- Lipidclassdata
+      rowname <- as.matrix((d$`Metabolite name`))
+      d <- select(d,-c(`Metabolite name`,LbmClass))
+      n <- nrow(d)
+      BreaksList <- seq(-2,2, by = 0.5)
+      n <- nrow(d)
+      height <- n/10*3
+      list <- c(-2,0,2)
+      lipid <- d
+      lipid <- lipid[,-1]
+      rownames(lipid) <- rowname
+      lipid <- scale_rows(lipid) %>% as.data.frame()
+      lipid <- tibble::rownames_to_column(lipid, "Metabolite name")
+      lipid <- pivot_longer(lipid,cols = -c(`Metabolite name`))
+      lipid <- mutate(lipid, name = name %>% factor(levels = input$levels))
+      ghm <- ggplot(lipid, aes(x = name, y =`Metabolite name` , fill = value))
+      ghm <- ghm + geom_tile(color = "black") + scale_fill_gradientn(
+        colors=c("navy","white","red"),limits=c(min(lipid$value), max(lipid$value))
+      ) + theme(axis.text.x = element_text(angle = 90, hjust = 1),axis.text = element_text(size = input$Fontsize))
+      print(ghm)
+      
+    }
+  }
+  if(unique(lipid_data_classmean$TotalChain) == "0"){
+    targetnacylchain <- c("16:0","16:1","18:0", "20:0", "22:0", "24:0", "24:1", "26:0", "26:1")
+    diacyl <- lipid_data_classmean %>% filter(TotalChain == "0")# %>%
+    targetlipidclass <- diacyl$LbmClass %>% unique()
+    diacyl <- diacyl[,-c(3:7)]
+    if(input$acylfilter == T){
+      
+      acyl1list <- list()
+      acyl2list <- list()
+      
+      for (i in 1:nrow(diacyl)) {
+        acyl1list[i] <- NA
+      }
+      
+      diacyl$acyl1 <- unlist(acyl1list)
+      targetdiacyllipid <- diacyl %>%  filter(acyl1 %in% targetnacylchain) %>% select(-c(Categories))
+      targetlipidclass <- unique(targetdiacyllipid$LbmClass)
+      
+      namelist <- list()
+      pathlist <- list()
+      
+      diacyl <- targetdiacyllipid %>% select(-c(acyl1))
+      
+      
+      
+    }
+    else{
+      
+    }
+    
+    Lipidclassdata<- filter(diacyl , LbmClass == inputclass) %>% distinct(`Metabolite name`,.keep_all = TRUE)
+    if(length(Lipidclassdata$LbmClass) != 0){
+      d <- Lipidclassdata
+      rowname <- as.matrix((d$`Metabolite name`))
+      d <- select(d,-c(`Metabolite name`,LbmClass))
+      n <- nrow(d)
+      BreaksList <- seq(-2,2, by = 0.5)
+      n <- nrow(d)
+      height <- n/10*3
+      list <- c(-2,0,2)
+      lipid <- d
+      lipid <- lipid[,-1]
+      rownames(lipid) <- rowname
+      lipid <- scale_rows(lipid) %>% as.data.frame()
+      lipid <- tibble::rownames_to_column(lipid, "Metabolite name")
+      lipid <- pivot_longer(lipid,cols = -c(`Metabolite name`))
+      lipid <- mutate(lipid, name = name %>% factor(levels = input$levels))
+      ghm <- ggplot(lipid, aes(x = name, y =`Metabolite name` , fill = value))
+      ghm <- ghm + geom_tile(color = "black") + scale_fill_gradientn(
+        colors=c("navy","white","red"),limits=c(min(lipid$value), max(lipid$value))
+      ) + theme(axis.text.x = element_text(angle = 90, hjust = 1),axis.text = element_text(size = input$Fontsize))
+      print(ghm)
+      
+    }}
+  if(unique(lipid_data_classmean$TotalChain) > 2 && unique(lipid_data_classmean$LbmClass) %in% c("TG","OxTG","HBMP","EtherTG")){
+    targettriacylchain <- c("16:0","16:1","18:0","18:1","18:2","18:3","20:3","20:4","20:5","22:4","22:5","22:6")
+    Triacyl <- lipid_data_classmean %>% filter(TotalChain > 2, LbmClass %in% c("TG","OxTG","HBMP","EtherTG"))
+    targetlipidclass <- Triacyl$LbmClass %>% unique()
+    Triacyl <- Triacyl[,-c(3:7)]
+    if(input$acylfilter == T){
+      acyllist1 <- list()
+      acyllist2 <- list()
+      acyllist3 <- list()
+      for (i in 1:nrow(Triacyl)) {
+        if(Triacyl$LbmClass[i] == "TG"){
+          
+          a <-  unlist(str_split(Triacyl$`Metabolite name`[i],pattern = "\\|"))[2] %>% str_split(pattern = " ")
+          a <- unlist(a)[2] %>% str_split(pattern = "_") %>% unlist()
+          acyllist1[i] <- a[1]
+          acyllist2[i] <- a[2]
+          acyllist3[i] <- a[3]
+        }else if(Triacyl$LbmClass[i] =="OxTG"){
+          
+          a <-  unlist(str_split(Triacyl$`Metabolite name`[i],pattern = "\\|"))[2] %>% str_split(pattern = " ")
+          a <- unlist(a)[2] %>% str_split(pattern = "_") %>% unlist()
+          acyllist1[i] <- a[1]
+          acyllist2[i] <- a[2]
+          b <- a[3] %>% str_split(pattern = ";") %>% unlist()
+          acyllist3[i] <- b[1]
+        }else if(Triacyl$LbmClass[i] =="HBMP"){
+          a <-  unlist(str_split(Triacyl$`Metabolite name`[i],pattern = "\\|"))[2] %>% str_split(pattern = " ")
+          a <- unlist(a)[2] %>% str_split(pattern = "_") %>% unlist()
+          b <- a[1] %>% str_split(pattern = "/") %>% unlist()
+          acyllist1[i] <-  b[1]
+          acyllist2[i] <-  b[2]
+          acyllist3[i] <-  a[2]
+        }else if(Triacyl$LbmClass[i] =="EtherTG"){
+          a <-  unlist(str_split(Triacyl$`Metabolite name`[i],pattern = "\\|"))[2] %>% str_split(pattern = " ")
+          a <- unlist(a)[2] %>% str_split(pattern = "_") %>% unlist()
+          acyl <- a[1] %>% str_split(pattern = "-") %>% unlist()
+          acyllist1[i] <- acyl[2]
+          acyllist2[i] <- a[2]
+          acyllist3[i]<- a[3]
+        }
+      }
+      
+      Triacyl$acyl1 <- unlist(acyllist1)
+      Triacyl$acyl2 <- unlist(acyllist2)
+      Triacyl$acyl3 <- unlist(acyllist3)
+      
+      targettriacyllipid <- Triacyl %>% filter(acyl1 %in% targettriacylchain, acyl2 %in% targettriacylchain,acyl3 %in% targettriacylchain) %>% select(-c(acyl1,acyl2,acyl3,Categories))
+    }
+    else{
+      targettriacyllipid <- Triacyl
+    }
+    Triacyl <- targettriacyllipid
+    # i <-
+    Lipidclassdata<- filter(Triacyl , LbmClass == inputclass) %>% distinct(`Metabolite name`,.keep_all = TRUE)
+    if(length(Lipidclassdata$LbmClass) != 0){
+      d <- Lipidclassdata
+      rowname <- as.matrix((d$`Metabolite name`))
+      d <- select(d,-c(`Metabolite name`,LbmClass))
+      BreaksList <- seq(-2,2, by = 0.5)
+      n <- nrow(d)
+      height <- n/10*3
+      list <- c(-2,0,2)
+      lipid <- d
+      lipid <- lipid[,-1]
+      rownames(lipid) <- rowname
+      lipid <- scale_rows(lipid) %>% as.data.frame()
+      lipid <- tibble::rownames_to_column(lipid, "Metabolite name")
+      lipid <- pivot_longer(lipid,cols = -c(`Metabolite name`))
+      lipid <- mutate(lipid, name = name %>% factor(levels = input$levels))
+      ghm <- ggplot(lipid, aes(x = name, y =`Metabolite name` , fill = value))
+      ghm <- ghm + geom_tile(color = "black") + scale_fill_gradientn(
+        colors=c("navy","white","red"),limits=c(min(lipid$value), max(lipid$value))
+      ) + theme(axis.text.x = element_text(angle = 90, hjust = 1),axis.text = element_text(size = input$Fontsize))
+      print(ghm)
+      
+    }}
+  if(unique(lipid_data_classmean$TotalChain) > 2 && ! unique(lipid_data_classmean$LbmClass) %in% c("TG","OxTG","HBMP","EtherTG")){
+    Triacyl <- lipid_data_classmean %>% filter(TotalChain > 2, ! LbmClass %in% c("TG","OxTG","HBMP","EtherTG"))
+    targetlipidclass <- Triacyl$LbmClass %>% unique()
+    Triacyl <- Triacyl[,-c(3:7)]
+    diacyl <- Triacyl
+    Lipidclassdata<- filter(diacyl , LbmClass == inputclass) %>% distinct(`Metabolite name`,.keep_all = TRUE)
+    if(length(Lipidclassdata$LbmClass) != 0){
+      d <- Lipidclassdata
+      rowname <- as.matrix((d$`Metabolite name`))
+      d <- d <- select(d,-c(`Metabolite name`,LbmClass))
+      n <- nrow(d)
+      BreaksList <- seq(-2,2, by = 0.5)
+      n <- nrow(d)
+      height <- n/10*3
+      list <- c(-2,0,2)
+      lipid <- d
+      lipid <- lipid[,-1]
+      rownames(lipid) <- rowname
+      lipid <- scale_rows(lipid) %>% as.data.frame()
+      lipid <- tibble::rownames_to_column(lipid, "Metabolite name")
+      lipid <- pivot_longer(lipid,cols = -c(`Metabolite name`))
+      lipid <- mutate(lipid, name = name %>% factor(levels = input$levels))
+      ghm <- ggplot(lipid, aes(x = name, y =`Metabolite name` , fill = value))
+      ghm <- ghm + geom_tile(color = "black") + scale_fill_gradientn(
+        colors=c("navy","white","red"),limits=c(min(lipid$value), max(lipid$value))
+      ) + theme(axis.text.x = element_text(angle = 90, hjust = 1),axis.text = element_text(size = input$Fontsize))
+      print(ghm)
+      
+    }}
+  
+}

app/R/librarys.R

@@ -0,0 +1,44 @@
+library(shiny)
+library(readr)
+library(shinyAce)
+library(ggplot2)
+library(dplyr)
+library(shinythemes)
+library(openintro)
+library(plotly)
+library(DT)
+library(ggprism)
+library(ggbeeswarm)
+library(reshape2)
+library(shinyFiles)
+library(stringr)
+library(shinyBS)
+library(shinyjs)
+library(shinydashboard)
+library(shinyWidgets)
+library(svglite)
+library(tidyr)
+library(pheatmap)
+library(grid)
+library(gridExtra)
+#library(RCy3)
+# library(rcdk)
+library(ggeasy)
+library(ggtext)
+library(colorspace)
+library(shinyjqui)
+library(tidyverse)
+library(gprofiler2)
+library(colourpicker)
+library(shinydashboardPlus)
+library(shiny)
+library(cyjShiny)
+library(graph)
+library(jsonlite)
+library(dplyr)
+library(reshape2)
+library(shinyjqui)
+library(shinyalert)
+library(bslib)
+library(rstatix)
+library(ggpubr)

app/R/modaldialog_utils.R

@@ -0,0 +1,64 @@
+ShowtestModaldialog = function(data,input,col){
+  showModal(modalDialog(
+    h2("Appearance setting"),
+    
+    div( class = "dynamicSI",
+         lapply(1:length(names(col)), function(i){
+           column(3,   
+                  colorPickr(paste0(names(col)[i],"_selectcolor"),theme = "monolith",label = paste0(names(col)[i],""),  selected  = col[i],interaction =  list(
+                    clear = T,
+                    save = T,
+                    RGBA = TRUE,
+                    input=TRUE,
+                    hex =TRUE
+                    
+                  ))       
+                  
+           )})),
+    br(),
+    br(),
+    br(),
+    br(),
+    br(),
+    easyClose = TRUE,
+    footer = NULL
+  ))
+}
+
+ShowtestModaldialog2 = function(data,input,col){
+  showModal(modalDialog(
+    title = "Plot in Dialog",
+    plotOutput("mappingraph"),
+    actionButton("actionplottest","pathway mapping",onclick = "$(tab).removeClass('disabled')"),
+    easyClose = TRUE,
+    footer = NULL
+  ))
+}
+
+
+ShowtestModaldialog4 = function(data,input,col){
+showModal(modalDialog(
+      h2("Appearance setting"),
+    
+    div( class = "dynamicSI",
+         lapply(1:length(names(col)), function(i){
+           column(3,   
+                  colorPickr(paste0(names(col)[i],"_selectcolor"),theme = "monolith",label = paste0(names(col)[i],""),  selected  = col[i],interaction =  list(
+                    clear = T,
+                    save = T,
+                    RGBA = TRUE,
+                    input=TRUE,
+                    hex =TRUE
+                    
+                  ))       
+                  
+           )})),
+    br(),
+    br(),
+    br(),
+    br(),
+    br(),
+    easyClose = TRUE,
+    footer = NULL
+    ))
+	}

