diff --git a/06_Shiny/.DS_Store b/06_Shiny/.DS_Store
deleted file mode 100644
index b455ec8..0000000
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diff --git a/06_Shiny/.Rproj.user/.DS_Store b/06_Shiny/.Rproj.user/.DS_Store
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index 7651473..0000000
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diff --git a/06_Shiny/.Rproj.user/94131CCE/pcs/files-pane.pper b/06_Shiny/.Rproj.user/94131CCE/pcs/files-pane.pper
deleted file mode 100644
index 7935ff1..0000000
--- a/06_Shiny/.Rproj.user/94131CCE/pcs/files-pane.pper
+++ /dev/null
@@ -1,9 +0,0 @@
-{
- "sortOrder": [
- {
- "columnIndex": 2,
- "ascending": true
- }
- ],
- "path": "~/Documents/ownCloud/DexStim_RNAseq_Mouse/scripts/06_Shiny"
-}
\ No newline at end of file
diff --git a/06_Shiny/.Rproj.user/94131CCE/pcs/source-pane.pper b/06_Shiny/.Rproj.user/94131CCE/pcs/source-pane.pper
deleted file mode 100644
index bc7dc99..0000000
--- a/06_Shiny/.Rproj.user/94131CCE/pcs/source-pane.pper
+++ /dev/null
@@ -1,3 +0,0 @@
-{
- "activeTab": 4
-}
\ No newline at end of file
diff --git a/06_Shiny/.Rproj.user/94131CCE/pcs/windowlayoutstate.pper b/06_Shiny/.Rproj.user/94131CCE/pcs/windowlayoutstate.pper
deleted file mode 100644
index 104979b..0000000
--- a/06_Shiny/.Rproj.user/94131CCE/pcs/windowlayoutstate.pper
+++ /dev/null
@@ -1,14 +0,0 @@
-{
- "left": {
- "splitterpos": 312,
- "topwindowstate": "NORMAL",
- "panelheight": 743,
- "windowheight": 781
- },
- "right": {
- "splitterpos": 468,
- "topwindowstate": "NORMAL",
- "panelheight": 743,
- "windowheight": 781
- }
-}
\ No newline at end of file
diff --git a/06_Shiny/.Rproj.user/94131CCE/pcs/workbench-pane.pper b/06_Shiny/.Rproj.user/94131CCE/pcs/workbench-pane.pper
deleted file mode 100644
index 07157f3..0000000
--- a/06_Shiny/.Rproj.user/94131CCE/pcs/workbench-pane.pper
+++ /dev/null
@@ -1,5 +0,0 @@
-{
- "TabSet1": 0,
- "TabSet2": 4,
- "TabZoom": {}
-}
\ No newline at end of file
diff --git a/06_Shiny/.Rproj.user/94131CCE/rmd-outputs b/06_Shiny/.Rproj.user/94131CCE/rmd-outputs
deleted file mode 100644
index 3f2ff2d..0000000
--- a/06_Shiny/.Rproj.user/94131CCE/rmd-outputs
+++ /dev/null
@@ -1,5 +0,0 @@
-
-
-
-
-
diff --git a/06_Shiny/.Rproj.user/94131CCE/saved_source_markers b/06_Shiny/.Rproj.user/94131CCE/saved_source_markers
deleted file mode 100644
index 2b1bef1..0000000
--- a/06_Shiny/.Rproj.user/94131CCE/saved_source_markers
+++ /dev/null
@@ -1 +0,0 @@
-{"active_set":"","sets":[]}
\ No newline at end of file
diff --git a/06_Shiny/.Rproj.user/94131CCE/sources/per/t/1532F1BD b/06_Shiny/.Rproj.user/94131CCE/sources/per/t/1532F1BD
deleted file mode 100644
index 0a412e4..0000000
--- a/06_Shiny/.Rproj.user/94131CCE/sources/per/t/1532F1BD
+++ /dev/null
@@ -1,24 +0,0 @@
-{
- "id": "1532F1BD",
- "path": "~/Documents/ownCloud/DexStim_RNAseq_Mouse/scripts/06_Shiny/R/network_multiRegion.R",
- "project_path": "R/network_multiRegion.R",
- "type": "r_source",
- "hash": "4109658605",
- "contents": "",
- "dirty": false,
- "created": 1636637444100.0,
- "source_on_save": false,
- "relative_order": 5,
- "properties": {
- "cursorPosition": "219,31",