app/R/plot_utils.R

@@ -0,0 +1,860 @@
+perform_statistical_test <- function(data, comparisons, test_method, y_var, w_var, p_adjust_method = "bonferroni") {
+  # 入力チェック
+  if (missing(data) || missing(comparisons) || missing(test_method) || 
+      missing(y_var) || missing(w_var)) {
+    stop("Required arguments are missing")
+  }
+  
+  # 検定関数の取得
+  test_func <- tryCatch({
+    get(test_method, asNamespace("rstatix"))
+  }, error = function(e) {
+    stop(paste("Invalid test method:", test_method))
+  })
+  
+  # 変数名を文字列からシンボルに変換
+  #y_sym <- sym(y_var)
+  w_sym <- sym(w_var)
+  
+  # 結果を格納するデータフレーム
+  results <- data.frame()
+  
+  # 各比較対についてループ
+  for(comp in comparisons) {
+    # グループを分割
+	print(comparisons)
+	print(comp)
+    groups <- strsplit(comp, "vs")[[1]]
+    print(as.formula(paste0("`", y_var, "` ~ `", w_var, "`")))
+	
+    # データのサブセット作成
+    subset_data <- data %>%
+      dplyr::filter(!!w_sym %in% groups)
+	  
+    print((subset_data$Class) %>% unique())
+
+    
+    # 検定実行
+    tryCatch({
+      test_result <- subset_data %>%
+        test_func(
+          formula = as.formula(paste0("`", y_var, "` ~ `", w_var, "`"))
+        ) %>%
+        adjust_pvalue(method = p_adjust_method) %>%
+        add_significance() 
+      
+      results <- bind_rows(results, test_result)
+      
+    }, error = function(e) {
+      warning(sprintf(
+        "Error in comparison %s: %s",
+        comp,
+        as.character(e)
+      ))
+    })
+  }
+  
+  # 結果が空の場合の処理
+  if (nrow(results) == 0) {
+    warning("No valid comparisons could be performed")
+    return(NULL)
+  }
+  
+  return(results)
+}
+
+
+
+
+
+create_boxplot <- function(data, x_var, y_var, col, alpha, size, xlab_text, ylab_text, ggtitle_text) {
+  p <- ggplot(data) +
+     geom_boxplot(aes_string(x = x_var, y = y_var, fill = x_var),color = "black", lwd = 1, alpha = alpha) +
+    ggbeeswarm::geom_beeswarm(aes_string(x = x_var, y = y_var, fill = x_var), dodge.width = 0.1,
+                               cex = 0.1,
+                               shape = 21,
+                               size = size) +
+	xlab(xlab_text) +
+    ylab(ylab_text) +
+    ggtitle(paste(ggtitle_text, "", sep = "")) +
+    scale_fill_manual(values = unlist(col))
+  p
+
+}
+
+process_boxplot <- function(input, yval, dataa, col,stat_test) {
+
+  y <- yval %>% as.character() %>% gsub("`", "", .)
+  p <- create_boxplot(dataa, input$w, yval, col, input$alpha, input$size, 
+                       input$xAxisLabel, input$yAxisLabel, yval)
+
+    if (input$pvaluecheck == TRUE && input$test_method != "anova_test") {
+      stat_results <- stat_test
+
+	    y_range <- diff(range(select(dataa,y)))
+		y_max <- max(select(dataa,y), na.rm = TRUE)
+		step_size <- y_range * 0.15
+        y_positions <- y_max + 
+        seq(step_size, by = step_size, length.out = nrow(stat_results))
+        
+        p <- p + stat_pvalue_manual(
+          stat_results,
+		  label.size = input$pvaluefontsize,
+          label = "p.adj.signif",
+          y.position = y_positions,
+          bracket.size = 0.5,
+          bracket.shorten = 0.1
+        )
+    }
+    
+    p <- p +
+      theme_classic() + 
+      theme(
+        axis.text.x = element_text(
+          angle = input$x_rotation,
+          size = input$Fontsize
+        ),
+        text = element_text(size = input$Fontsize)
+      ) 
+    
+    plot(p)
+}
+
+
+process_boxplot_diagram <- function(input,yval,dataa,col,stat_test) {
+  
+    y <- yval %>% as.character() %>% gsub("`", "", .)
+    p <- create_boxplot(dataa, input$w, yval, col, input$alpha, input$size, 
+                       input$xAxisLabel, input$yAxisLabel, yval)
+    p <- p + theme_prism(
+      base_fontface = "plain",
+      base_line_size = 0.9,
+      base_family = "Arial"
+    ) +
+      theme(
+        legend.position = "none",
+		axis.text.x = element_text(angle = input$x_rotation, 
+                                 size = input$Fontsize),
+        axis.title.x = element_blank(),
+        aspect.ratio = 1.0,
+        text = element_text(size = input$Fontsize)
+      )
+    #if (input$q != "" && input$pvaluecheck == TRUE) {
+    #  tukey_result <-  process_tukey_result(input,y,input$q,p,dataa,0.05,input$Fontsize)
+    #  p <- p + add_pvalue(tukey_result, remove.bracket = TRUE, bracket.size = 0.000001, label = "{symbol}", tip.length = 0,
+    #                      label.size = tukey_result$size, inherit.aes = FALSE,y.position = tukey_result$y.position_boxplot) + scale_y_continuous(
+    #                        limits = c(min(dataa[[y]]), (max(tukey_result$y.position_boxplot) + max(tukey_result$y.position_boxplot) / 30))
+    #                      )
+    #}
+	
+	 if (input$pvaluecheck == TRUE && input$test_method != "anova_test") {
+      stat_results <- stat_test
+
+	    y_range <- diff(range(select(dataa,y)))
+		y_max <- max(select(dataa,y), na.rm = TRUE)
+		step_size <- y_range * 0.15
+        y_positions <- y_max + 
+        seq(step_size, by = step_size, length.out = nrow(stat_results))
+        
+        p <- p + stat_pvalue_manual(
+          stat_results,
+		  label.size = input$pvaluefontsize,
+          label = "p.adj.signif",
+          y.position = y_positions,
+          bracket.size = 0.5,
+          bracket.shorten = 0.1
+        )
+    }
+	
+    p
+}
+
+process_action_boxplot <- function(input,dataa,metainfocol,svg_path,col,output,data) {
+  
+  withProgress(message = 'Calculation in progress', detail = 'This may take a while...', value = 1, {
+    startnum <- 1 + metainfocol
+    for (i in startnum:ncol(dataa)) {
+      yval <- colnames(dataa)[i]
+      p <- create_boxplot(dataa, input$w, dataa[[yval]], col, input$alpha, input$size,input$xAxisLabel, input$yAxisLabel, "")
+      p <- p + theme_prism(
+        base_fontface = "plain",
+        base_line_size = 0.9,
+        base_family = "Arial"
+      ) +
+        theme(
+          legend.position = "none",
+		  axis.text.x = element_text(angle = input$x_rotation,
+                                 size = input$Fontsize),
+          axis.title.x = element_blank(),
+          aspect.ratio = 1.0,
+          text = element_text(size = input$Fontsize)
+        )
+    #  if (input$q != "" && input$pvaluecheck == TRUE) {
+    #    tukey_result <-  process_tukey_result(input,yval,input$q,p,dataa,0.05,input$Fontsize)
+    #    p <- p + add_pvalue(tukey_result, remove.bracket = TRUE, bracket.size = 0.000001, label = "{symbol}", tip.length = 0,
+    #                        label.size = tukey_result$size, inherit.aes = FALSE,y.position = tukey_result$y.position_boxplot) + scale_y_continuous(
+    #                          limits = c(min(dataa[[yval]]), (max(tukey_result$y.position_boxplot) + max(tukey_result$y.position_boxplot) / 30))
+    #                        )
+    #  }
+	  
+	   if (input$pvaluecheck == TRUE && input$test_method != "anova_test") { 
+    # 検定関数の選択
+    test_func <- get(input$test_method, asNamespace("rstatix"))
+    
+    # 結果を格納するデータフレーム
+    results <- data.frame()
+    for(comp in input$comparisons) {
+      # グループを分割
+      groups <- strsplit(comp, "vs")[[1]]
+      
+      # データのサブセット作成
+      subset_data <- data %>%
+        dplyr::filter(!!sym(input$w) %in% groups)
+
+      # 検定実行
+      test_result <- test_func(subset_data, as.formula(paste(yval, "~", input$w)),paired = FALSE) %>%
+        adjust_pvalue(method = input$p_adjust) %>%
+        add_significance()
+      results <- rbind(results, test_result)
+    }
+    
+    stat_results <- results
+
+	   #stat_results <- perform_statistical_test(
+       #      data = dataa,
+       #      comparisons = input$comparisons,
+       #      test_method = input$test_method,  # または "t_test" など
+       #      y_var = yval,
+       #      w_var = input$w,
+       #      p_adjust_method = input$p_adjust
+       #)
+	    y_range <- diff(range(select(dataa,yval)))
+		y_max <- max(select(dataa,yval), na.rm = TRUE)
+		step_size <- y_range * 0.15
+        y_positions <- y_max + 
+        seq(step_size, by = step_size, length.out = nrow(stat_results))
+        
+        p <- p + stat_pvalue_manual(
+          stat_results,
+		  label.size = input$pvaluefontsize,
+          label = "p.adj.signif",
+          y.position = y_positions,
+          bracket.size = 0.5,
+          bracket.shorten = 0.1
+        )
+    }
+	  
+	  
+      svg(paste("./svg/",colnames(dataa)[i], ".svg", sep = ""))
+      plot(p)
+      dev.off()
+      incProgress(1 / ncol(dataa))
+      Sys.sleep(0.25)
+    }
+  })
+  data.frame(name = colnames(dataa)[-c(1:metainfocol)], path = paste("file:/", svg_path, "/", colnames(dataa)[-c(1:metainfocol)], ".svg", sep = "")) %>% write.csv("./svg/path.csv")
+  output$testtext1 <- renderText({
+    "Finish"
+  })
+}
+
+create_barplot <- function(data, x_var, y_var, col, alpha, size, xlab_text, ylab_text, ggtitle_text) {
+  p <- ggplot(data) +
+    geom_bar(aes_string(x = x_var, y = y_var, fill = x_var),color = "black", stat = "summary",fun = "mean",lwd = 1.2,width = 0.6) +
+	xlab(xlab_text) +
+    ylab(ylab_text) +
+    ggtitle(paste(ggtitle_text, "", sep = "")) +
+    scale_fill_manual(values = unlist(col))
+  
+  p
+}
+
+
+process_barplot <- function(input, yval, dataa, col,stat_test) {
+
+  y <- yval %>% as.character() %>% gsub("`", "", .)
+  p <- create_barplot(dataa, input$w, yval, col, input$alpha, input$size, 
+                       input$xAxisLabel, input$yAxisLabel, yval)
+
+    if (input$pvaluecheck == TRUE && input$test_method != "anova_test") {
+      stat_results <- stat_test
+
+	    y_range <- diff(range(select(dataa,y)))
+		y_max <- dataa %>%
+		select(1,!!sym(input$w),!!sym(y)) %>%
+        group_by(!!sym(input$w)) %>%
+        summarise(mean_value = mean(!!sym(y))) %>%
+        summarise(max_mean = max(mean_value)) %>%
+        pull(max_mean)
+        #y_max <- max(select(dataa,y), na.rm = TRUE)
+		print(y_max)
+		step_size <- y_range * 0.15
+        y_positions <- y_max + 
+        seq(step_size, by = step_size, length.out = nrow(stat_results))
+        
+        p <- p + stat_pvalue_manual(
+          stat_results,
+		  label.size = input$pvaluefontsize,
+          label = "p.adj.signif",
+          y.position = y_positions,
+          bracket.size = 0.5,
+          bracket.shorten = 0.1
+        )
+    }
+    
+    p <- p +
+      theme_classic() + 
+      theme(
+        axis.text.x = element_text(
+          angle = input$x_rotation,
+          size = input$Fontsize
+        ),
+        text = element_text(size = input$Fontsize)
+      ) 
+    
+    plot(p)
+}
+
+
+process_barplot_diagram <- function(input,yval,dataa,col,stat_test) {
+  
+  y <- yval %>% as.character()
+  p <- create_barplot(dataa, input$w, y, col, input$alpha, input$size, input$xAxisLabel, input$yAxisLabel, y)
+  p <- p + theme_prism(
+    base_fontface = "plain",
+    base_line_size = 0.9,
+    base_family = "Arial") +
+    theme(
+      legend.position = "none",
+	  axis.text.x = element_text(angle = input$x_rotation,
+                                 size = input$Fontsize),
+      aspect.ratio = 1.0,
+      text = element_text(size = input$Fontsize))
+	 if (input$pvaluecheck == TRUE && input$test_method != "anova_test") {
+      stat_results <- stat_test
+
+	    y_range <- diff(range(select(dataa,y)))
+		y_max <- max(select(dataa,y), na.rm = TRUE)
+		step_size <- y_range * 0.15
+        y_positions <- y_max + 
+        seq(step_size, by = step_size, length.out = nrow(stat_results))
+        
+        p <- p + stat_pvalue_manual(
+          stat_results,
+		  label.size = input$pvaluefontsize,
+          label = "p.adj.signif",
+          y.position = y_positions,
+          bracket.size = 0.5,
+          bracket.shorten = 0.1
+        )
+    }
+  p
+}
+
+
+
+process_action_barplot <- function(input,dataa,metainfocol,svg_path,col,output,data) {
+  withProgress(message = 'Calculation in progress', detail = 'This may take a while...', value = 1, {
+    startnum <- 1 + metainfocol
+    for (i in startnum:ncol(dataa)) {
+      yval <- colnames(dataa)[i]
+      p <- create_barplot(dataa, input$w, dataa[[yval]], col, input$alpha, input$size, input$xAxisLabel, input$yAxisLabel, "")
+      p <- p + theme_prism(
+        base_fontface = "plain",
+        base_line_size = 0.9,
+        base_family = "Arial") +
+        theme(
+          legend.position = "none",
+          axis.title.x = element_blank(),
+          aspect.ratio = 1.0,
+		  axis.text.x = element_text(angle = input$x_rotation,
+                                 size = input$Fontsize),
+          text = element_text(size = input$Fontsize))
+     # if (input$q!= "" && input$pvaluecheck == TRUE) {
+      #  tukey_result <-  process_tukey_result(input,yval,input$q,p,dataa,0.05,input$Fontsize)
+      #  p <- p + add_pvalue(tukey_result,remove.bracket = TRUE,
+      #                      bracket.size = 0.000001,
+      #                      label = "{symbol}",
+      #                      tip.length = 0,
+      #                      label.size = tukey_result$size,inherit.aes = FALSE,y.position = tukey_result$y.position_barplot) 
+     # }
+if (input$pvaluecheck == TRUE && input$test_method != "anova_test") { 
+    # 検定関数の選択
+    test_func <- get(input$test_method, asNamespace("rstatix"))
+    
+    # 結果を格納するデータフレーム
+    results <- data.frame()
+    for(comp in input$comparisons) {
+      # グループを分割
+      groups <- strsplit(comp, "vs")[[1]]
+      
+      # データのサブセット作成
+      subset_data <- data %>%
+        dplyr::filter(!!sym(input$w) %in% groups)
+
+      # 検定実行
+      test_result <- test_func(subset_data, as.formula(paste(yval, "~", input$w)),paired = FALSE) %>%
+        adjust_pvalue(method = input$p_adjust) %>%
+        add_significance()
+      results <- rbind(results, test_result)
+    }
+    
+    stat_results <- results
+
+	   #stat_results <- perform_statistical_test(
+       #      data = dataa,
+       #      comparisons = input$comparisons,
+       #      test_method = input$test_method,  # または "t_test" など
+       #      y_var = yval,
+       #      w_var = input$w,
+       #      p_adjust_method = input$p_adjust
+       #)
+	    y_range <- diff(range(select(dataa,yval)))
+		y_max <- max(select(dataa,yval), na.rm = TRUE)
+		step_size <- y_range * 0.15
+        y_positions <- y_max + 
+        seq(step_size, by = step_size, length.out = nrow(stat_results))
+        
+        p <- p + stat_pvalue_manual(
+          stat_results,
+		  label.size = input$pvaluefontsize,
+          label = "p.adj.signif",
+          y.position = y_positions,
+          bracket.size = 0.5,
+          bracket.shorten = 0.1
+        )
+    }
+      svg(paste("./svg/",colnames(dataa)[i], ".svg", sep = ""))
+      plot(p)
+      dev.off()
+    }
+	      incProgress(1 / ncol(dataa))
+      Sys.sleep(0.25)
+  })
+  data.frame(name = colnames(dataa)[-c(1:metainfocol)], path = paste("file:/", svg_path, "/", colnames(dataa)[-c(1:metainfocol)], ".svg", sep = "")) %>% write.csv("./svg/path.csv")
+  output$testtext1 <- renderText({
+    "Finish"
+  })
+}
+
+process_violinplot <- function(input, yval, dataa, col,stat_test) {
+
+  y <- yval %>% as.character() %>% gsub("`", "", .)
+  p <- create_violinplot(dataa, input$w, yval, col, input$alpha, input$size, 
+                       input$xAxisLabel, input$yAxisLabel, yval)
+
+    if (input$pvaluecheck == TRUE && input$test_method != "anova_test") {
+      stat_results <- stat_test
+
+	    y_range <- diff(range(select(dataa,y)))
+        y_max <- max(select(dataa,y), na.rm = TRUE)
+		step_size <- y_range * 0.15
+        y_positions <- y_max + 
+        seq(step_size, by = step_size, length.out = nrow(stat_results))
+        
+        p <- p + stat_pvalue_manual(
+          stat_results,
+		  label.size = input$pvaluefontsize,
+          label = "p.adj.signif",
+          y.position = y_positions,
+          bracket.size = 0.5,
+          bracket.shorten = 0.1
+        )
+    }
+    
+    p <- p +
+      theme_classic() + 
+      theme(
+        axis.text.x = element_text(
+          angle = input$x_rotation,
+          size = input$Fontsize
+        ),
+        text = element_text(size = input$Fontsize)
+      ) 
+    
+    plot(p)
+}
+
+create_violinplot <- function(data, x_var, y_var, col, alpha, size, xlab_text, ylab_text, ggtitle_text) {
+  p <- ggplot(data,aes_string(y = y_var)) +
+    geom_violin(aes_string(x = x_var,fill = x_var), adjust = 2,trim = FALSE) +
+	xlab(xlab_text) +
+    ylab(ylab_text) +
+    ggtitle(paste(ggtitle_text, "", sep = "")) +
+    scale_fill_manual(values = unlist(col))
+  
+  p
+}
+
+process_violinplot_diagram <- function(input,yval,dataa,col,stat_test) {
+
+  y <- yval %>% as.character()
+  p <- create_violinplot(dataa, input$w, y, col, input$alpha, input$size, input$xAxisLabel, input$yAxisLabel, y)
+  p <- p + theme_prism(
+    base_fontface = "plain",
+    base_line_size = 0.9,
+    base_family = "Arial") +
+    theme(
+      legend.position = "none",
+      axis.title.x = element_blank(),
+      aspect.ratio = 1.0,
+	  axis.text.x = element_text(angle = input$x_rotation, 
+                                 size = input$Fontsize),
+      text = element_text(size = input$Fontsize))
+	 if (input$pvaluecheck == TRUE && input$test_method != "anova_test") {
+      stat_results <- stat_test
+
+	    y_range <- diff(range(select(dataa,y)))
+		y_max <- max(select(dataa,y), na.rm = TRUE)
+		step_size <- y_range * 0.15
+        y_positions <- y_max + 
+        seq(step_size, by = step_size, length.out = nrow(stat_results))
+        
+        p <- p + stat_pvalue_manual(
+          stat_results,
+		  label.size = input$pvaluefontsize,
+          label = "p.adj.signif",
+          y.position = y_positions,
+          bracket.size = 0.5,
+          bracket.shorten = 0.1
+        )
+    }
+  p
+}
+
+process_action_violinplot <- function(input,dataa,metainfocol,svg_path,col,output,data) {
+  withProgress(message = 'Calculation in progress', detail = 'This may take a while...', value = 1, {
+    startnum <- 1 + metainfocol
+    for (i in startnum:ncol(dataa)) {
+      yval <- colnames(dataa)[i]
+      p <- create_violinplot(dataa, input$w, dataa[[yval]], col, input$alpha, input$size, input$xAxisLabel, input$yAxisLabel, "")
+      p <- p + theme_prism(
+        base_fontface = "plain",
+        base_line_size = 0.9,
+        base_family = "Arial") +
+        theme(
+          legend.position = "none",
+          axis.title.x = element_blank(),
+          aspect.ratio = 1.0,
+		  axis.text.x = element_text(angle = input$x_rotation,
+                                 size = input$Fontsize),
+          text = element_text(size = input$Fontsize))
+      #if (input$q!= "" && input$pvaluecheck == TRUE) {
+      #  tukey_result <-  process_tukey_result(input,yval,input$q,p,dataa,0.05,input$Fontsize)
+       # p <- p + add_pvalue(tukey_result, remove.bracket = TRUE, bracket.size = 0.000001, label = "{symbol}", tip.length = 0,
+       #                     label.size = tukey_result$size, inherit.aes = FALSE,y.position = tukey_result$y.position_boxplot) + scale_y_continuous(
+       #                       limits = c(min(dataa[[yval]]), (max(tukey_result$y.position_boxplot) + max(tukey_result$y.position_boxplot) / 30))
+       #                     )
+      #}
+if (input$pvaluecheck == TRUE && input$test_method != "anova_test") { 
+    # 検定関数の選択
+    test_func <- get(input$test_method, asNamespace("rstatix"))
+    
+    # 結果を格納するデータフレーム
+    results <- data.frame()
+    for(comp in input$comparisons) {
+      # グ��ープを分割
+      groups <- strsplit(comp, "vs")[[1]]
+      
+      # データのサブセット作成
+      subset_data <- data %>%
+        dplyr::filter(!!sym(input$w) %in% groups)
+
+      # 検定実行
+      test_result <- test_func(subset_data, as.formula(paste(yval, "~", input$w)),paired = FALSE) %>%
+        adjust_pvalue(method = input$p_adjust) %>%
+        add_significance()
+      results <- rbind(results, test_result)
+    }
+    
+    stat_results <- results
+
+	   #stat_results <- perform_statistical_test(
+       #      data = dataa,
+       #      comparisons = input$comparisons,
+       #      test_method = input$test_method,  # または "t_test" など
+       #      y_var = yval,
+       #      w_var = input$w,
+       #      p_adjust_method = input$p_adjust
+       #)
+	    y_range <- diff(range(select(dataa,yval)))
+		y_max <- max(select(dataa,yval), na.rm = TRUE)
+		step_size <- y_range * 0.15
+        y_positions <- y_max + 
+        seq(step_size, by = step_size, length.out = nrow(stat_results))
+        
+        p <- p + stat_pvalue_manual(
+          stat_results,
+		  label.size = input$pvaluefontsize,
+          label = "p.adj.signif",
+          y.position = y_positions,
+          bracket.size = 0.5,
+          bracket.shorten = 0.1
+        )
+    }
+      svg(paste("./svg/",colnames(dataa)[i], ".svg", sep = ""))
+      plot(p)
+      dev.off()
+    }
+    incProgress(1 / ncol(dataa))
+    Sys.sleep(0.25)
+  })
+  data.frame(name = colnames(dataa)[-c(1:metainfocol)], path = paste("file:/", svg_path, "/", colnames(dataa)[-c(1:metainfocol)], ".svg", sep = "")) %>% write.csv("./svg/path.csv")
+  output$testtext1 <- renderText({
+    "Finish"
+  })
+}
+process_polarplot <- function(input,yval,dataa,col) {
+  
+  # if(is.na(unique(select(dataa,input$w))) != TRUE){
+  y <- yval %>% as.character()
+  p <- create_polarplot(dataa, input$w, y, col, input$alpha, input$size, input$yAxisLabel, y, input$Fontsize)
+  if (input$q != "" && input$pvaluecheck == TRUE) {
+    tukey_result <-  process_tukey_result(input,y,input$q,p,dataa,0.05,input$Fontsize)
+    p <- p + add_pvalue(tukey_result,remove.bracket = TRUE,
+                        bracket.size = 0.000001,
+                        label = "{symbol}",
+                        tip.length = 0,
+                        label.size = tukey_result$size,inherit.aes = FALSE,y.position = tukey_result$y.position_barplot)
+  }
+  plot(p)
+  # }
+}
+
+create_polarplot <- function(data, x_var, y_var, col, alpha, size, ylab_text, ggtitle_text, Fontsize) {
+  p <- ggplot(data, aes_string(x = x_var, y = y_var, fill = x_var)) +
+    geom_bar(color = "black", stat = "summary",fun = "mean",lwd = 1.2,width = 0.6) +
+    coord_polar()+
+    scale_fill_manual(values = unlist(col))+
+    labs(
+      x="", 
+      y="")+
+    theme_minimal(10) +
+    theme(
+      axis.text.x = element_text(size = Fontsize, angle = 360,hjust=0.8),
+      axis.title.x = element_blank(),
+      panel.grid.minor = element_blank(),
+      axis.ticks.y = element_blank())+
+    easy_remove_axes()
+  
+  return(p)
+}
+
+
+process_dotplot <- function(input, yval, dataa, col,stat_test) {
+
+  y <- yval %>% as.character() %>% gsub("`", "", .)
+  p <- create_dotplot(dataa, input$w, yval, col, input$alpha, input$size, 
+                       input$xAxisLabel, input$yAxisLabel, yval)
+
+    if (input$pvaluecheck == TRUE && input$test_method != "anova_test") {
+      stat_results <- stat_test
+	    y_range <- diff(range(select(dataa,y)))
+        y_max <- max(select(dataa,y), na.rm = TRUE)
+		step_size <- y_range * 0.15
+        y_positions <- y_max + 
+        seq(step_size, by = step_size, length.out = nrow(stat_results))
+        
+        p <- p + stat_pvalue_manual(
+          stat_results,
+		  label.size = input$pvaluefontsize,
+          label = "p.adj.signif",
+          y.position = y_positions,
+          bracket.size = 0.5,
+          bracket.shorten = 0.1
+        )
+    }
+    
+    p <- p +
+      theme_classic() + 
+      theme(
+        axis.text.x = element_text(
+          angle = input$x_rotation,
+          size = input$Fontsize
+        ),
+        text = element_text(size = input$Fontsize)
+      ) 
+    
+    plot(p)
+}
+
+create_dotplot <- function(data, x_var, y_var, col, alpha, size, xlab_text, ylab_text, ggtitle_text) {
+  p <- ggplot(data,aes_string(y = y_var)) +
+    ggbeeswarm::geom_beeswarm(aes_string(x = x_var,fill = x_var),
+                              cex = 0.1,
+                              shape = 21,
+                              size = size)+
+	xlab(xlab_text) +
+    ylab(ylab_text) +
+    ggtitle(paste(ggtitle_text, "", sep = "")) +
+    scale_fill_manual(values = unlist(col)) 
+  p
+}
+
+process_dotplot_diagram <- function(input,yval,dataa,col,stat_test) {
+  
+  
+  y <- yval %>% as.character()
+  p <- create_dotplot(dataa, input$w, y, col, input$alpha, input$size, input$xAxisLabel, input$yAxisLabel, y)
+  p <- p + theme_prism(
+    base_fontface = "plain",
+    base_line_size = 0.9,
+    base_family = "Arial"
+  ) +
+    theme(
+      legend.position = "none",
+      axis.title.x = element_blank(),
+      aspect.ratio = 1.0,
+	  axis.text.x = element_text(angle = input$x_rotation,
+                                 size = input$Fontsize),
+      text = element_text(size = input$Fontsize)
+    )
+	 if (input$pvaluecheck == TRUE && input$test_method != "anova_test") {
+      stat_results <- stat_test
+
+	    y_range <- diff(range(select(dataa,y)))
+		y_max <- max(select(dataa,y), na.rm = TRUE)
+		step_size <- y_range * 0.15
+        y_positions <- y_max + 
+        seq(step_size, by = step_size, length.out = nrow(stat_results))
+        
+        p <- p + stat_pvalue_manual(
+          stat_results,
+		  label.size = input$pvaluefontsize,
+          label = "p.adj.signif",
+          y.position = y_positions,
+          bracket.size = 0.5,
+          bracket.shorten = 0.1
+        )
+    }
+  p
+}
+
+process_action_dotplot <- function(input,dataa,metainfocol,svg_path,col,output,data) {
+  
+  withProgress(message = 'Calculation in progress', detail = 'This may take a while...', value = 1, {
+    startnum <- 1 + metainfocol
+    for (i in startnum:ncol(dataa)) {
+      yval <- colnames(dataa)[i]
+      p <- create_dotplot(dataa, input$w, dataa[[yval]], col, input$alpha, input$size, input$xAxisLabel, input$yAxisLabel, "")
+      p <- p + theme_prism(
+        base_fontface = "plain",
+        base_line_size = 0.9,
+        base_family = "Arial"
+      ) +
+        theme(
+          legend.position = "none",
+          axis.title.x = element_blank(),
+          aspect.ratio = 1.0,
+		  axis.text.x = element_text(angle = input$x_rotation,
+                                 size = input$Fontsize),
+          text = element_text(size = input$Fontsize)
+        )
+     # if (input$q != "" && input$pvaluecheck == TRUE) {
+     #   tukey_result <-  process_tukey_result(input,yval,input$q,p,dataa,0.05,input$Fontsize)
+     #   p <- p + add_pvalue(tukey_result, remove.bracket = TRUE, bracket.size = 0.000001, label = "{symbol}", tip.length = 0,
+     #                       label.size = tukey_result$size, inherit.aes = FALSE,y.position = tukey_result$y.position_boxplot) + scale_y_continuous(
+     #                         limits = c(min(dataa[[yval]]), (max(tukey_result$y.position_boxplot) + max(tukey_result$y.position_boxplot) / 30))
+      #                      )
+     # }
+if (input$pvaluecheck == TRUE && input$test_method != "anova_test") { 
+    # 検定関数の選択
+    test_func <- get(input$test_method, asNamespace("rstatix"))
+    
+    # 結果を格納するデータフレーム
+    results <- data.frame()
+    for(comp in input$comparisons) {
+      # グループを分割
+      groups <- strsplit(comp, "vs")[[1]]
+      
+      # データのサブセット作成
+      subset_data <- data %>%
+        dplyr::filter(!!sym(input$w) %in% groups)
+
+      # 検定実行
+      test_result <- test_func(subset_data, as.formula(paste(yval, "~", input$w)),paired = FALSE) %>%
+        adjust_pvalue(method = input$p_adjust) %>%
+        add_significance()
+      results <- rbind(results, test_result)
+    }
+    
+    stat_results <- results
+
+	   #stat_results <- perform_statistical_test(
+       #      data = dataa,
+       #      comparisons = input$comparisons,
+       #      test_method = input$test_method,  # または "t_test" など
+       #      y_var = yval,
+       #      w_var = input$w,
+       #      p_adjust_method = input$p_adjust
+       #)
+	    y_range <- diff(range(select(dataa,yval)))
+		y_max <- max(select(dataa,yval), na.rm = TRUE)
+		step_size <- y_range * 0.15
+        y_positions <- y_max + 
+        seq(step_size, by = step_size, length.out = nrow(stat_results))
+        
+        p <- p + stat_pvalue_manual(
+          stat_results,
+		  label.size = input$pvaluefontsize,
+          label = "p.adj.signif",
+          y.position = y_positions,
+          bracket.size = 0.5,
+          bracket.shorten = 0.1
+        )
+    }
+      svg(paste("./svg/",colnames(dataa)[i], ".svg", sep = ""))
+      plot(p)
+      dev.off()
+      incProgress(1 / ncol(dataa))
+      Sys.sleep(0.25)
+    }
+  })
+  data.frame(name = colnames(dataa)[-c(1:metainfocol)], path = paste("file:/", svg_path, "/", colnames(dataa)[-c(1:metainfocol)], ".svg", sep = "")) %>% write.csv("./svg/path.csv")
+  output$testtext1 <- renderText({
+    "Finish"
+  })
+}
+
+process_select_color_input <- function(input,data) {
+  if (length(grep(paste0(unique(as.matrix(select(data, input$w)))[1], "_selectcolor"), names(input), value = TRUE)) != 0) {
+    inputlist <- grep("selectcolor", names(input), value = TRUE)
+    for (input_name in inputlist) {
+      collist[str_remove_all(input_name, pattern = "_selectcolor")] <- input[[input_name]]
+    }
+    cola <<- unlist(collist)
+    cola <- cola[c(unique(data[[input$w]]))]
+    return(cola)
+  }
+}
+
+
+
+process_tukey_result <- function(input,yval, q,plot_obj, dataa, p_adj_threshold, Fontsize) {
+  yval <- gsub("`","",yval)
+  y <- dataa %>% pull(as.symbol(yval))
+  w <- dataa %>% pull(as.symbol(input$w))
+  tukey_result <- TukeyHSD(aov(y ~ w, data = dataa))
+  tukey_result <- data.frame(tukey_result$w)
+  a <- str_split_fixed(rownames(tukey_result), "-", 2)
+  colnames(a) <- c("Class1", "Class2")
+  
+  tukey_result <- cbind(a, tukey_result)
+  maxvalue <- select(dataa,input$w,yval) %>%
+    group_by_(input$w) %>%
+    mutate(Mean = mean(!!as.symbol(yval))) %>%
+    ungroup() %>%
+    distinct(!!as.symbol(input$w),.keep_all = TRUE )
+  
+  maxvalue <- max(maxvalue$Mean)
+  tukey_result <- filter(tukey_result, Class1 %in% q | Class2 %in% q) %>%
+    mutate(group2 = ifelse(Class1 == q, Class2, Class1)) %>%
+    mutate(group1 = paste0(q)) %>%
+    mutate(symbol = ifelse(p.adj < p_adj_threshold, "*", "")) %>%
+    select(group1, group2, p.adj, symbol) %>%
+    mutate(y.position_boxplot = max(dataa[[yval]])) %>%
+    mutate(y.position_barplot = maxvalue-maxvalue/15 ) %>%
+    mutate(y.min.position = min(dataa[[yval]])) %>%
+    mutate(size = ifelse(symbol != "*", 0, Fontsize))
+  return(tukey_result)
+}

app/R/runShinyApp.R

@@ -0,0 +1,2 @@
+shiny::runApp(appDir = getwd())
+

app/R/runplumber.R

@@ -0,0 +1,52 @@
+library(plumber)
+# 'plumber.R' is the location of the file shown above
+#
+ # RUN = "TRUE"
+ # while(RUN == "TRUE"){
+ #   a <- readLines("./runShinyApp.Rout")
+ #   b <- grepl("Close the program", a)
+ #   if(TRUE %in% b) {
+ #     RUN = "FALSE"
+ #     stop()
+ #   }
+ # }
+pr("./shiny/plumber.R") %>%
+  pr_run()
+
+
+# library(plumbplumb)
+# library(plumber)
+# 
+# .state <- new.env(parent = emptyenv()) #create .state when package is first loaded
+# 
+# start_plumber3 <- function(path) {
+#   trml <- rstudioapi::terminalCreate(show = FALSE)
+#   rstudioapi::terminalSend(trml, "R\n") 
+#   Sys.sleep(2)
+#   cmd <- sprintf('plumber::plumb("%s")$run()\n', path)
+#   rstudioapi::terminalSend(trml, cmd)
+#   
+#   .state[["trml"]] <- trml #store terminal name
+#   invisible(trml)
+# }
+# 
+# kill_plumber <- function() {
+#   rstudioapi::terminalKill(.state[["trml"]]) #access terminal name
+# }
+# 
+# 
+# 
+# plumber_path <- system.file("./shiny/plumber.R", package = "plumbplumb")
+# start_plumber3(plumber_path)
+
+# while (TRUE) {
+#   a <- readLines("./runShinyApp.Rout")
+#   b <- grepl("Close the program", a)
+#   print("ccccc")
+#   if (TRUE %in% b) {
+#     message("Stopping the loop...")
+#     future::plan("sequential")
+#     quit()
+#   }
+#   Sys.sleep(1)
+# }

app/R/selectcolor_utils.R

@@ -0,0 +1,12 @@
+collist <- list()
+colset <- function(input,col){
+  if(length(grep("selectcolor", names(input), value = TRUE)) != 0){
+    inputlist<- grep("selectcolor", names(input), value = TRUE) 
+    for (input_name in inputlist) {
+      
+      collist[str_remove_all(input_name,pattern = "_selectcolor")] <- input[[input_name]]
+      
+    }
+    col <<- unlist(collist)
+    col <<- col[c(unique(data[[input$x]]))]
+  }}

app/R/ui_modules.R

@@ -0,0 +1,580 @@
+library(shinyjs)
+plotTabAppearanceUI <- function(title, imgSrc, width, height, outputId, outputId2, textOutputId) {
+ card(
+    h3(title),
+    
+    # コンテナ領域の設定
+    div(
+      style = "position: relative; height: 1500px;",
+      
+      # 1つ目のプロット
+      jqui_draggable(
+        jqui_resizable(
+          div(style = "width: 600px; position: absolute; left: 0px; top: 20px;",
+            wellPanel(
+              style = "border: 2px solid #ddd; border-radius: 4px; box-shadow: 0 1px 3px rgba(0,0,0,0.12);",
+              plotOutput(outputId, width = "100%", height = "600px")
+            )
+          ),
+          options = list(
+            handles = "e, w",
+            minWidth = 200,
+            maxWidth = 2000,
+            grid = 50,
+            animate = FALSE
+          )
+        ),
+        options = list(
+          grid = c(50, 50),
+          containment = "parent",
+          snap = TRUE,
+          snapTolerance = 20
+        )
+      ),
+	  
+	  jqui_draggable(
+        jqui_resizable(
+          div(style = "width: 600px; position: absolute; left: 1300px; top: 20px;",
+            wellPanel(
+               style = "border: 2px solid #ddd; border-radius: 4px; box-shadow: 0 1px 3px rgba(0,0,0,0.12);",
+               verbatimTextOutput("stat_results"),
+               tableOutput("summary_stats")
+            )
+          ),
+          options = list(
+            handles = "e, w",
+            minWidth = 200,
+            maxWidth = 2000,
+            grid = 50,
+            animate = FALSE
+          )
+        ),
+        options = list(
+          grid = c(50, 50),
+          containment = "parent",
+          snap = TRUE,
+          snapTolerance = 20
+        )
+      ),
+      
+      # 2つ目のプロット
+      jqui_draggable(
+        jqui_resizable(
+          div(style = "width: 600px; position: absolute; left: 650px; top: 20px;",
+            wellPanel(
+              style = "border: 2px solid #ddd; border-radius: 4px; box-shadow: 0 1px 3px rgba(0,0,0,0.12);",
+              plotOutput(outputId2, width = "100%", height = "600px")
+            )
+          ),
+          options = list(
+            handles = "e, w",
+            minWidth = 200,
+            maxWidth = 2000,
+            grid = 50,
+            animate = FALSE
+          )
+        ),
+        options = list(
+          grid = c(50, 50),
+          containment = "parent",
+          snap = TRUE,
+          snapTolerance = 20
+        )
+      )
+    ),
+    br(), br(), br(), br()
+  )
+}
+
+
+         
+createCustomSidebar <- function(y_id, w_id, z_id, alpha_id, size_id, fontsize_id,orderInputId, pValueCheckId, qInputId) {
+   card(
+    full_screen = TRUE,
+    card_header(""),
+    card_body(
+    # カスタムCSSの追加
+    # CSSの修正
+tags$head(
+  tags$style(HTML("
+    .plot-type-container {
+      display: flex;
+      align-items: center;  /* 中央揃えに変更 */
+      justify-content: flex-start; /* 左寄せに変更 */
+      width: 100%;
+      margin-bottom: 20px;
+      padding: 10px 0;
+    }
+
+    .plot-buttons-container {
+      display: flex;
+      gap: 40px;  /* ボタン間の間隔を調整 */
+      margin-left: 40px;  /* dropInputとの間隔 */
+    }
+
+    .round-button {
+      border-radius: 50%;
+      width: 55px;
+      height: 55px;
+      padding: 0;
+      border: 2px solid #007bff;
+      background-color: white;
+      color: #007bff;
+      transition: all 0.3s;
+    }
+
+    .button-with-label {
+      display: flex;
+      flex-direction: column;
+      align-items: center;
+      width: 70px;  /* 幅を固定 */
+    }
+
+    .button-label {
+      text-align: center;
+      font-size: 12px;  /* フォントサイズを小さく */
+      margin-top: 8px;  /* ボタンとの間隔 */
+      line-height: 1.3;  /* 行間を調整 */
+      color: #666;
+    }
+  "))
+),
+
+# HTML構造の修正
+div(
+  class = "plot-type-container",
+  # dropInput
+  esquisse::dropInput(
+    inputId = "mydrop",
+    choicesNames = tagList(
+      list(img(
+        src = "./boxplot.png",
+        style = "margin-left: 5px; margin-bottom: 4px; margin-top: 0px; width: 50px; height: 50px;"
+      ), style = "width: 100px;"),
+      list(img(
+        src = "./barplot.png",
+        style = "margin-left: 5px; margin-bottom: 4px; margin-top: 0px; width: 50px; height: 50px;"
+      ), style = "width: 100px;"),
+      list(img(
+        src = "./violin2.png",
+        style = "margin-left: 5px; margin-bottom: 4px; margin-top: 0px; width: 50px; height: 50px;"
+      ), style = "width: 100px;"),
+      list(img(
+        src = "./dotplot.png",
+        style = "margin-left: 5px; margin-bottom: 4px; margin-top: 0px; width: 50px; height: 50px;"
+      ), style = "width: 100px;")
+    ),
+    choicesValues = c("box", "bar", "violin", "coding"),
+    dropWidth = "220px"
+  ),
+  
+  # ボタンコンテナ
+   div(
+    class = "plot-buttons-container",
+    # 保存ボタン
+    div(
+      class = "button-with-label",
+      actionButton("Colorpicker", 
+                  label = icon("palette", class = "fa-lg"),  # アイコンサイズ調整 
+                  class = "round-button"),
+      div(
+        class = "button-label",
+        "Color",
+        tags$br(),
+        "setting"
+      )
+    ),
+    
+    # ダウンロードボタン
+    div(
+      class = "button-with-label",
+      actionButton("exportgraph", 
+                  label = icon("diagram-project", class = "fa-lg"),  # アイコンサイズ調整
+                  class = "round-button"),
+      div(
+        class = "button-label",
+        "Pathway",
+        tags$br(),
+        "mapping"
+      )
+    )
+  )
+),
+accordion(
+        accordion_panel(
+		"Plot Settings",
+    selectInput(inputId = y_id, label = "Y-axis:", choices = NULL),
+	      selectInput(
+        inputId = "mol",
+        label = "molecule:",
+        choices = NULL
+      ),
+    selectInput(inputId = w_id, label = "X-axis:", choices = c("Class"), selected = "Class"),
+	numericInput("x_rotation", "X-axis Label Rotation:",
+                  value = 0, min = -90, max = 90),
+    sliderInput(inputId = alpha_id, label = "Transparency:", min = 0, max = 1, value = 1),
+    chooseSliderSkin("Round", color = "DarkTurquoise"),
+    sliderInput(inputId = size_id, label = "Size:", min = 0, max = 10, value = 5),
+    numericInput(inputId = fontsize_id, label = "Fontsize:", value = 20, min = 1, max = 30),
+	textInput("AxisLabel", "Enter x-axis label:", ""),
+	textInput("yAxisLabel", "Enter y-axis label:", "Normalized expression"),
+    orderInput(
+      inputId = orderInputId,
+      label = "Factor level order",
+      items = c(""),
+      width = "100%"
+    ),
+    selectInput(
+      inputId = qInputId,
+      label = "p-value (Tukey-Kramer test) from:",
+      choices = c(""),
+      selected = ""
+    )),
+	accordion_panel(
+          "Statistical Test Settings",
+		  checkboxInput(
+      inputId = pValueCheckId,
+      label = "Add p-value",
+      value = FALSE
+                         ),
+          selectInput("test_method", "Statistical Test:",
+                     choices = c("t-test" = "t_test",
+                               "Wilcoxon" = "wilcox_test",
+                               "ANOVA" = "anova_test")),
+          
+          conditionalPanel(
+            condition = "input.test_method == 'anova_test'",
+            selectInput("post_hoc", "Post-hoc test:",
+                       choices = c("Tukey" = "tukey",
+                                 "Bonferroni" = "bonferroni"))
+          ),
+          
+          conditionalPanel(
+            condition = "input.test_method != 'anova_test'",
+            selectInput("p_adjust", "P-value adjustment:",
+                       choices = c("Bonferroni" = "bonferroni",
+                                 "Holm" = "holm",
+                                 "BH" = "BH",
+                                 "Hochberg" = "hochberg",
+                                 "None" = "none"),
+                       selected = "bonferroni")
+          ),
+          
+          numericInput("significance", "Significance Level:",
+                      value = 0.05, min = 0.01, max = 0.1, step = 0.01),
+		  numericInput("pvaluefontsize", "Fontsize for pvalue symbol:",
+                      value = 10, min = 1, max = 30),		  
+        br(),
+          div(
+            class = "d-grid gap-2 d-md-flex",
+            actionButton("select_all", "Select All", 
+                        class = "btn-secondary"),
+            actionButton("clear_all", "Clear All", 
+                        class = "btn-secondary")),
+						uiOutput("comparison_choices"))
+          
+        ),
+    br(),
+    h5("Ver 1.20241114"),
+    br()
+	)
+  )
+}
+
+transcriptomeuploadpanel <- 
+div(
+          class = "input-panel",
+          style = "
+            background: white;
+            padding: 25px;
+            border-radius: 12px;
+            box-shadow: 0 4px 15px rgba(0,0,0,0.08);
+          ",
+  div(
+            class = "section-title",
+            style = "
+              margin-bottom: 25px;
+              padding-bottom: 15px;
+              border-bottom: 2px solid #2b2b2b;
+            ",
+            h3("Other Omics Data", 
+               style = "
+                 color: #2b2b2b;
+                 font-weight: 500;
+                 margin: 0;
+               ")
+          ),
+  selectInput(
+    inputId = "transcriptomefiletype",
+    label = "Input format:",
+    choices = c("Sample in cols"),
+    selected = "Sample in cols"
+  ),
+  fileInput(
+    inputId = "transcriptomefile",
+    label = "Input transcriptome data:",
+    accept = c("text/csv", "text/comma-separated-values,text/plain", ".csv")
+  ),
+  hr(),
+  div(
+            class = "section-title",
+            style = "
+              margin-bottom: 25px;
+              padding-bottom: 15px;
+              border-bottom: 2px solid #2b2b2b;
+            ",
+            h3("Input Meta Data", 
+               style = "
+                 color: #2b2b2b;
+                 font-weight: 500;
+                 margin: 0;
+               ")
+          ),
+  h5("If necessary, you can add class information.", style = "font-style: italic; color: #7f8c8d;"),
+  fileInput(
+    inputId = "file2",
+    label = "Input meta data file:",
+    accept = c("text/csv", "text/comma-separated-values,text/plain", ".csv")
+  )
+)
+
+uploadpanel <- tabPanel(
+  value = "uploadtab",
+  tagList(
+    div(
+      style = "display: flex; align-items: center; justify-content: center; margin-bottom: 15px;",
+      img(src = "./upload.png", style = "width: 50px; height: 50px; margin-right: 10px;"),
+      div(style = "font-size: 18px; color: #000000; font-weight: bold;", "Data Upload")
+    )
+  ),
+  fluidRow(
+      # メタボローム入力パネル
+      column(
+        width = 3,
+        div(
+          class = "input-panel",
+          style = "
+            background: white;
+            padding: 25px;
+            border-radius: 12px;
+            box-shadow: 0 4px 15px rgba(0,0,0,0.08);
+          ",
+          
+          # メタボロームテーブル入力セクション
+          div(
+            class = "section-title",
+            style = "
+              margin-bottom: 25px;
+              padding-bottom: 15px;
+              border-bottom: 2px solid #2b2b2b;
+            ",
+            h3("Input Metabolome Table (.csv)", 
+               style = "
+                 color: #2b2b2b;
+                 font-weight: 500;
+                 margin: 0;
+               ")
+          ),
+          
+          # 入力フォーマット選択
+          div(
+            class = "input-group",
+            style = "margin-bottom: 20px;",
+            selectInput(
+              inputId = "filetype",
+              label = tags$span(
+                style = "color: #5f6368; font-weight: 500;",
+                "Input format:"
+              ),
+              choices = c("MS-DIAL export", "Sample in rows"),
+              selected = "MS-DIAL export"
+            )
+          ),
+          
+          # ファイル入力フィールド
+          div(
+            class = "file-input-group",
+            style = "margin-bottom: 20px;",
+            fileInput(
+              inputId = "file1",
+              label = tags$span(
+                style = "color: #5f6368; font-weight: 500;",
+                "Input peak table:"
+              ),
+              accept = c("text/csv", 
+                        "text/comma-separated-values,text/plain", 
+                        ".csv"),
+              width = "100%"
+            )
+          ),
+          
+          fileInput(
+            inputId = "ontfile",
+            label = tags$span(
+              style = "color: #5f6368; font-weight: 500;",
+              "Input ontology file:"
+            ),
+            accept = c("text/csv", 
+                      "text/comma-separated-values,text/plain", 
+                      ".csv"),
+            width = "100%"
+          ),
+          
+          tags$hr(style = "
+            border: 0;
+            height: 1px;
+            background: #e0e0e0;
+            margin: 25px 0;
+          "),
+          
+          # テストデータセクション
+          div(
+            class = "section-title",
+            style = "margin-bottom: 25px;",
+            h3("Try Our Test Data", 
+               style = "
+                 color: #2b2b2b;
+                 font-weight: 500;
+                 border-bottom: 2px solid #2b2b2b;
+                 padding-bottom: 15px;
+               ")
+          ),
+          
+          div(
+    style = "text-align: left; margin: 20px;",
+    downloadButton(
+      outputId = "downloadData",
+      label = "Download Demo Data",
+      style = "
+        background: transparent;
+        color: #1a1a1a;
+        border: 2px solid #1a1a1a;
+        border-radius: 6px;
+        font-size: 16px;
+        font-weight: 500;
+        padding: 10px 30px;
+        transition: all 0.3s ease;
+        &:hover {
+          background: #1a1a1a;
+          color: #f5f5f5;
+          box-shadow: 0 2px 8px rgba(26,115,232,0.2);
+        }
+      "
+    )
+  ),
+            div(
+            class = "section-title",
+            style = "
+              margin-bottom: 25px;
+              padding-bottom: 15px;
+              border-bottom: 2px solid #2b2b2b;
+            ",
+            h3("Submit", 
+               style = "
+                 color: #2b2b2b;
+                 font-weight: 500;
+                 margin: 0;
+               ")
+          ),
+          
+          # 送信ボタン
+          div(
+            style = "margin: 20px; text-align: left;",
+            actionButton(
+              inputId = "submit",
+              label = "Submit Data",
+              class = "btn-submit",
+              style = "
+                background: transparent;
+                color: #1a1a1a;
+                border: 2px solid #1a1a1a;
+                border-radius: 6px;
+                font-size: 16px;
+                font-weight: 500;
+                padding: 10px 30px;
+                transition: all 0.3s ease;
+                &:hover {
+                  background: #1a1a1a;
+                  color: #f5f5f5;
+                  box-shadow: 0 2px 8px rgba(26,115,232,0.2);
+                }
+              "
+            )
+          )
+        )
+      ),
+      
+      # トランスクリプトームアップロードパネル
+      column(
+        width = 3,
+        transcriptomeuploadpanel
+      )
+    )
+)
+
+originalplotTabAppearanceUI <- tabPanel(
+  value = "test",
+  tagList(
+    img(
+      src = "./cord.png",
+      style = "margin-left: 5px; margin-bottom: 4px; margin-top: 0px; width: 50px; height: 50px;"
+    ),
+    div(
+      style = "font-size: 10px; color: #000000; text-align: center;",
+      HTML(' '), "coding", HTML(' ')
+    )
+  ),
+  mainPanel(
+    width = 10,
+    h3("original"),
+    fluidRow(
+      column(
+        width = 6,
+        aceEditor(
+          "code",
+          value = "p <- ggplot(data = data, aes(y = Yvariable, x = Class)) + 
+                  ggbeeswarm::geom_beeswarm(aes(fill = Class), 
+                                            dodge.width = 0.1, 
+                                            shape = 21,
+                                            size = 3,
+                                            cex = 1
+                  ) +
+                  stat_summary(
+                    geom = \"crossbar\",
+                    aes(fill = Class),
+                    fun = mean,
+                    position = position_dodge(0.9),
+                    colour = \"red\",
+                    linewidth = 0.5, width = 0.7,
+                    show.legend = FALSE
+                  ) +
+                  theme_prism(
+                    base_fontface = \"plain\", 
+                    base_line_size = 0.9, 
+                    base_family = \"Arial\"
+                  ) +
+                  scale_x_discrete(
+                    guide = guide_prism_bracket(width = 0.1),
+                    labels = scales::wrap_format(5)
+                  ) +
+                  theme(aspect.ratio = 1.0)
+          p"
+        ),
+        actionButton("runButton", "Run Code")
+      ),
+      column(
+        width = 6,
+        plotOutput("plot"),
+        downloadButton("downloadPlot", "Download Plot")
+      )
+    )
+  )
+)
+
+
+
+
+
+
+
+

app/data/ceramidepathway.cyjs

@@ -0,0 +1,2528 @@
+{
+  "format_version" : "1.0",
+  "generated_by" : "cytoscape-3.10.0",
+  "target_cytoscapejs_version" : "~2.1",
+  "data" : {
+    "shared_name" : "Network(1)",
+    "name" : "Network(1)",
+    "SUID" : 51687,
+    "__Annotations" : [ "" ],
+    "selected" : true
+  },
+  "elements" : {
+    "nodes" : [ {
+      "data" : {
+        "id" : "60361",
+        "Shape" : "Ellipse",
+        "Fillcolor" : "#ffffff",
+        "Color" : "",
+        "label" : " ",
+        "Label_size" : 1,
+        "shared_name" : "Node 18",
+        "LabelFont" : "",
+        "path" : "http://localhost:9000/plot?str1=Node1.svg",
+        "pathpng" : "http://localhost:9000/png?str2=(Node1).png",
+        "name" : "Node 18",
+        "Ensembl_ID" : "",
+        "SUID" : 60361,
+        "Height" : 1,
+        "Width" : 1,
+        "selected" : false
+      },
+      "position" : {
+        "x" : -772.3715110031136,
+        "y" : -442.24842702277897
+      },
+      "selected" : false
+    }, {
+      "data" : {
+        "id" : "59880",
+        "Shape" : "Ellipse",
+        "Fillcolor" : "#ffffff",
+        "Color" : "",
+        "label" : " ",
+        "Label_size" : 1,
+        "shared_name" : "Node 13",
+        "LabelFont" : "",
+        "path" : "http://localhost:9000/plot?str1=Node1.svg",
+        "pathpng" : "http://localhost:9000/png?str2=(Node1).png",
+        "name" : "Node 13",
+        "Ensembl_ID" : "",
+        "SUID" : 59880,
+        "Height" : 1,
+        "Width" : 1,
+        "selected" : false
+      },
+      "position" : {
+        "x" : -1180.4239308066317,
+        "y" : -358.0486505034973
+      },
+      "selected" : false
+    }, {
+      "data" : {
+        "id" : "59874",
+        "Shape" : "Ellipse",
+        "Fillcolor" : "#ffffff",
+        "Color" : "",
+        "label" : " ",
+        "Label_size" : 1,
+        "shared_name" : "Node 2",
+        "LabelFont" : "",
+        "path" : "http://localhost:9000/plot?str1=Node1.svg",
+        "pathpng" : "http://localhost:9000/png?str2=(Node1).png",
+        "name" : "Node 2",
+        "Ensembl_ID" : "",
+        "SUID" : 59874,
+        "Height" : 1,
+        "Width" : 1,
+        "selected" : false
+      },
+      "position" : {
+        "x" : -1182.4911756886006,
+        "y" : 111.7836490135568
+      },
+      "selected" : false
+    }, {
+      "data" : {
+        "id" : "59832",
+        "Shape" : "Ellipse",
+        "Fillcolor" : "#ffffff",
+        "Color" : "",
+        "label" : " ",
+        "Label_size" : 1,
+        "shared_name" : "Node 6",
+        "LabelFont" : "",
+        "path" : "http://localhost:9000/plot?str1=Node1.svg",
+        "pathpng" : "http://localhost:9000/png?str2=(Node1).png",
+        "name" : "Node 6",
+        "Ensembl_ID" : "",
+        "SUID" : 59832,
+        "Height" : 1,
+        "Width" : 1,
+        "selected" : false
+      },
+      "position" : {
+        "x" : -937.4993548517596,
+        "y" : -50.61982027825535
+      },
+      "selected" : false
+    }, {
+      "data" : {
+        "id" : "59829",
+        "Shape" : "Ellipse",
+        "Fillcolor" : "#ffffff",
+        "Color" : "",
+        "label" : " ",
+        "Label_size" : 1,
+        "shared_name" : "Node 4",
+        "LabelFont" : "",
+        "path" : "http://localhost:9000/plot?str1=Node1.svg",
+        "pathpng" : "http://localhost:9000/png?str2=(Node1).png",
+        "name" : "Node 4",
+        "Ensembl_ID" : "",
+        "SUID" : 59829,
+        "Height" : 1,
+        "Width" : 1,
+        "selected" : false
+      },
+      "position" : {
+        "x" : -937.6842371049761,
+        "y" : 59.53152187837176
+      },
+      "selected" : false
+    }, {
+      "data" : {
+        "id" : "59826",
+        "Shape" : "Ellipse",
+        "Fillcolor" : "#ffffff",
+        "Color" : "",
+        "label" : " ",
+        "Label_size" : 1,
+        "shared_name" : "Node 5",
+        "LabelFont" : "",
+        "path" : "http://localhost:9000/plot?str1=Node1.svg",
+        "pathpng" : "http://localhost:9000/png?str2=(Node1).png",
+        "name" : "Node 5",
+        "Ensembl_ID" : "",
+        "SUID" : 59826,
+        "Height" : 1,
+        "Width" : 1,
+        "selected" : false
+      },
+      "position" : {
+        "x" : -937.8691193581923,
+        "y" : 1.6323068569296595
+      },
+      "selected" : false
+    }, {
+      "data" : {
+        "id" : "59782",
+        "Shape" : "Ellipse",
+        "Fillcolor" : "#ffffff",
+        "Color" : "",
+        "label" : " ",
+        "Label_size" : 1,
+        "shared_name" : "Node 10",
+        "LabelFont" : "",
+        "path" : "http://localhost:9000/plot?str1=Node1.svg",
+        "pathpng" : "http://localhost:9000/png?str2=(Node1).png",
+        "name" : "Node 10",
+        "Ensembl_ID" : "",
+        "SUID" : 59782,
+        "Height" : 1,
+        "Width" : 1,
+        "selected" : false
+      },
+      "position" : {
+        "x" : -935.98675672944,
+        "y" : -244.3174653425818
+      },
+      "selected" : false
+    }, {
+      "data" : {
+        "id" : "59768",
+        "Shape" : "Ellipse",
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+        "target" : "51804",
+        "shared_name" : "B4galt5 (interacts with) Node 1",
+        "shared_interaction" : "interacts with",
+        "name" : "B4galt5 (interacts with) Node 1",
+        "interaction" : "interacts with",
+        "SUID" : 51926,
+        "selected" : false
+      },
+      "selected" : false
+    }, {
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+        "source" : "51794",
+        "target" : "51812",
+        "shared_name" : "Gal3st1 (interacts with) Node 1",
+        "shared_interaction" : "interacts with",
+        "name" : "Gal3st1 (interacts with) Node 1",
+        "interaction" : "interacts with",
+        "SUID" : 51924,
+        "selected" : false
+      },
+      "selected" : false
+    }, {
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+        "source" : "51794",
+        "target" : "51716",
+        "shared_name" : "Gal3st1 (interacts with) Node 1",
+        "shared_interaction" : "interacts with",
+        "name" : "Gal3st1 (interacts with) Node 1",
+        "interaction" : "interacts with",
+        "SUID" : 51922,
+        "selected" : false
+      },
+      "selected" : false
+    }, {
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+        "source" : "51792",
+        "target" : "51828",
+        "shared_name" : "Cer_AS (interacts with) HexCer_HS",
+        "shared_interaction" : "interacts with",
+        "name" : "Cer_AS (interacts with) HexCer_HS",
+        "interaction" : "interacts with",
+        "SUID" : 51920,
+        "selected" : false
+      },
+      "selected" : false
+    }, {
+      "data" : {
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+        "source" : "51788",
+        "target" : "51790",
+        "shared_name" : "UGT8a (interacts with) Node 1",
+        "shared_interaction" : "interacts with",
+        "name" : "UGT8a (interacts with) Node 1",
+        "interaction" : "interacts with",
+        "SUID" : 51918,
+        "selected" : false
+      },
+      "selected" : false
+    }, {
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+        "source" : "51788",
+        "target" : "51802",
+        "shared_name" : "UGT8a (interacts with) Node 1",
+        "shared_interaction" : "interacts with",
+        "name" : "UGT8a (interacts with) Node 1",
+        "interaction" : "interacts with",
+        "SUID" : 51914,
+        "selected" : false
+      },
+      "selected" : false
+    }, {
+      "data" : {
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+        "source" : "51780",
+        "target" : "51850",
+        "shared_name" : "GM3 (interacts with) GM2",
+        "shared_interaction" : "interacts with",
+        "name" : "GM3 (interacts with) GM2",
+        "interaction" : "interacts with",
+        "SUID" : 51912,
+        "selected" : false
+      },
+      "selected" : false
+    }, {
+      "data" : {
+        "id" : "51910",
+        "source" : "51776",
+        "target" : "51784",
+        "shared_name" : "Cer_NS (interacts with) Sph",
+        "shared_interaction" : "interacts with",
+        "name" : "Cer_NS (interacts with) Sph",
+        "interaction" : "interacts with",
+        "SUID" : 51910,
+        "selected" : false
+      },
+      "selected" : false
+    }, {
+      "data" : {
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+        "source" : "51776",
+        "target" : "51746",
+        "shared_name" : "Cer_NS (interacts with) HexCer_NS",
+        "shared_interaction" : "interacts with",
+        "name" : "Cer_NS (interacts with) HexCer_NS",
+        "interaction" : "interacts with",
+        "SUID" : 51908,
+        "selected" : false
+      },
+      "selected" : false
+    }, {
+      "data" : {
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+        "source" : "51776",
+        "target" : "51748",
+        "shared_name" : "Cer_NS (interacts with) SM",
+        "shared_interaction" : "interacts with",
+        "name" : "Cer_NS (interacts with) SM",
+        "interaction" : "interacts with",
+        "SUID" : 51906,
+        "selected" : false
+      },
+      "selected" : false
+    }, {
+      "data" : {
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+        "source" : "51772",
+        "target" : "51808",
+        "shared_name" : "SMS (interacts with) Node 1",
+        "shared_interaction" : "interacts with",
+        "name" : "SMS (interacts with) Node 1",
+        "interaction" : "interacts with",
+        "SUID" : 51904,
+        "selected" : false
+      },
+      "selected" : false
+    }, {
+      "data" : {
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+        "source" : "51768",
+        "target" : "51786",
+        "shared_name" : "DEGS2 (interacts with) Node 1",
+        "shared_interaction" : "interacts with",
+        "name" : "DEGS2 (interacts with) Node 1",
+        "interaction" : "interacts with",
+        "SUID" : 51902,
+        "selected" : false
+      },
+      "selected" : false
+    }, {
+      "data" : {
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+        "source" : "51764",
+        "target" : "51778",
+        "shared_name" : "DEGS1 (interacts with) Node 1",
+        "shared_interaction" : "interacts with",
+        "name" : "DEGS1 (interacts with) Node 1",
+        "interaction" : "interacts with",
+        "SUID" : 51898,
+        "selected" : false
+      },
+      "selected" : false
+    }, {
+      "data" : {
+        "id" : "51896",
+        "source" : "51764",
+        "target" : "51782",
+        "shared_name" : "DEGS1 (interacts with) Node 1",
+        "shared_interaction" : "interacts with",
+        "name" : "DEGS1 (interacts with) Node 1",
+        "interaction" : "interacts with",
+        "SUID" : 51896,
+        "selected" : false
+      },
+      "selected" : false
+    }, {
+      "data" : {
+        "id" : "51892",
+        "source" : "51756",
+        "target" : "51760",
+        "shared_name" : "FA2H (interacts with) Node 1",
+        "shared_interaction" : "interacts with",
+        "name" : "FA2H (interacts with) Node 1",
+        "interaction" : "interacts with",
+        "SUID" : 51892,
+        "selected" : false
+      },
+      "selected" : false
+    }, {
+      "data" : {
+        "id" : "59850",
+        "source" : "51754",
+        "target" : "59829",
+        "shared_name" : "2OH-FA (interacts with) Node 1",
+        "shared_interaction" : "interacts with",
+        "name" : "2OH-FA (interacts with) Node 1",
+        "interaction" : "interacts with",
+        "SUID" : 59850,
+        "selected" : false
+      },
+      "selected" : false
+    }, {
+      "data" : {
+        "id" : "51890",
+        "source" : "51752",
+        "target" : "51800",
+        "shared_name" : "Cer_AP (interacts with) HexCer_AP",
+        "shared_interaction" : "interacts with",
+        "name" : "Cer_AP (interacts with) HexCer_AP",
+        "interaction" : "interacts with",
+        "SUID" : 51890,
+        "selected" : false
+      },
+      "selected" : false
+    }, {
+      "data" : {
+        "id" : "51888",
+        "source" : "51750",
+        "target" : "51752",
+        "shared_name" : "Cer_ADS (interacts with) Cer_AP",
+        "shared_interaction" : "interacts with",
+        "name" : "Cer_ADS (interacts with) Cer_AP",
+        "interaction" : "interacts with",
+        "SUID" : 51888,
+        "selected" : false
+      },
+      "selected" : false
+    }, {
+      "data" : {
+        "id" : "51886",
+        "source" : "51750",
+        "target" : "51792",
+        "shared_name" : "Cer_ADS (interacts with) Cer_AS",
+        "shared_interaction" : "interacts with",
+        "name" : "Cer_ADS (interacts with) Cer_AS",
+        "interaction" : "interacts with",
+        "SUID" : 51886,
+        "selected" : false
+      },
+      "selected" : false
+    }, {
+      "data" : {
+        "id" : "51884",
+        "source" : "51746",
+        "target" : "51730",
+        "shared_name" : "HexCer_NS (interacts with) Hex2Cer",
+        "shared_interaction" : "interacts with",
+        "name" : "HexCer_NS (interacts with) Hex2Cer",
+        "interaction" : "interacts with",
+        "SUID" : 51884,
+        "selected" : false
+      },
+      "selected" : false
+    }, {
+      "data" : {
+        "id" : "51882",
+        "source" : "51746",
+        "target" : "51826",
+        "shared_name" : "HexCer_NS (interacts with) SHexCer",
+        "shared_interaction" : "interacts with",
+        "name" : "HexCer_NS (interacts with) SHexCer",
+        "interaction" : "interacts with",
+        "SUID" : 51882,
+        "selected" : false
+      },
+      "selected" : false
+    }, {
+      "data" : {
+        "id" : "51880",
+        "source" : "51437",
+        "target" : "51852",
+        "shared_name" : "B3galt4 (interacts with) Node 1",
+        "shared_interaction" : "interacts with",
+        "name" : "B3galt4 (interacts with) Node 1",
+        "interaction" : "interacts with",
+        "SUID" : 51880,
+        "selected" : false
+      },
+      "selected" : false
+    }, {
+      "data" : {
+        "id" : "51878",
+        "source" : "51435",
+        "target" : "51814",
+        "shared_name" : "B4galnt1 (interacts with) Node 1",
+        "shared_interaction" : "interacts with",
+        "name" : "B4galnt1 (interacts with) Node 1",
+        "interaction" : "interacts with",
+        "SUID" : 51878,
+        "selected" : false
+      },
+      "selected" : false
+    }, {
+      "data" : {
+        "id" : "59893",
+        "source" : "51738",
+        "target" : "59880",
+        "shared_name" : "FA (interacts with) Node 1",
+        "shared_interaction" : "interacts with",
+        "name" : "FA (interacts with) Node 1",
+        "interaction" : "interacts with",
+        "SUID" : 59893,
+        "selected" : false
+      },
+      "selected" : false
+    }, {
+      "data" : {
+        "id" : "59788",
+        "source" : "51738",
+        "target" : "59754",
+        "shared_name" : "FA (interacts with) Node 1",
+        "shared_interaction" : "interacts with",
+        "name" : "FA (interacts with) Node 1",
+        "interaction" : "interacts with",
+        "SUID" : 59788,
+        "selected" : false
+      },
+      "selected" : false
+    }, {
+      "data" : {
+        "id" : "51872",
+        "source" : "51730",
+        "target" : "51780",
+        "shared_name" : "Hex2Cer (interacts with) GM3",
+        "shared_interaction" : "interacts with",
+        "name" : "Hex2Cer (interacts with) GM3",
+        "interaction" : "interacts with",
+        "SUID" : 51872,
+        "selected" : false
+      },
+      "selected" : false
+    } ]
+  }
+}

app/data/demodata.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4f7b03ae39fb13252efa16890abb7bdf09a9d532cd22e71fb8369851d9062291
+size 3735341

app/data/lipidclassproperties.csv

@@ -0,0 +1,150 @@
+LbmClass,Categories,AcylChain,AlkylChain,SphingoChain,ExtraAcylChain,TotalChain
+FA,Fatty acyls,1,0,0,0,1
+FAHFA,Fatty acyls,1,0,0,1,1
+OxFA,Fatty acyls,1,0,0,0,1
+CAR,Fatty acyls,1,0,0,0,1
+NAE,Fatty acyls,1,0,0,0,1
+NAGly,Fatty acyls,1,0,0,1,1
+NAGlySer,Fatty acyls,1,0,0,1,1
+NAOrn,Fatty acyls,1,0,0,1,1
+NATau,Fatty acyls,1,0,0,1,1
+PA,Glycerophospholipids,2,0,0,0,2
+PC,Glycerophospholipids,2,0,0,0,2
+PE,Glycerophospholipids,2,0,0,0,2
+PG,Glycerophospholipids,2,0,0,0,2
+PI,Glycerophospholipids,2,0,0,0,2
+PS,Glycerophospholipids,2,0,0,0,2
+BMP,Glycerophospholipids,2,0,0,0,2
+HBMP,Glycerophospholipids,3,0,0,0,3
+EtherPC,Glycerophospholipids,1,1,0,0,2
+EtherPE,Glycerophospholipids,1,1,0,0,2
+EtherPI,Glycerophospholipids,1,1,0,0,2
+EtherPS,Glycerophospholipids,1,1,0,0,2
+EtherPG,Glycerophospholipids,1,1,0,0,2
+OxPC,Glycerophospholipids,2,0,0,0,2
+OxPS,Glycerophospholipids,2,0,0,0,2
+OxPE,Glycerophospholipids,2,0,0,0,2
+OxPG,Glycerophospholipids,2,0,0,0,2
+OxPI,Glycerophospholipids,2,0,0,0,2
+EtherOxPC,Glycerophospholipids,1,1,0,0,2
+EtherOxPE,Glycerophospholipids,1,1,0,0,2
+LPC,Glycerophospholipids,1,0,0,0,1
+LPE,Glycerophospholipids,1,0,0,0,1
+LPA,Glycerophospholipids,1,0,0,0,1
+LPS,Glycerophospholipids,1,0,0,0,1
+LPI,Glycerophospholipids,1,0,0,0,1
+LPG,Glycerophospholipids,1,0,0,0,1
+PMeOH,Glycerophospholipids,2,0,0,0,2
+PEtOH,Glycerophospholipids,2,0,0,0,2
+EtherLPC,Glycerophospholipids,0,1,0,0,1
+EtherLPE,Glycerophospholipids,0,1,0,0,1
+EtherLPG,Glycerophospholipids,0,1,0,0,1
+LNAPE,Glycerophospholipids,2,0,0,0,2
+LNAPS,Glycerophospholipids,2,0,0,0,2
+MLCL,Glycerophospholipids,3,0,0,0,3
+DLCL,Glycerophospholipids,2,0,0,0,2
+CL,Glycerophospholipids,4,0,0,0,4
+MG,Glycerolipids,1,0,0,0,1
+DG,Glycerolipids,2,0,0,0,2
+TG,Glycerolipids,3,0,0,0,3
+MGDG,Glycerolipids,2,0,0,0,2
+DGDG,Glycerolipids,2,0,0,0,2
+SQDG,Glycerolipids,2,0,0,0,2
+DGTS,Glycerolipids,2,0,0,0,2
+DGTA,Glycerolipids,2,0,0,0,2
+DGGA,Glycerolipids,2,0,0,0,2
+ADGGA,Glycerolipids,3,0,0,0,3
+LDGTS,Glycerolipids,1,0,0,0,1
+LDGTA,Glycerolipids,1,0,0,0,1
+LDGCC,Glycerolipids,1,0,0,0,1
+DGCC,Glycerolipids,2,0,0,0,2
+EtherMGDG,Glycerolipids,1,1,0,0,2
+EtherDGDG,Glycerolipids,1,1,0,0,2
+EtherTG,Glycerolipids,2,1,0,0,3
+EtherDG,Glycerolipids,1,1,0,0,2
+EtherSMGDG,Glycerolipids,1,1,0,0,2
+SMGDG,Glycerolipids,2,0,0,0,2
+Sph,Sphingolipids,1,0,0,0,1
+DHSph,Sphingolipids,1,0,0,0,1
+PhytoSph,Sphingolipids,1,0,0,0,1
+SM,Sphingolipids,2,0,0,0,2
+CerP,Sphingolipids,1,0,1,0,2
+Cer_ADS,Sphingolipids,1,0,1,0,2
+Cer_AS,Sphingolipids,1,0,1,0,2
+Cer_BDS,Sphingolipids,1,0,1,0,2
+Cer_BS,Sphingolipids,1,0,1,0,2
+Cer_EODS,Sphingolipids,1,0,1,1,2
+Cer_EOS,Sphingolipids,1,0,1,1,2
+Cer_NDS,Sphingolipids,1,0,1,0,2
+Cer_NS,Sphingolipids,1,0,1,0,2
+Cer_NP,Sphingolipids,1,0,1,0,2
+Cer_AP,Sphingolipids,1,0,1,0,2
+Cer_EBDS,Sphingolipids,1,0,1,1,2
+Cer_HS,Sphingolipids,1,0,1,0,2
+Cer_HDS,Sphingolipids,1,0,1,0,2
+HexCer_NS,Sphingolipids,1,0,1,0,2
+HexCer_NDS,Sphingolipids,1,0,1,0,2
+HexCer_AP,Sphingolipids,1,0,1,0,2
+HexCer_HS,Sphingolipids,1,0,1,0,2
+HexCer_HDS,Sphingolipids,1,0,1,0,2
+HexCer_EOS,Sphingolipids,1,0,1,1,2
+Hex2Cer,Sphingolipids,1,0,1,0,2
+Hex3Cer,Sphingolipids,1,0,1,0,2
+SHexCer,Sphingolipids,1,0,1,0,2
+GM3,Sphingolipids,1,0,1,0,2
+AHexCer,Sphingolipids,2,0,1,0,3
+ASM,Sphingolipids,1,0,1,1,8
+PE_Cer,Sphingolipids,2,0,0,0,2
+PI_Cer,Sphingolipids,2,0,0,0,2
+SL,Sphingolipids,2,0,0,0,2
+Ac2PIM1,Glycerophospholipids,2,0,0,0,2
+Ac2PIM2,Glycerophospholipids,2,0,0,0,2
+Ac3PIM2,Glycerophospholipids,3,0,0,0,2
+Ac4PIM2,Glycerophospholipids,4,0,0,0,2
+LipidA,Saccharolipids,4,0,0,2,4
+Vitamin_E,Prenol Lipids,0,0,0,0,0
+Vitamin_D,Sterol Lipids,0,0,0,0,0
+CoQ,Prenol Lipids,0,0,0,0,0
+CE,Sterol Lipids,1,0,0,0,1
+DCAE,Sterol Lipids,1,0,0,0,1
+GDCAE,Sterol Lipids,1,0,0,0,1
+GLCAE,Sterol Lipids,1,0,0,0,1
+TDCAE,Sterol Lipids,1,0,0,0,1
+TLCAE,Sterol Lipids,1,0,0,0,1
+BileAcid,Sterol Lipids,0,0,0,0,0
+VAE,Prenol Lipids,1,0,0,0,1
+BRSE,Sterol Lipids,1,0,0,0,1
+CASE,Sterol Lipids,1,0,0,0,1
+SISE,Sterol Lipids,1,0,0,0,1
+STSE,Sterol Lipids,1,0,0,0,1
+AHexBRS,Sterol Lipids,1,0,0,0,1
+AHexCAS,Sterol Lipids,1,0,0,0,1
+AHexCS,Sterol Lipids,1,0,0,0,1
+AHexSIS,Sterol Lipids,1,0,0,0,1
+AHexSTS,Sterol Lipids,1,0,0,0,1
+SHex,Sterol Lipids,0,0,0,0,0
+SSulfate,Sterol Lipids,0,0,0,0,0
+BASulfate,Sterol Lipids,0,0,0,0,0
+LCAE,Sterol Lipids,1,0,0,0,1
+KLCAE,Sterol Lipids,1,0,0,0,1
+KDCAE,Sterol Lipids,1,0,0,0,1
+MMPE,Glycerophospholipids,2,0,0,0,2
+DMPE,Glycerophospholipids,2,0,0,0,2
+MIPC,Unknown,2,0,0,0,2
+EGSE,Sterol Lipids,1,0,0,0,1
+DEGSE,Sterol Lipids,1,0,0,0,1
+DSMSE,Sterol Lipids,1,0,0,0,1
+OxTG,Glycerolipids,3,0,0,0,3
+TG_EST,Glycerolipids,3,0,0,1,3
+GPNAE,Glycerophospholipids,1,0,0,0,1
+DGMG,Glycerolipids,2,0,0,0,2
+MGMG,Glycerolipids,2,0,0,0,2
+GD1a,Sphingolipids,2,0,0,0,2
+GD1b,Sphingolipids,2,0,0,0,2
+GD2,Sphingolipids,2,0,0,0,2
+GD3,Sphingolipids,2,0,0,0,2
+GM1,Sphingolipids,2,0,0,0,2
+GQ1b,Sphingolipids,2,0,0,0,2
+GT1b,Sphingolipids,2,0,0,0,2
+NGcGM3,Sphingolipids,2,0,0,0,2
+ST,Sterol Lipids,0,0,0,0,0

app/data/nodestyle1.js

@@ -0,0 +1,36 @@
+[
+  {"selector":"node", "css": {
+    "border-width": "1px",
+    "content": "data(id)",
+	"background-color": "gray",
+	"background-fit": "cover cover",
+	"shape" : "rectangle",
+	"background-image": "data(path)",
+	"label" : "data(shared_name)",
+	"height": "data(Height)",
+	"z-index" : 1,
+	"width": "data(Width)",
+	"background-fit": "cover cover",
+	"font-size" : "data(Label_size)"
+  }
+  },
+  {"selector":"node:selected", "css": {
+	"background-color": "white",
+	"width": 700,
+	"height": 700,
+	"background-image": "data(path)",
+	"background-fit": "contain",
+	"z-index" : 4,
+	"font-size" : 50
+	
+  }
+  },
+  {"selector": "node[NumChildren<=1]", "css": {
+        "height": "10",
+	"width": "10"
+        }},
+  {"selector": "edge", "css": {
+    "line-color": "gray",
+	"label": "data(label)"
+  }}
+]

app/data/nodestyle2.js

@@ -0,0 +1,32 @@
+[
+  {"selector":"node", "css": {
+    "border-width": "1px",
+    "content": "data(id)",
+	"background-color": "data(color)",
+	"background-fit": "cover cover",
+	"shape" : "rectangle",
+	"background-image": "data(path)",
+	"label" : "data(shared_name)",
+	"height": "data(Height)",
+	"z-index" :  1,
+	"width": "data(Width)",
+	"background-fit": "cover cover",
+	"font-size" : "data(Label_size)"
+  }
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+      "selected" : false
+    }, {
+      "data" : {
+        "id" : "2790",
+        "source" : "2094",
+        "target" : "2469",
+        "shared_name" : "Node1 (interacts with) CL",
+        "name" : "Node1 (interacts with) CL",
+        "interaction" : "interacts with",
+        "SUID" : 2790,
+        "shared_interaction" : "interacts with",
+        "selected" : false
+      },
+      "selected" : false
+    }, {
+      "data" : {
+        "id" : "2788",
+        "source" : "2045",
+        "target" : "2043",
+        "shared_name" : "Group3 (interacts with) Node1",
+        "name" : "Group3 (interacts with) Node1",
+        "interaction" : "interacts with",
+        "SUID" : 2788,
+        "shared_interaction" : "interacts with",
+        "selected" : false
+      },
+      "selected" : false
+    }, {
+      "data" : {
+        "id" : "2786",
+        "source" : "2043",
+        "target" : "2502",
+        "shared_name" : "Node1 (interacts with) PG",
+        "name" : "Node1 (interacts with) PG",
+        "interaction" : "interacts with",
+        "SUID" : 2786,
+        "shared_interaction" : "interacts with",
+        "selected" : false
+      },
+      "selected" : false
+    }, {
+      "data" : {
+        "id" : "2784",
+        "source" : "1999",
+        "target" : "1997",
+        "shared_name" : "Group4 (interacts with) Node1",
+        "name" : "Group4 (interacts with) Node1",
+        "interaction" : "interacts with",
+        "SUID" : 2784,
+        "shared_interaction" : "interacts with",
+        "selected" : false
+      },
+      "selected" : false
+    }, {
+      "data" : {
+        "id" : "2782",
+        "source" : "1997",
+        "target" : "2535",
+        "shared_name" : "Node1 (interacts with) PI",
+        "name" : "Node1 (interacts with) PI",
+        "interaction" : "interacts with",
+        "SUID" : 2782,
+        "shared_interaction" : "interacts with",
+        "selected" : false
+      },
+      "selected" : false
+    }, {
+      "data" : {
+        "id" : "2780",
+        "source" : "1953",
+        "target" : "1951",
+        "shared_name" : "Group5 (interacts with) Node1",
+        "name" : "Group5 (interacts with) Node1",
+        "interaction" : "interacts with",
+        "SUID" : 2780,
+        "shared_interaction" : "interacts with",
+        "selected" : false
+      },
+      "selected" : false
+    }, {
+      "data" : {
+        "id" : "2778",
+        "source" : "1951",
+        "target" : "2568",
+        "shared_name" : "Node1 (interacts with) PS",
+        "name" : "Node1 (interacts with) PS",
+        "interaction" : "interacts with",
+        "SUID" : 2778,
+        "shared_interaction" : "interacts with",
+        "selected" : false
+      },
+      "selected" : false
+    }, {
+      "data" : {
+        "id" : "2776",
+        "source" : "1902",
+        "target" : "1900",
+        "shared_name" : "Group6 (interacts with) Node1",
+        "name" : "Group6 (interacts with) Node1",
+        "interaction" : "interacts with",
+        "SUID" : 2776,
+        "shared_interaction" : "interacts with",
+        "selected" : false
+      },
+      "selected" : false
+    }, {
+      "data" : {
+        "id" : "2774",
+        "source" : "1900",
+        "target" : "2304",
+        "shared_name" : "Node1 (interacts with) PE",
+        "name" : "Node1 (interacts with) PE",
+        "interaction" : "interacts with",
+        "SUID" : 2774,
+        "shared_interaction" : "interacts with",
+        "selected" : false
+      },
+      "selected" : false
+    }, {
+      "data" : {
+        "id" : "2772",
+        "source" : "1846",
+        "target" : "1844",
+        "shared_name" : "Group7 (interacts with) Node1",
+        "name" : "Group7 (interacts with) Node1",
+        "interaction" : "interacts with",
+        "SUID" : 2772,
+        "shared_interaction" : "interacts with",
+        "selected" : false
+      },
+      "selected" : false
+    }, {
+      "data" : {
+        "id" : "2770",
+        "source" : "1844",
+        "target" : "2238",
+        "shared_name" : "Node1 (interacts with) PC",
+        "name" : "Node1 (interacts with) PC",
+        "interaction" : "interacts with",
+        "SUID" : 2770,
+        "shared_interaction" : "interacts with",
+        "selected" : false
+      },
+      "selected" : false
+    }, {
+      "data" : {
+        "id" : "2768",
+        "source" : "1842",
+        "target" : "1840",
+        "shared_name" : "Node1 (interacts with) Node1",
+        "name" : "Node1 (interacts with) Node1",
+        "interaction" : "interacts with",
+        "SUID" : 2768,
+        "shared_interaction" : "interacts with",
+        "selected" : false
+      },
+      "selected" : false
+    } ]
+  }
+}

app/data/styles.xml

@@ -0,0 +1,142 @@
+<?xml version="1.0" encoding="UTF-8" standalone="yes"?>
+<vizmap id="VizMap-2024_10_23-17_45" documentVersion="3.1">
+    <visualStyle name="default_1_0">
+        <network>
+            <visualProperty default="" name="NETWORK_TITLE"/>
+            <visualProperty default="#FFFFFF" name="NETWORK_BACKGROUND_PAINT"/>
+            <visualProperty default="0.0" name="NETWORK_DEPTH"/>
+            <visualProperty default="550.0" name="NETWORK_WIDTH"/>
+            <visualProperty default="0.0" name="NETWORK_CENTER_X_LOCATION"/>
+            <visualProperty default="true" name="NETWORK_NODE_SELECTION"/>
+            <visualProperty default="0.0" name="NETWORK_CENTER_Y_LOCATION"/>
+            <visualProperty default="1.0" name="NETWORK_SCALE_FACTOR"/>
+            <visualProperty default="false" name="NETWORK_FORCE_HIGH_DETAIL"/>
+            <visualProperty default="550.0" name="NETWORK_SIZE"/>
+            <visualProperty default="false" name="NETWORK_NODE_LABEL_SELECTION"/>
+            <visualProperty default="400.0" name="NETWORK_HEIGHT"/>
+            <visualProperty default="false" name="NETWORK_ANNOTATION_SELECTION"/>
+            <visualProperty default="true" name="NETWORK_EDGE_SELECTION"/>
+            <visualProperty default="0.0" name="NETWORK_CENTER_Z_LOCATION"/>
+        </network>
+        <node>
+            <dependency value="true" name="nodeCustomGraphicsSizeSync"/>
+            <dependency value="false" name="nodeSizeLocked"/>
+            <visualProperty default="org.cytoscape.cg.model.NullCustomGraphics,0,[ Remove Graphics ]," name="NODE_CUSTOMGRAPHICS_2"/>
+            <visualProperty default="140.0" name="NODE_HEIGHT">
+                <passthroughMapping attributeName="Height" attributeType="float"/>
+            </visualProperty>
+            <visualProperty default="" name="NODE_TOOLTIP"/>
+            <visualProperty default="SOLID" name="NODE_BORDER_STROKE"/>
+            <visualProperty default="N,N,c,0.00,0.00" name="NODE_LABEL_POSITION"/>
+            <visualProperty default="DefaultVisualizableVisualProperty(id=NODE_CUSTOMPAINT_1, name=Node Custom Paint 1)" name="NODE_CUSTOMPAINT_1"/>
+            <visualProperty default="0.0" name="NODE_LABEL_ROTATION"/>
+            <visualProperty default="50.0" name="NODE_CUSTOMGRAPHICS_SIZE_9"/>
+            <visualProperty default="#1E90FF" name="NODE_PAINT"/>
+            <visualProperty default="35.0" name="NODE_SIZE"/>
+            <visualProperty default="DefaultVisualizableVisualProperty(id=NODE_CUSTOMPAINT_4, name=Node Custom Paint 4)" name="NODE_CUSTOMPAINT_4"/>
+            <visualProperty default="5.0" name="NODE_BORDER_WIDTH"/>
+            <visualProperty default="org.cytoscape.cg.model.NullCustomGraphics,0,[ Remove Graphics ]," name="NODE_CUSTOMGRAPHICS_3"/>
+            <visualProperty default="255" name="NODE_TRANSPARENCY"/>
+            <visualProperty default="true" name="NODE_NESTED_NETWORK_IMAGE_VISIBLE"/>
+            <visualProperty default="10.0" name="COMPOUND_NODE_PADDING"/>
+            <visualProperty default="" name="NODE_LABEL">
+                <passthroughMapping attributeName="label" attributeType="string"/>
+            </visualProperty>
+            <visualProperty default="DefaultVisualizableVisualProperty(id=NODE_CUSTOMPAINT_3, name=Node Custom Paint 3)" name="NODE_CUSTOMPAINT_3"/>
+            <visualProperty default="50.0" name="NODE_CUSTOMGRAPHICS_SIZE_4"/>
+            <visualProperty default="C,C,c,0.00,0.00" name="NODE_CUSTOMGRAPHICS_POSITION_3"/>
+            <visualProperty default="NONE" name="NODE_LABEL_BACKGROUND_SHAPE"/>
+            <visualProperty default="50.0" name="NODE_CUSTOMGRAPHICS_SIZE_8"/>
+            <visualProperty default="C,C,c,0.00,0.00" name="NODE_CUSTOMGRAPHICS_POSITION_9"/>
+            <visualProperty default="0.0" name="NODE_Y_LOCATION"/>
+            <visualProperty default="org.cytoscape.cg.model.NullCustomGraphics,0,[ Remove Graphics ]," name="NODE_CUSTOMGRAPHICS_5"/>
+            <visualProperty default="DefaultVisualizableVisualProperty(id=NODE_CUSTOMPAINT_5, name=Node Custom Paint 5)" name="NODE_CUSTOMPAINT_5"/>
+            <visualProperty default="C,C,c,0.00,0.00" name="NODE_CUSTOMGRAPHICS_POSITION_5"/>
+            <visualProperty default="#F7FCF5" name="NODE_FILL_COLOR"/>
+            <visualProperty default="org.cytoscape.cg.model.NullCustomGraphics,0,[ Remove Graphics ]," name="NODE_CUSTOMGRAPHICS_8"/>
+            <visualProperty default="50.0" name="NODE_CUSTOMGRAPHICS_SIZE_5"/>
+            <visualProperty default="#B6B6B6" name="NODE_LABEL_BACKGROUND_COLOR"/>
+            <visualProperty default="DefaultVisualizableVisualProperty(id=NODE_CUSTOMPAINT_2, name=Node Custom Paint 2)" name="NODE_CUSTOMPAINT_2"/>
+            <visualProperty default="140.0" name="NODE_WIDTH">
+                <passthroughMapping attributeName="Width" attributeType="float"/>
+            </visualProperty>
+            <visualProperty default="RECTANGLE" name="NODE_SHAPE"/>
+            <visualProperty default="255" name="NODE_LABEL_TRANSPARENCY"/>
+            <visualProperty default="DefaultVisualizableVisualProperty(id=NODE_CUSTOMPAINT_8, name=Node Custom Paint 8)" name="NODE_CUSTOMPAINT_8"/>
+            <visualProperty default="C,C,c,0.00,0.00" name="NODE_CUSTOMGRAPHICS_POSITION_1"/>
+            <visualProperty default="org.cytoscape.cg.model.NullCustomGraphics,0,[ Remove Graphics ]," name="NODE_CUSTOMGRAPHICS_4"/>
+            <visualProperty default="255" name="NODE_BORDER_TRANSPARENCY"/>
+            <visualProperty default="#000000" name="NODE_LABEL_COLOR"/>
+            <visualProperty default="DefaultVisualizableVisualProperty(id=NODE_CUSTOMPAINT_7, name=Node Custom Paint 7)" name="NODE_CUSTOMPAINT_7"/>
+            <visualProperty default="200.0" name="NODE_LABEL_WIDTH"/>
+            <visualProperty default="C,C,c,0.00,0.00" name="NODE_CUSTOMGRAPHICS_POSITION_6"/>
+            <visualProperty default="50.0" name="NODE_CUSTOMGRAPHICS_SIZE_7"/>
+            <visualProperty default="50.0" name="NODE_CUSTOMGRAPHICS_SIZE_1"/>
+            <visualProperty default="#FFFF00" name="NODE_SELECTED_PAINT"/>
+            <visualProperty default="#252525" name="NODE_BORDER_PAINT"/>
+            <visualProperty default="ROUND_RECTANGLE" name="COMPOUND_NODE_SHAPE"/>
+            <visualProperty default="50.0" name="NODE_CUSTOMGRAPHICS_SIZE_6"/>
+            <visualProperty default="SansSerif.plain,plain,12" name="NODE_LABEL_FONT_FACE"/>
+            <visualProperty default="true" name="NODE_VISIBLE"/>
+            <visualProperty default="0.0" name="NODE_Z_LOCATION"/>
+            <visualProperty default="C,C,c,0.00,0.00" name="NODE_CUSTOMGRAPHICS_POSITION_2"/>
+            <visualProperty default="0.0" name="NODE_DEPTH"/>
+            <visualProperty default="0.0" name="NODE_X_LOCATION"/>
+            <visualProperty default="DefaultVisualizableVisualProperty(id=NODE_CUSTOMPAINT_9, name=Node Custom Paint 9)" name="NODE_CUSTOMPAINT_9"/>
+            <visualProperty default="50.0" name="NODE_CUSTOMGRAPHICS_SIZE_2"/>
+            <visualProperty default="C,C,c,0.00,0.00" name="NODE_CUSTOMGRAPHICS_POSITION_4"/>
+            <visualProperty default="org.cytoscape.cg.model.NullCustomGraphics,0,[ Remove Graphics ]," name="NODE_CUSTOMGRAPHICS_7"/>
+            <visualProperty default="50.0" name="NODE_CUSTOMGRAPHICS_SIZE_3"/>
+            <visualProperty default="255" name="NODE_LABEL_BACKGROUND_TRANSPARENCY"/>
+            <visualProperty default="org.cytoscape.cg.model.NullCustomGraphics,0,[ Remove Graphics ]," name="NODE_CUSTOMGRAPHICS_9"/>
+            <visualProperty default="org.cytoscape.cg.model.NullCustomGraphics,0,[ Remove Graphics ]," name="NODE_CUSTOMGRAPHICS_1">
+                <passthroughMapping attributeName="path" attributeType="string"/>
+            </visualProperty>
+            <visualProperty default="org.cytoscape.cg.model.NullCustomGraphics,0,[ Remove Graphics ]," name="NODE_CUSTOMGRAPHICS_6"/>
+            <visualProperty default="C,C,c,0.00,0.00" name="NODE_CUSTOMGRAPHICS_POSITION_8"/>
+            <visualProperty default="15" name="NODE_LABEL_FONT_SIZE"/>
+            <visualProperty default="DefaultVisualizableVisualProperty(id=NODE_CUSTOMPAINT_6, name=Node Custom Paint 6)" name="NODE_CUSTOMPAINT_6"/>
+            <visualProperty default="false" name="NODE_SELECTED"/>
+            <visualProperty default="C,C,c,0.00,0.00" name="NODE_CUSTOMGRAPHICS_POSITION_7"/>
+        </node>
+        <edge>
+            <dependency value="false" name="arrowColorMatchesEdge"/>
+            <visualProperty default="NONE" name="EDGE_SOURCE_ARROW_SHAPE"/>
+            <visualProperty default="false" name="EDGE_LABEL_AUTOROTATE"/>
+            <visualProperty default="#848484" name="EDGE_STROKE_UNSELECTED_PAINT"/>
+            <visualProperty default="#FF0000" name="EDGE_SELECTED_PAINT"/>
+            <visualProperty default="10" name="EDGE_LABEL_FONT_SIZE"/>
+            <visualProperty default="#B6B6B6" name="EDGE_LABEL_BACKGROUND_COLOR"/>
+            <visualProperty default="2.0" name="EDGE_WIDTH"/>
+            <visualProperty default="0.0" name="EDGE_Z_ORDER"/>
+            <visualProperty default="#000000" name="EDGE_SOURCE_ARROW_UNSELECTED_PAINT"/>
+            <visualProperty default="6.0" name="EDGE_TARGET_ARROW_SIZE"/>
+            <visualProperty default="" name="EDGE_LABEL"/>
+            <visualProperty default="200.0" name="EDGE_LABEL_WIDTH"/>
+            <visualProperty default="Dialog.plain,plain,10" name="EDGE_LABEL_FONT_FACE"/>
+            <visualProperty default="C,C,c,0.00,0.00" name="EDGE_LABEL_POSITION"/>
+            <visualProperty default="#000000" name="EDGE_TARGET_ARROW_UNSELECTED_PAINT"/>
+            <visualProperty default="#323232" name="EDGE_PAINT"/>
+            <visualProperty default="" name="EDGE_BEND"/>
+            <visualProperty default="0.0" name="EDGE_LABEL_ROTATION"/>
+            <visualProperty default="true" name="EDGE_VISIBLE"/>
+            <visualProperty default="" name="EDGE_TOOLTIP"/>
+            <visualProperty default="6.0" name="EDGE_SOURCE_ARROW_SIZE"/>
+            <visualProperty default="NONE" name="EDGE_TARGET_ARROW_SHAPE"/>
+            <visualProperty default="0.5" name="EDGE_STACKING_DENSITY"/>
+            <visualProperty default="255" name="EDGE_LABEL_BACKGROUND_TRANSPARENCY"/>
+            <visualProperty default="255" name="EDGE_LABEL_TRANSPARENCY"/>
+            <visualProperty default="true" name="EDGE_CURVED"/>
+            <visualProperty default="#FF0000" name="EDGE_STROKE_SELECTED_PAINT"/>
+            <visualProperty default="false" name="EDGE_SELECTED"/>
+            <visualProperty default="SOLID" name="EDGE_LINE_TYPE"/>
+            <visualProperty default="#404040" name="EDGE_UNSELECTED_PAINT"/>
+            <visualProperty default="AUTO_BEND" name="EDGE_STACKING"/>
+            <visualProperty default="#000000" name="EDGE_LABEL_COLOR"/>
+            <visualProperty default="#FFFF00" name="EDGE_TARGET_ARROW_SELECTED_PAINT"/>
+            <visualProperty default="#FFFF00" name="EDGE_SOURCE_ARROW_SELECTED_PAINT"/>
+            <visualProperty default="255" name="EDGE_TRANSPARENCY"/>
+            <visualProperty default="NONE" name="EDGE_LABEL_BACKGROUND_SHAPE"/>
+        </edge>
+    </visualStyle>
+</vizmap>

app/plumber.R

@@ -0,0 +1,48 @@
+
+library(plumber)
+
+
+# Title
+#* @apiTitle resr4cyjs
+# Description
+#* @apiDescription This is a resr4cyjs API.
+# Version
+#* @apiVersion 0.0.1
+# Tag Description
+#* @apiTag resr4cyjs "resr4cyjs API"
+
+
+#* @filter cors
+cors <- function(req, res) {
+  res$setHeader("Access-Control-Allow-Origin", "*")
+  if (req$REQUEST_METHOD == "OPTIONS") {
+   res$setHeader("Access-Control-Allow-Methods", "GET")
+    res$setHeader(
+      "Access-Control-Allow-Headers",
+      req$HTTP_ACCESS_CONTROL_REQUEST_HEADERS
+    )
+   res$status <- 200
+   return(list())
+  } else {
+    plumber::forward()
+  }
+}
+
+
+
+#* @serializer contentType list(type="image/svg+xml")
+#* @get /plot
+#* @arg str1
+function(str1) {
+  thefolder <- "/srv/app/svg/"
+  svg_file_path <- paste(thefolder,str1,sep = "")
+  if(file.exists(svg_file_path) == TRUE){
+  readBin(svg_file_path, "raw", file.info(svg_file_path)$size)}
+  else{
+    svg_file_path <- "/srv/app/www/background.svg"
+    readBin(svg_file_path, "raw", file.info(svg_file_path)$size)
+  }
+}
+
+
+

app/server.R

@@ -0,0 +1,696 @@
+validate_dataframe <- function(df, validation_rules = c("structure", "peak_headers", "metadata")
+) {
+  expected_headers <- c(
+    "Alignment ID", "Average Rt(min)", "Average Mz", "Metabolite name",
+    "Adduct type", "Post curation result", "Fill %", "MS/MS assigned",
+    "Reference RT", "Reference m/z", "Formula", "Ontology", "INCHIKEY",
+    "SMILES", "Annotation tag (VS1.0)", "RT matched", "m/z matched",
+    "MS/MS matched", "Comment", "Manually modified for quantification",
+    "Manually modified for annotation", "Isotope tracking parent ID", 
+    "Isotope tracking weight number", "RT similarity", "m/z similarity",
+    "Simple dot product", "Weighted dot product", "Reverse dot product",
+    "Matched peaks count", "Matched peaks percentage", "Total score",
+    "S/N average", "Spectrum reference file name", "MS1 isotopic spectrum",
+    "MS/MS spectrum"
+  )
+  # Input check
+  if (!is.data.frame(df)) {
+    stop("Input must be a dataframe")
+  }
+  
+  if (!is.character(validation_rules)) {
+    stop("validation_rules must be a character vector")
+  }
+  
+  if (!all(validation_rules %in% c("structure", "peak_headers", "metadata"))) {
+    stop("Invalid validation_rules specified")
+  }
+  
+  validation_results <- list()
+  
+  # Check data structure  
+  if ("structure" %in% validation_rules) {
+    if (nrow(df) < 5) {
+      validation_results$insufficient_rows <- 
+        "Data has less than 5 rows. At least 5 rows are required."
+    }
+    if (ncol(df) < 34) {
+      validation_results$insufficient_cols <- 
+        "Missing 34 columns required for peak information."
+    }
+  }
+  
+  # Check peak information headers (row 5)
+  if ("peak_headers" %in% validation_rules) {
+    if (is.null(expected_headers)) {
+      stop("expected_headers required for peak_headers validation")
+    }
+    
+    if (nrow(df) >= 5) {
+      headers <- df[5, 1:min(34, ncol(df))]
+      missing_headers <- expected_headers[1:length(headers)][headers != expected_headers[1:length(headers)]]
+      
+      if (length(missing_headers) > 0) {
+        validation_results$incorrect_headers <- 
+          paste("Headers in row 5 differ from expected headers:",
+                paste(missing_headers, collapse = ", "))
+      }
+    }
+  }
+  
+  # Check metadata (rows 1-2, columns 35+)
+  if ("metadata" %in% validation_rules && ncol(df) > 34) {
+    # Check for Sample in columns 36+
+    if (ncol(df) > 35) {
+      file_types <- df[2, 36:ncol(df)]
+      if (!"Sample" %in% file_types) {
+        validation_results$no_sample <- 
+          "'Sample' not found in columns 36 onward"
+      }
+    }
+  }
+  
+  return(validation_results)
+}
+
+
+
+
+server <- function(input, output,session) {
+  options(shiny.maxRequestSize=150*1024^100)
+  originaldir <- reactiveValues(datapath = getwd()) # directry of shiny R script
+  global <- reactiveValues(datapath = getwd()) # directory of file path in lipidomics tab
+  col = reactiveValues(col = col)
+  validation_status <- reactiveVal(FALSE)
+  svg_path <- paste(getwd(),"/svg",sep = "")
+  host <- session$request$HTTP_HOST　%>% 
+  gsub("http:", "https:", .)
+   output$downloadData <- downloadHandler(
+     filename = function() {
+       "Demo_data.zip"  # ダウンロード時のファイル名
+     },
+     content = function(file) {
+       file.copy("./pathwaymap/demodata.zip", file)
+    },
+    contentType = "application/zip"
+    )
+    observeEvent(input$filetype,{
+    if(input$filetype =="Sample in rows"){
+      shinyjs::show("ontfile")
+    }else{
+      shinyjs::hide("ontfile")
+    }
+	if(input$ClassorMol =="TRUE"){
+      shinyjs::show("X1")
+	  shinyjs::show("X2")
+    }else{
+      shinyjs::hide("X1")
+	  shinyjs::hide("X2")
+    }
+	})
+	
+	observeEvent(input$ClassorMol,{
+	if(input$ClassorMol =="TRUE"){
+      shinyjs::show("X1")
+	  shinyjs::show("X2")
+    }else{
+      shinyjs::hide("X1")
+	  shinyjs::hide("X2")
+    }
+	})
+	
+ observeEvent(input$file1, {	
+ tryCatch({
+    originaldata <- read.csv(input$file1$datapath, header = F, check.names = F, fileEncoding = "UTF-8-BOM")
+  }, error = function(e) {
+    showNotification(paste("Failed to load the file", e$message), type = "error")
+  })
+  if(input$filetype == "Sample in rows"){
+   
+  }else if(input$filetype == "MS-DIAL export"){
+  validateresults <- validate_dataframe(originaldata, validation_rules = c("structure", "peak_headers", "metadata"))
+      if (length(validateresults) == 0) {
+      validation_status(TRUE)
+      shinyalert(
+        "Success",
+        "All validation checks passed successfully!",
+        type = "success"
+      )
+    } else {
+      validation_status(FALSE)
+error_message <- "<div style='text-align: left;'><strong>Validation Errors:</strong><br><br>"
+    
+    # 各エラーメッセージの処理
+    for (error_name in names(validateresults)) {
+      error_message <- paste0(
+        error_message,
+        "<span style='color: #dc3545;'>• ",  # 赤色のブレット
+        switch(error_name,
+               "insufficient_cols" = "<strong>Column Error:</strong> ",
+               "incorrect_headers" = "<strong>Header Error:</strong> ",
+               "no_sample" = "<strong>Sample Error:</strong> "),
+        validateresults[[error_name]],
+        "</span><br><br>"
+      )
+    }
+    error_message <- paste0(error_message, "</div>")
+    
+    # エラーメッセージの表示
+    shinyalert(
+      "Validation Failed",
+      html = TRUE,
+      text = error_message,
+      type = "error",
+      size = "l",
+      closeOnEsc = TRUE,
+      closeOnClickOutside = TRUE,
+      showConfirmButton = TRUE,
+      confirmButtonText = "OK",
+      timer = FALSE
+    )
+
+    }
+  }
+	observeEvent(input$submit,{
+  tryCatch({
+  if(input$filetype == "Sample in rows"){
+    
+    Lipidomedata <- processSampleInRows(originaldata, session, input)[[1]]
+    lipidont <- read.csv(input$ontfile$datapath, sep = ",", check.names = FALSE)
+    metadata <- processSampleInRows(originaldata, session, input)[[2]]
+    alitable <- read.csv(input$ontfile$datapath, check.names = FALSE, fileEncoding = "UTF-8-BOM")
+    colnames(alitable) <- c("Metabolite name","Ontology")
+    letchoice <- c(colnames(Lipidomedata)[!colnames(Lipidomedata) %in% colnames(metadata)],"Others")
+    moldata <- processSampleInRowstomoldata(originaldata, session, input)[[1]]
+  }else if(input$filetype == "MS-DIAL export"){
+  
+  Lipidomedata <- processMSDIALExport(originaldata, session, input)[[1]]
+  metadata <- processMSDIALExport(originaldata, session, input)[[2]]
+  alitable <- process_alignment_file(originaldata)[[1]] %>% select(c(1,2))
+  moldata <- processMSDIALExporttomoldata(originaldata, session, input)[[1]] 
+  }
+  if(length(input$transcriptomefile) !=0){
+    transcriptome <- read.csv(input$transcriptomefile$datapath,check.names = F,fileEncoding ="UTF-8-BOM") %>% t()
+    colnames(transcriptome) <- transcriptome[1,]
+    transcriptome <- transcriptome[-1,] %>% data.frame()
+    transcriptome[,-c(1)] <- apply(transcriptome[,-c(1)],2,as.numeric) %>% data.frame()
+    transcriptome <-  rownames_to_column(transcriptome,"name")
+    transcriptome[,2] <- as.character(transcriptome[,2])
+  }
+  
+  if(length(input$file1) !=0 & length(input$transcriptomefile) != 0){
+    
+    Ensembl <- gconvert(query = colnames(transcriptome)[-c(1:2)] , organism = "mmusculus",#sapiens
+                       target="ENSG", mthreshold = Inf, filter_na = TRUE) %>% select(input,target)
+    transcriptome <- transcriptome %>% select(-2)
+    data <- inner_join(Lipidomedata,transcriptome,by =c("name"))
+	moldata <- inner_join(moldata,transcriptome,by =c("name"))
+  }
+  else{
+    data <- Lipidomedata
+  }
+  }, error = function(e) {
+    showNotification(paste("Error:Invalid CSV file format. Please ensure your file is a properly formatted CSV.", e$message), type = "error")
+  })
+  
+  metainfocol <- ncol(metadata)
+  lipidclassproperties <- read_csv("./pathwaymap/lipidclassproperties.csv")
+  
+  processAndUpdateInputs(data, session, metadata, metainfocol)
+  processAndUpdateInputs2(moldata, session, metadata, metainfocol)
+  shinyalert(
+        "Success",
+        "GO to Plot tab",
+        type = "success"
+      )
+  observeEvent(input$y,{
+    targetclass <- filter(alitable,Ontology %in% input$y)
+    shiny::updateSelectInput(session, "mol", selected = targetclass$`Metabolite name`[1], choices = targetclass$`Metabolite name`)
+  })
+
+  #observeEvent(input$Colorpicker,{
+  #print("boton")
+  #  ShowtestModaldialog4(data,input,col$col)
+  #}
+  #)
+  observeEvent(input$exportgraph,{
+    ShowtestModaldialog2(data,input,col$col)
+  }
+  )
+  observeEvent(input$Colorpicker, {
+    ShowtestModaldialog(data,input,col$col)
+  })
+  
+    observeEvent(input$saveConfirm, {
+    removeModal()
+    showNotification("保存が完了しました", type = "success")
+  })
+
+  observeEvent(input$w,{
+    updateOrderInput(session, "levels",
+                     items = c(unique(as.matrix(select(data,input$w)))),
+                     item_class = "success")
+    shiny::updateSelectInput(session, "q", selected = c(unique(as.matrix(select(data,input$w))))[1], choices = c(unique(as.matrix(select(data,input$w)))))
+    col$col <<- rainbow_hcl(length(unique(as.matrix(select(data,input$w)))))
+    col$col <<- setNames(col$col,unique(as.matrix(select(data,input$w))))
+  })
+  
+  observeEvent(input$levels,{
+    if (!is.null(input$levels) && any(input$levels != "")) {
+      dataa <<- mutate(data, !!as.symbol(input$w) := !!as.symbol(input$w) %>% factor(levels = input$levels))
+      moldataa <<- mutate(moldata, !!as.symbol(input$w) := !!as.symbol(input$w) %>% factor(levels = input$levels))
+      
+    }
+  })
+  
+  observe({
+    if (length(grep("selectcolor", names(input), value = TRUE)) != 0) {
+      col$col <<-  process_select_color_input(input,data)
+    }
+  })
+  
+    # 可能な比較の組み合わせを生成
+  possible_comparisons <- reactive({
+    groups <- unique(data[[input$w]])
+    combs <- combn(groups, 2, simplify = FALSE)
+    setNames(
+      lapply(combs, function(x) paste(x, collapse = "vs")),
+      lapply(combs, function(x) paste(x, collapse = " vs "))
+    )
+  })
+  
+  # 比較選択用のUI生成
+  output$comparison_choices <- renderUI({
+    checkboxGroupInput("comparisons", "Select comparisons:",
+                      choices = possible_comparisons(),
+                      selected = possible_comparisons()[1])
+  })
+  
+  # Select All ボタンの処理
+  observeEvent(input$select_all, {
+    updateCheckboxGroupInput(session, "comparisons",
+                           selected = possible_comparisons())
+  })
+  
+  # Clear All ボタンの処理
+  observeEvent(input$clear_all, {
+    updateCheckboxGroupInput(session, "comparisons",
+                           selected = character(0))
+  })
+  
+  # 統計検定の実行
+  stat_test <- reactive({
+    req(input$comparisons, input$test_method)
+    
+    # 検定関数の選択
+    test_func <- get(input$test_method, asNamespace("rstatix"))
+    
+    # 結果を格納するデータフレーム
+    results <- data.frame()
+    
+    for(comp in input$comparisons) {
+      # グループを分割
+      groups <- strsplit(comp, "vs")[[1]]
+      
+      # データのサブセット作成
+      subset_data <- data %>%
+        dplyr::filter(!!sym(input$w) %in% groups)
+      
+      # 検定実行
+      test_result <- test_func(subset_data, as.formula(paste(input$y, "~", input$w))) %>%
+        adjust_pvalue(method = input$p_adjust) %>%
+        add_significance()
+      
+      results <- rbind(results, test_result)
+    }
+    
+    results
+  })
+
+  stat_testmol <- reactive({
+    req(input$comparisons, input$test_method)
+    
+    # 検定関数の選択
+    test_func <- get(input$test_method, asNamespace("rstatix"))
+    
+    # 結果を格納するデータフレーム
+    results <- data.frame()
+    
+    for(comp in input$comparisons) {
+      # グループを分割
+      groups <- strsplit(comp, "vs")[[1]]
+      
+      # データのサブセット作成
+      subset_data <- moldata %>%
+        dplyr::filter(!!sym(input$w) %in% groups)
+      
+      # 検定実行
+      test_result <- test_func(subset_data, as.formula(paste(paste("`",input$mol,"`",sep =""), "~", input$w))) %>%
+        adjust_pvalue(method = input$p_adjust) %>%
+        add_significance()
+      
+      results <- rbind(results, test_result)
+    }
+    
+    results
+  })
+  
+#if(input$pvaluecheck == TRUE){
+  # サマリー統計の表示
+
+   output$stat_results <- renderPrint({
+    req(stat_test())
+    results <- stat_test() 
+    cat("Results:\n")
+    print(summary(results))
+  })
+#}  
+  observeEvent(input$levels,{
+    observe({
+      if (!is.null(input$mol) && input$mol != "") {
+        if(!is.null(input$levels) && any(input$levels != "")){
+          
+          output$plottest <- renderPlot({
+            
+            if(input$mydrop == "box"){
+              process_boxplot(input,input$y,dataa,col$col,stat_test())
+            }
+            else if(input$mydrop == "bar"){
+              process_barplot(input,input$y,dataa,col$col,stat_test())
+            }
+            else if(input$mydrop == "violin"){
+              process_violinplot(input,input$y,dataa,col$col,stat_test())
+            }
+            else if(input$mydrop == "polar"){
+              process_polarplot(input,input$y,dataa,col$col,stat_test())
+            }
+            else if(input$mydrop == "coding"){
+              process_dotplot(input,input$y,dataa,col$col,stat_test())
+            }
+          }
+          )
+          output$mappingraph <- renderPlot({
+            
+            if(input$mydrop == "box"){
+              process_boxplot_diagram(input,input$y,dataa,col$col,stat_test())
+            }
+            else if(input$mydrop == "bar"){
+              process_barplot_diagram(input,input$y,dataa,col$col,stat_test())
+            }
+            else if(input$mydrop == "violin"){
+              process_violinplot_diagram(input,input$y,dataa,col$col,stat_test())
+            }
+            else if(input$mydrop == "polar"){
+              process_polar_diagram(input,input$y,dataa,col$col,stat_test())
+            }
+            else if(input$mydrop == "coding"){
+              process_dotplot_diagram(input,input$y,dataa,col$col,stat_test())
+            }
+          }
+          )
+          
+          observeEvent(input$mol,{
+            
+            output$plottest2 <- renderPlot({
+              if(input$mydrop == "box"){
+                process_boxplot(input, paste("`",input$mol,"`",sep =""),moldataa,col$col,stat_testmol())
+              }
+              else if(input$mydrop == "bar"){
+                process_barplot(input, paste("`",input$mol,"`",sep =""),moldataa,col$col,stat_testmol())
+              }
+              else if(input$mydrop == "violin"){
+                process_violinplot(input,paste("`",input$mol,"`",sep =""),moldataa,col$col,stat_testmol())
+              }
+              else if(input$mydrop == "polar"){
+                process_polarplot(input,paste("`",input$mol,"`",sep =""),moldataa,col$col,stat_testmol())
+              }
+              else if(input$mydrop == "coding"){
+                process_dotplot(input,paste("`",input$mol,"`",sep =""),moldataa,col$col,stat_testmol())
+              }
+              
+            })
+          })
+		   }
+      }
+    })
+  })
+  
+  graph_json <- reactive({
+    if (input$pathwaytype == "Global pathway") {
+      read_graph_json("./pathwaymap/globalpathway.cyjs") %>% gsub("localhost:9000", paste0(host,"/api"), .)
+    } else if (input$pathwaytype == "Ceramide pathway") {
+      read_graph_json("./pathwaymap/ceramidepathway.cyjs") %>% gsub("localhost:9000", paste0(host,"/api"), .)
+    } else if (input$pathwaytype == "Remodeling pathway") {
+      read_graph_json("./pathwaymap/remodeling.cyjs") %>% gsub("localhost:9000", paste0(host,"/api"), .)
+    }
+  })
+  
+  style_file_path <- "./pathwaymap/nodestyle1.js"
+  styles_xml_path <- "./pathwaymap/styles.xml"
+  # CYJSファイルのダウンロード
+  output$exportCYJS <- downloadHandler(
+    filename = function() {
+      paste0(input$pathwaytype, "_", format(Sys.Date(), "%Y%m%d"), ".cyjs")
+    },
+    content = function(file) {
+      writeLines(graph_json, file)
+    },
+    contentType = "application/json"
+  )
+  
+  # styles.xmlファイルのダウンロード
+  output$exportStyles <- downloadHandler(
+    filename = function() {
+      "styles.xml"
+    },
+    content = function(file) {
+      file.copy(styles_xml_path, file)
+    },
+    contentType = "application/xml"
+  )
+  #observeEvent(input$sidebarCollapse, {
+  #  toggleClass(id = "content", class = "active")
+  #})
+  
+   observeEvent(input$toggle_sidebar, {
+    shinyjs::runjs("toggleSidebar()")
+  })
+  observeEvent(input$actionplottest,{
+  observeEvent(input$pathway, {
+    output$graphContainer <- renderUI({
+      cyjShinyOutput("cyjShinytest", height = "90%", width = "90%")
+    })
+  })
+          output$cyjShinytest <- renderCyjShiny({
+            #p <- input$pathway           
+            #if (p != 0) {
+              if (input$pathwaytype == "Global pathway") {
+                graph_json <<- paste(readLines("./pathwaymap/globalpathway.cyjs"), collapse = "") %>% gsub("localhost:9000", paste0(host,"/api"), .)
+              } else if (input$pathwaytype == "Ceramide pathway") {
+                graph_json <<- paste(readLines("./pathwaymap/ceramidepathway.cyjs"), collapse = "") %>% gsub("localhost:9000", paste0(host,"/api"), .)
+              } else if (input$pathwaytype == "Remodeling pathway") {
+                graph_json <<- paste(readLines("./pathwaymap/remodeling.cyjs"), collapse = "") %>% gsub("localhost:9000", paste0(host,"/api"), .)
+              }
+              
+              test <- fromJSON(graph_json)
+              test <- as.data.frame(test$elements$nodes)
+              
+              if (input$viewacyllevel == TRUE) {
+                cyjShiny(graph_json, layoutName = "preset", styleFile = "./pathwaymap/nodestyle1.js")
+              } else {
+                cyjShiny(graph_json, layoutName = "preset", styleFile = "./pathwaymap/nodestyle2.js")
+              }
+           # } else {
+              
+            #}
+          })
+		  
+		  
+		  
+          observeEvent(input$getSelectedNodes, ignoreInit=TRUE, {
+            output$selectedNodesDisplay <- renderText({" "})
+            getSelectedNodes(session)
+          })
+          
+          
+          output$corselect <- renderPlotly({
+            if(input$pathwaytype == "Global pathway"){
+              graph_json <<- paste(readLines("./pathwaymap/globalpathway.cyjs"), collapse="") %>% gsub("localhost:9000", paste0(host,"/api"), .)
+            }else if(input$pathwaytype == "Ceramide pathway"){
+              graph_json <<- paste(readLines("./pathwaymap/ceramidepathway.cyjs"), collapse="") %>% gsub("localhost:9000", paste0(host,"/api"), .)
+            }else if(input$pathwaytype == "Remodeling pathway"){
+              graph_json <<- paste(readLines("./pathwaymap/remodeling.cyjs"), collapse="")　%>% gsub("localhost:9000", paste0(host,"/api"), .)
+            }
+            test <- fromJSON(graph_json)
+            test <- as.data.frame(test$elements$nodes)
+            test1 <- test[test$data$id %in% unlist(input$selectedNodes),]
+            dataa <- mutate(data, !!as.symbol(input$w) := !!as.symbol(input$w) %>% factor(levels = input$levels))
+            
+            if(length(test1$data$shared_name) == 2){
+            moldataa <- mutate(moldata, !!as.symbol(input$w) := !!as.symbol(input$w) %>% factor(levels = input$levels))
+            targetclass <- filter(alitable,Ontology %in% test1$data$shared_name)
+              if(! test1$data$shared_name[1] %in% colnames(data)) {
+                a <- Ensembl[Ensembl$target %in% test1$data$Ensembl_ID[1],]
+                test1$data$shared_name[1] <- a$input
+              }
+              if(! test1$data$shared_name[2] %in% colnames(data)) {
+                b <- Ensembl[Ensembl$target %in% test1$data$Ensembl_ID[2],]
+                test1$data$shared_name[2] <- b$input
+              }
+			  if(input$ClassorMol == FALSE){
+			  cor_value <- cor(dataa[,test1$data$shared_name[1]], dataa[,test1$data$shared_name[2]],method = "spearman")
+              g <- ggplot(dataa,aes_string(test1$data$shared_name[1],test1$data$shared_name[2],fill = input$w,size = input$size))+geom_point(shape =21,color = "black")+
+    scale_fill_manual(values = unlist(col$col)) + ggtitle(paste("r = ",round(cor_value, 2)))
+			  }else{
+			  moldataa <- mutate(moldata, !!as.symbol(input$w) := !!as.symbol(input$w) %>% factor(levels = input$levels))
+	cor_value <- cor(moldataa[,input$X1], moldataa[,input$X2],method = "spearman")
+			  g <- ggplot(moldataa,aes_string(paste("`",input$X1,"`",sep =""),paste("`",input$X2,"`",sep =""),fill = input$w,size = input$size))+geom_point(shape =21,color = "black")+
+    scale_fill_manual(values = unlist(col$col))+ ggtitle(paste("r = ",round(cor_value, 2)))
+			  }
+			  plotly::ggplotly(g)
+            }
+            
+          })
+		  output$textOutput <- renderText({
+		  if(input$ClassorMol == FALSE){
+    "Select two nodes from the network to analyze their correlation."}
+	else{
+	"Select two features to analyze their correlation."
+	}
+  })
+		  
+
+
+		  
+		     observeEvent(input$selectedNodes, {
+      if(input$pathwaytype == "Global pathway"){
+        graph_json <<- paste(readLines("./pathwaymap/globalpathway.cyjs"), collapse="")　%>% gsub("localhost:9000", paste0(host,"/api"), .)
+      }else if(input$pathwaytype == "Ceramide pathway"){
+        graph_json <<- paste(readLines("./pathwaymap/ceramidepathway.cyjs"), collapse="")　%>% gsub("localhost:9000", paste0(host,"/api"), .)
+      }else if(input$pathwaytype == "Remodeling pathway"){
+        graph_json <<- paste(readLines("./pathwaymap/remodeling.cyjs"), collapse="")　%>% gsub("localhost:9000", paste0(host,"/api"), .)
+      }
+      test <- fromJSON(graph_json)
+      test <- as.data.frame(test$elements$nodes)
+      test1 <- test[test$data$id %in% unlist(input$selectedNodes),]
+      moldataa <- mutate(moldata, !!as.symbol(input$w) := !!as.symbol(input$w) %>% factor(levels = input$levels))
+      targetclass <- filter(alitable,Ontology %in% test1$data$shared_name)
+        shiny::updateSelectInput(session, "selectmol", selected = targetclass$`Metabolite name`[1], choices = targetclass$`Metabolite name`)
+      },ignoreNULL = TRUE)
+	  
+		  output$corselect2 <- renderPlot({
+        if(input$pathwaytype == "Global pathway"){
+          graph_json <<- paste(readLines("./pathwaymap/globalpathway.cyjs"), collapse="")　%>% gsub("localhost:9000", paste0(host,"/api"), .)
+        }else if(input$pathwaytype == "Ceramide pathway"){
+          graph_json <<- paste(readLines("./pathwaymap/ceramidepathway.cyjs"), collapse="")　%>% gsub("localhost:9000", paste0(host,"/api"), .)
+        }else if(input$pathwaytype == "Remodeling pathway"){
+          graph_json <<- paste(readLines("./pathwaymap/remodeling.cyjs"), collapse="")　%>% gsub("localhost:9000", paste0(host,"/api"), .)
+        }
+        test <- fromJSON(graph_json)
+        test <- as.data.frame(test$elements$nodes)
+        test1 <- test[test$data$id %in% unlist(input$selectedNodes),]
+        moldataa <- mutate(moldata, !!as.symbol(input$w) := !!as.symbol(input$w) %>% factor(levels = input$levels))
+        targetclass <- filter(alitable,Ontology %in% test1$data$shared_name)
+        if(length(test1$data$shared_name) > 0 && input$selectmol != " "){
+          if(test1$data$shared_name[1] %in% colnames(data)) {
+          if(input$mydrop == "box"){
+			g <- process_boxplot_diagram(input,paste("`",input$selectmol,"`",sep =""),moldataa,col$col,stat_testmol())
+          }
+          else if(input$mydrop == "bar"){
+			g <- process_barplot_diagram(input,paste("`",input$selectmol,"`",sep =""),moldataa,col$col,stat_testmol())
+          }
+          else if(input$mydrop == "violin"){
+			g <- process_violinplot_diagram(input,paste("`",input$selectmol,"`",sep =""),moldataa,col$col,stat_testmol())
+          }
+          else if(input$mydrop == "polar"){
+			g <- process_polar_diagram(input,paste("`",input$selectmol,"`",sep =""),moldataa,col$col,stat_testmol())
+          }
+          else if(input$mydrop == "coding"){
+
+			g <- process_dotplot_diagram(input,paste("`",input$selectmol,"`",sep =""),moldataa,col$col,stat_testmol())
+          }}
+          plot(g)
+        }
+        
+      })
+          })
+
+  observeEvent(input$save_pdf,{
+            shinyscreenshot::screenshot(selector="#cyjShinytest")
+          })
+  #Creating heatmap
+  observeEvent(input$actionplottest, {
+             waiter::waiter_show(
+               id = NULL,
+               html = tagList(waiter::spin_loader(),
+                              "Loading ..."),
+               color = "#333e48",
+               logo = "",
+               image = ""
+             )
+			 file_list <- list.files(svg_path)
+             if (length(unlist(file_list)) > 0) {
+                file.remove(file.path(svg_path, file_list))
+             } else {
+			 }
+            if(is.null(input$transcriptomefile) == FALSE){
+               Ensembl <- gconvert(query = colnames(transcriptome)[-c(1:2)] , organism = "mmusculus",#sapiens
+                                   target="ENSG", mthreshold = Inf, filter_na = TRUE) %>% select(input,target)
+              graph_json1 <- paste(readLines("./pathwaymap/ceramidepathway.cyjs"), collapse="")  %>% fromJSON()
+              graph_json2 <- paste(readLines("./pathwaymap/remodeling.cyjs"), collapse="") %>% fromJSON()
+              Ensemblinmap <- c(graph_json1$elements$nodes$data$Ensembl_ID,graph_json2$elements$nodes$data$Ensembl_ID)
+              Ensemblinmap <- Ensemblinmap[-which(Ensemblinmap %in% "")]
+	
+              geneinmap <- Ensembl[Ensembl[,2] %in% Ensemblinmap,]
+
+              transcriptome2 <- colnames(transcriptome)[colnames(transcriptome) %in% geneinmap$input]
+			  
+              data <- data[,colnames(data) %in% c(colnames(data)[1:metainfocol],colnames(Lipidomedata),transcriptome2)]
+			  dataa <- mutate(data, !!as.symbol(input$w) := !!as.symbol(input$w) %>% factor(levels = input$levels))
+			  names(dataa)[match(geneinmap$input, names(dataa))] <- geneinmap$target
+			  
+			  } else {
+			  dataa <- mutate(data, !!as.symbol(input$w) := !!as.symbol(input$w) %>% factor(levels = input$levels))
+			 }
+            
+           # if (length(grep("selectcolor", names(input), value = TRUE)) != 0) {
+           #   col <<-  process_select_color_input(input,data)
+           # }
+            if(input$mydrop == "box"){
+              process_action_boxplot(input,dataa,metainfocol,svg_path,col$col,output,data) 
+            }
+            else if(input$mydrop == "bar"){
+              process_action_barplot(input,dataa,metainfocol,svg_path,col$col,output,data) 
+            }
+            else if(input$mydrop == "violin"){
+              process_action_violinplot(input,dataa,metainfocol,svg_path,col$col,output,data) 
+            }
+            else if(input$mydrop == "polar"){
+              process_action_polarplot(input,dataa,metainfocol,svg_path,col$col,output,data) 
+            }
+            else if(input$mydrop == "coding"){
+              process_action_dotplot(input,dataa,metainfocol,svg_path,col$col,output,data) 
+            }
+            # if(input$filetype == "MS-DIAL export"){
+            #   exportgroupnodeheatmap(originaldata,metadata,paste(global$datapath,"",sep =""),input$w,input$levels,originaldir,input)}
+            Sys.sleep(3)
+             waiter::waiter_hide()
+			   shinyalert(
+        "Success",
+        "GO to Pathway analysis tab",
+        type = "success"
+      )
+        }
+          )
+  observeEvent(input$w,{
+   
+  output$heatmap <- renderPlot({
+  if(length(unlist(input$selectedNodes)) > 0 ) {
+        test <- fromJSON(graph_json)
+      test <- as.data.frame(test$elements$nodes)
+      test1 <- test[test$data$id %in% unlist(input$selectedNodes),]
+     groupnodeheatmap(lipidclassproperties,originaldata,metadata,input$w,input$levels,test1$data$shared_name[1],input)
+	 }
+  })
+  })
+} )})}

app/svg/.gitkeep

@@ -0,0 +1 @@
+

app/ui.R

@@ -0,0 +1,274 @@
+source("./modules/librarys.R")
+source("./modules/data_processing.R")
+source("./modules/plot_utils.R")
+source("./modules/heatmap_utils.R")
+source("./modules/modaldialog_utils.R")
+source("./modules/selectcolor_utils.R")
+source("./modules/ui_modules.R")
+
+shinyUI(
+  fluidPage(
+  theme = bs_theme(version = 5,,bootswatch = "sandstone"),
+    useShinyjs(),
+    tags$head(
+      tags$style(HTML(
+        "
+        .well { background-color: initial; border: initial; }
+        .navbar { margin-bottom: 5px; }
+        .container-fluid { padding-right: 10px; padding-left: 10px; }
+      .round-button {
+        border-radius: 50%;
+        width: 50px;
+        height: 50px;
+        padding: 0;
+        border: 2px solid #007bff;
+        background-color: white;
+        color: #007bff;
+        transition: all 0.3s;
+      }
+      
+      .round-button:hover {
+        background-color: #007bff;
+        color: white;
+      }
+      
+      .button-container {
+        display: flex;
+        gap: 20px;
+        justify-content: center;
+        margin: 20px 0;
+      }
+      
+      .button-with-label {
+        display: flex;
+        flex-direction: column;
+        align-items: center;
+        gap: 5px;
+      }
+
+      .card {
+        transition: transform 0.2s;
+        margin-bottom: 20px;
+      }
+
+      .card-title {
+        font-size: 1.2rem;
+        margin-bottom: 15px;
+      }
+
+      /* モーダル用のスタイル */
+      .modal-content {
+        border-radius: 10px;
+      }
+      .modal-header {
+        background-color: #f8f9fa;
+        border-radius: 10px 10px 0 0;
+      }
+        #sidebar {
+          position: fixed;
+          left: 0;
+          width: 250px;
+          height: calc(100% - 160px);  
+          top: 160px;  
+          background: #f8f9fa;
+          transition: all 0.3s;
+          padding: 15px;
+          z-index: 1000;
+        }
+        #sidebar.inactive {
+          left: -250px;
+        }
+		#content {
+        margin-left: 60px;
+        transition: all 0.3s;
+        height: calc(100vh - 50px);
+      }
+	  #content.active {
+        margin-left: 310px;
+      }
+        #main-content {
+          margin-left: 250px;
+          transition: all 0.3s;
+          padding-top: 80px;  
+        }
+        #main-content.inactive {
+          margin-left: 0;
+        }
+        #toggle_sidebar {
+          position: fixed;
+          left: 260px;
+          top: 160px;  
+          z-index: 1001;
+          width: 40px;
+          height: 40px;
+          background: #343a40;
+          color: #fff;
+          border: none;
+          border-radius: 5px;
+          transition: all 0.3s;
+        }
+        #toggle_sidebar.inactive {
+          left: 10px;
+        }
+        #graphContainer {
+          height: 100%;
+          width: 100%;
+        }
+        #tab3 {
+          margin-top: 70px;
+        }
+        "
+      ))
+    ),
+    navbarPage(
+      "MSDIAL2Cytoscape",
+      theme = bs_theme(version = 5),
+      tabPanel(
+        waiter::useWaiter(),
+        uiOutput("dynamicSidebar"),
+        uiOutput("dynamicSidebarToggle"),
+        tabsetPanel(
+          id = "mainTabs",
+          uploadpanel,
+          tabPanel(
+            value = "tab2",
+              tagList(
+    div(
+      style = "display: flex; align-items: center; justify-content: center; margin-bottom: 15px;",
+      img(src = "./barplot.png", style = "width: 50px; height: 50px; margin-right: 10px;"),
+      div(style = "font-size: 18px; color: #000000; font-weight: bold;", "Plot")
+    )
+  ),
+            fluidRow(
+ layout_columns(
+    col_widths = c(2, 10),
+				createCustomSidebar("y", "w", "z", "alpha", "size", "Fontsize", "levels", "pvaluecheck", "q"),
+                plotTabAppearanceUI(
+                  "", "./bar.png", "50px", "50px", "plottest", "plottest2", "testtext"
+                )
+              )
+            )
+          ),
+          tabPanel(
+  value = "tab3",   
+
+    tagList(
+    div(
+      style = "display: flex; align-items: center; justify-content: center; margin-bottom: 15px;",
+      img(src = "./network2.png", style = "width: 50px; height: 50px; margin-right: 10px;"),
+      div(style = "font-size: 18px; color: #000000; font-weight: bold;", "Pathway analysis")
+    )
+  ),
+
+ actionButton("toggle_sidebar", icon("bars"), class = "btn-info"), 
+
+    div(
+      id = "sidebar",
+      wellPanel(
+        id = "pathway-controls",
+        selectInput("pathwaytype", "Pathway type", 
+                    choices = c("Global pathway", "Ceramide pathway", "Remodeling pathway"), 
+                    selected = "Global pathway"),
+        actionButton("pathway", "Pathway mapping", class = "btn-primary btn-block"),
+        actionButton("save_pdf", "Save as PNG", class = "btn-success btn-block"),
+        downloadButton("exportCYJS", "Export CYJS", class = "btn-block"),
+        downloadButton("exportStyles", "Export styles.xml", class = "btn-block"),
+        checkboxInput("viewacyllevel", "Close-up nodes", value = TRUE)
+      )
+    ),
+        div(
+      id = "main-content",
+      fluidRow(
+        column(
+          width = 9,
+          div(id = "content", 
+              uiOutput("graphContainer")
+          )
+        ),
+        column(
+  width = 3,
+
+  actionButton("getSelectedNodes", "Click nodes in the Network and Confirm Selection", class = "btn-primary btn-block"),
+  br(), 
+  tabsetPanel(
+    id = "sideTabset",
+    tabPanel("Lipid Species Visualization",
+      div(id = "tabContent",
+	  wellPanel(
+        selectInput(
+          inputId = "selectmol",
+          label = "Select molecule to display",
+          choices = c(" "),
+          selected = " "
+        ),
+        plotOutput(outputId = "corselect2")),
+		wellPanel(
+		plotOutput(outputId = "heatmap",height = "600px" ,width = "100%"),
+        checkboxInput(inputId = "acylfilter", label = "Filtering common acylchains (16:0, 16:1, 18:0, 18:1, 18:2, 18:3, 20:3, 20:4, 20:5, 22:4, 22:5, 22:6)", value = TRUE),
+        checkboxInput(inputId = "sn", label = "sn", value = FALSE))
+      )
+    ),
+    tabPanel("Correlation Plot",
+      div(id = "tabContent",
+	  wellPanel(
+	  textOutput("textOutput"), 
+	  br(),
+	  plotlyOutput(outputId = "corselect")),
+        checkboxInput(inputId = "ClassorMol", label = "Correlation of lipid species", value = FALSE),
+        selectInput(
+          inputId = "X1",
+          label = "X",
+          choices = c(" "),
+          selected = " "
+        ),
+        selectInput(
+          inputId = "X2",
+          label = "Y",
+          choices = c(" "),
+          selected = " "
+        )
+      )
+    )
+  )
+)
+      )
+    ),
+    tags$script(HTML(
+      "
+      function toggleSidebar() {
+        var sidebar = $('#sidebar');
+        var mainContent = $('#main-content');
+        var toggleBtn = $('#toggle_sidebar');
+        
+        sidebar.toggleClass('inactive');
+        mainContent.toggleClass('inactive');
+        toggleBtn.toggleClass('inactive');
+        
+        if (sidebar.hasClass('inactive')) {
+          toggleBtn.find('i').removeClass('fa-times').addClass('fa-bars');
+        } else {
+          toggleBtn.find('i').removeClass('fa-bars').addClass('fa-times');
+        }
+      }
+      "
+    )),
+	tags$script(
+    '
+    var tab = $(\'a[data-value="tab3"]\').parent().addClass("disabled");
+    $(function(){
+      $(tab.parent()).on("click", "li.disabled", function(e) {
+        e.preventDefault();
+        return false;
+      });
+    });
+    '
+  )
+)
+        )
+      )
+    )
+  )
+)
+
+
+

inst/examples/Metadata.csv

@@ -0,0 +1,57 @@
+name,Year,Sex,Class2,Year_Sex
+190628_TT1_Hclass_Aging_001_38_9w_GF_F_Kidney_2_Pos,2,Female,2_GF,2_Female
+190628_TT1_Hclass_Aging_002_4_12M_SPF_M_Kidney_1_Pos,12,Male,12_SPF,12_Male
+190628_TT1_Hclass_Aging_003_27_9w_SPF_F_Kidney_3_Pos,2,Female,2_SPF,2_Female
+190628_TT1_Hclass_Aging_004_14_9w_GF_M_Kidney_2_Pos,2,Male,2_GF,2_Male
+190628_TT1_Hclass_Aging_005_33_18M_SPF_F_Kidney_3_Pos,19,Female,19_SPF,19_Female
+190628_TT1_Hclass_Aging_006_39_9w_GF_F_Kidney_3_Pos,2,Female,2_GF,2_Female
+190628_TT1_Hclass_Aging_007_28_12M_SPF_F_Kidney_1_Pos,12,Female,12_SPF,12_Female
+190628_TT1_Hclass_Aging_008_43_18M_GF_F_Kidney_1_Pos,19,Female,19_GF,19_Female
+190628_TT1_Hclass_Aging_009_20_18M_GF_M_Kidney_3_Pos,19,Male,19_GF,19_Male
+190628_TT1_Hclass_Aging_010_8_18M_SPF_M_Kidney_3_Pos,19,Male,19_SPF,19_Male
+190628_TT1_Hclass_Aging_011_19_18M_GF_M_Kidney_2_Pos,19,Male,19_GF,19_Male
+190628_TT1_Hclass_Aging_012_29_12M_SPF_F_Kidney_2_Pos,12,Female,12_SPF,12_Female
+190628_TT1_Hclass_Aging_013_45_18M_GF_F_Kidney_3_Pos,19,Female,19_GF,19_Female
+190628_TT1_Hclass_Aging_014_11_24M_SPF_M_Kidney_4_Pos,24,Male,24_SPF,24_Male
+190628_TT1_Hclass_Aging_015_31_18M_SPF_F_Kidney_1_Pos,19,Female,19_SPF,19_Female
+190628_TT1_Hclass_Aging_016_6_12M_SPF_M_Kidney_3_Pos,12,Male,12_SPF,12_Male
+190628_TT1_Hclass_Aging_017_18_12M_GF_M_Kidney_3_Pos,12,Male,12_GF,12_Male
+190628_TT1_Hclass_Aging_018_24_24M_GF_M_Kidney_3_Pos,24,Male,24_GF,24_Male
+190628_TT1_Hclass_Aging_019_17_12M_GF_M_Kidney_2_Pos,12,Male,12_GF,12_Male
+190628_TT1_Hclass_Aging_020_7_18M_SPF_M_Kidney_1_Pos,19,Male,19_SPF,19_Male
+190628_TT1_Hclass_Aging_021_5_12M_SPF_M_Kidney_2_Pos,12,Male,12_SPF,12_Male
+190628_TT1_Hclass_Aging_022_44_18M_GF_F_Kidney_2_Pos,19,Female,19_GF,19_Female
+190628_TT1_Hclass_Aging_023_35_24M_SPF_F_Kidney_3_Pos,24,Female,24_SPF,24_Female
+190628_TT1_Hclass_Aging_024_10_24M_SPF_M_Kidney_3_Pos,24,Male,24_SPF,24_Male
+190628_TT1_Hclass_Aging_025_36_24M_SPF_F_Kidney_5_Pos,24,Female,24_SPF,24_Female
+190628_TT1_Hclass_Aging_026_2_9w_SPF_M_Kidney_2_Pos,2,Male,2_SPF,2_Male
+190628_TT1_Hclass_Aging_027_15_9w_GF_M_Kidney_3_Pos,2,Male,2_GF,2_Male
+190628_TT1_Hclass_Aging_028_9_18M_SPF_M_Kidney_4_Pos,19,Male,19_SPF,19_Male
+190628_TT1_Hclass_Aging_029_42_12M_GF_F_Kidney_3_Pos,12,Female,12_GF,12_Female
+190628_TT1_Hclass_Aging_030_3_9w_SPF_M_Kidney_3_Pos,2,Male,2_SPF,2_Male
+190628_TT1_Hclass_Aging_031_48_24M_GF_F_Kidney_6_Pos,24,Female,24_GF,24_Female
+190628_TT1_Hclass_Aging_032_23_24M_GF_M_Kidney_2_Pos,24,Male,24_GF,24_Male
+190628_TT1_Hclass_Aging_033_12_24M_SPF_M_Kidney_5_Pos,24,Male,24_SPF,24_Male
+190628_TT1_Hclass_Aging_034_32_18M_SPF_F_Kidney_2_Pos,19,Female,19_SPF,19_Female
+190628_TT1_Hclass_Aging_035_16_12M_GF_M_Kidney_1_Pos,12,Male,12_GF,12_Male
+190628_TT1_Hclass_Aging_036_21_18M_GF_M_Kidney_5_Pos,19,Male,19_GF,19_Male
+190628_TT1_Hclass_Aging_037_13_9w_GF_M_Kidney_1_Pos,2,Male,2_GF,2_Male
+190628_TT1_Hclass_Aging_038_47_24M_GF_F_Kidney_3_Pos,24,Female,24_GF,24_Female
+190628_TT1_Hclass_Aging_039_41_12M_GF_F_Kidney_2_Pos,12,Female,12_GF,12_Female
+190628_TT1_Hclass_Aging_040_25_9w_SPF_F_Kidney_1_Pos,2,Female,2_SPF,2_Female
+190628_TT1_Hclass_Aging_041_46_24M_GF_F_Kidney_2_Pos,24,Female,24_GF,24_Female
+190628_TT1_Hclass_Aging_042_26_9w_SPF_F_Kidney_2_Pos,2,Female,2_SPF,2_Female
+190628_TT1_Hclass_Aging_043_37_9w_GF_F_Kidney_1_Pos,2,Female,2_GF,2_Female
+190628_TT1_Hclass_Aging_044_34_24M_SPF_F_Kidney_2_Pos,24,Female,24_SPF,24_Female
+190628_TT1_Hclass_Aging_045_30_12M_SPF_F_Kidney_3_Pos,12,Female,12_SPF,12_Female
+190628_TT1_Hclass_Aging_046_22_24M_GF_M_Kidney_1_Pos,24,Male,24_GF,24_Male
+190628_TT1_Hclass_Aging_047_1_9w_SPF_M_Kidney_1_Pos,2,Male,2_SPF,2_Male
+190628_TT1_Hclass_Aging_048_40_12M_GF_F_Kidney_1_Pos,12,Female,12_GF,12_Female
+190628_TT1_Hclass_Aging_Blank_Pos,Blank,Blank,Blank,Blank_Blank
+190628_TT1_Hclass_Aging_QC_Pos_01,Quality control,Quality control,QC,Quality control_Quality control
+190628_TT1_Hclass_Aging_QC_Pos_02,Quality control,Quality control,QC,Quality control_Quality control
+190628_TT1_Hclass_Aging_QC_Pos_03,Quality control,Quality control,QC,Quality control_Quality control
+190628_TT1_Hclass_Aging_QC_Pos_04,Quality control,Quality control,QC,Quality control_Quality control
+190628_TT1_Hclass_Aging_QC_Pos_05,Quality control,Quality control,QC,Quality control_Quality control
+190628_TT1_Hclass_Aging_QC_Pos_06,Quality control,Quality control,QC,Quality control_Quality control
+190628_TT1_Hclass_Aging_QC_Pos_07,Quality control,Quality control,QC,Quality control_Quality control

nginx.conf

@@ -0,0 +1,29 @@
+events {
+    worker_connections 1024;
+}
+
+http {
+    server {
+        listen 7860;
+        client_max_body_size 150M;
+        
+        # Shinyアプリへのプロキシ
+        location / {
+            proxy_pass http://localhost:9000;
+            proxy_set_header Host $host;
+            proxy_set_header X-Real-IP $remote_addr;
+            proxy_set_header Upgrade $http_upgrade;
+            proxy_set_header Connection "upgrade";
+            proxy_read_timeout 86400;
+            proxy_set_header X-Forwarded-Proto $scheme;      # プロトコル
+            proxy_set_header Request-Path      $request_uri; # リクエストURLのパス部分
+        }
+
+        # Plumber APIへのプロキシ
+        location /api/ {
+            proxy_pass http://localhost:8000/;
+            proxy_set_header Host $host;
+            proxy_set_header X-Real-IP $remote_addr;
+        }
+    }
+}

scripts/start.sh

@@ -0,0 +1,10 @@
+#!/bin/bash
+# デーモンモードでNginxを起動
+nginx 
+
+# Plumber APIを起動（8000番ポート）
+R -e "plumber::pr_run(plumber::pr('/srv/app/plumber.R'), port=8000, host='0.0.0.0')" &
+
+# メインプロセスとしてShinyアプリを起動（9000番ポート）
+# フォアグラウンドで実行
+R -e "shiny::runApp('/srv/app', host='0.0.0.0', port=9000)"