- "scrollLine": "211"
- },
- "folds": "",
- "lastKnownWriteTime": 1636637543,
- "encoding": "UTF-8",
- "collab_server": "",
- "source_window": "",
- "last_content_update": 1636637543028,
- "read_only": false,
- "read_only_alternatives": []
-}
\ No newline at end of file
diff --git a/06_Shiny/.Rproj.user/94131CCE/sources/per/t/1532F1BD-contents b/06_Shiny/.Rproj.user/94131CCE/sources/per/t/1532F1BD-contents
deleted file mode 100644
index 402cda3..0000000
--- a/06_Shiny/.Rproj.user/94131CCE/sources/per/t/1532F1BD-contents
+++ /dev/null
@@ -1,494 +0,0 @@
-# module UI function
-networkMultiUI <- function(id, label = "Network Multiple Regions"){
-
- ns <- NS(id)
-
- tagList(
-
- sidebarPanel(
- pickerInput(
- inputId = ns("network_multireg"),
- label = "Select brain regions",
- choices = c("Amygdala" = "AMY",
- "Cerebellum" = "CER",
- "Dorsal DG of Hippocampus" = "dDG",
- "Dorsal CA1 of Hippocampus" = "dCA1",
- "Prefrontal Cortex" = "PFC",
- "PVN of Hypothalamus" = "PVN",
- "Ventral DG of Hippocampus" = "vDG",
- "Ventral CA1 of Hippocampus" = "vCA1"),
- options = list(
- `actions-box` = TRUE,
- size = 8,
- `selected-text-format` = "count > 3"
- ),
- multiple = TRUE
- ),
- searchInput(
- inputId = ns("network_gene"),
- label = "Enter comma-separated list of genes: ",
- placeholder = "e.g. Nr3c1,Fkbp5",
- btnSearch = icon("search", class = "fas fa-search", lib = "font-awesome"),
- btnReset = icon("remove", class = "fas fa-remove", lib = "font-awesome"),
- width = "100%"
- ),
- fileInput(
- inputId = ns("file1"),
- label = "Upload file with list of genes",
- accept = "text/plain",
- buttonLabel = "Upload",
- placeholder = "Upload file with list of genes"),
- radioButtons(
- ns("network_type"),
- label = "Select type of network to display:",
- choiceNames = list("Baseline", "Differential", "Treatment"),
- choiceValues = list("baseline", "diff", "treatment"),
- selected = "diff"
- ),
- radioButtons(
- ns("vis_neighbours"),
- label = "Include gene neighbourhood",
- choiceNames = c("Yes", "No"),
- choiceValues = c(TRUE, FALSE),
- selected = FALSE
- ),
- radioButtons(
- ns("id_type"),
- label = "Select type of gene ID:",
- choices = list("Ensembl", "Gene Symbol"),
- selected = "Gene Symbol"
- ),
- checkboxGroupInput(
- ns("tableContent"),
- label = "Table content:",
- choices = list(
- "z-scores" = "z",
- "p-values" = "p",
- "beta values" = "beta"
- ),
- selected = "z"
- ),
- downloadButton(ns("download2"),"Download (filtered) table as csv"),
- width = 3
- ),
- mainPanel(
- # visNetwork
- fluidPage(
- br(),
- tags$style(".fa-project-diagram {color:#2980B9}"),
- h3(p(
- em("Network analysis of gene expression "),
- icon("project-diagram", lib = "font-awesome"),
- style = "color:black;text-align:center"
- )),
- hr(),
- h4(p("Differential network", style = "color:black;text-align:center")),
- visNetworkOutput(ns("network_diff_multi"), height = "600px")
- %>% withSpinner(color="#0dc5c1"),
- DT::dataTableOutput(ns("network_table"))
- )
- )
-
-
- )
-}
-
-
-
-# module server function
-networkMultiServer <- function(id) {
- moduleServer(
- id,
-
- function(input, output, session) {
-
- ### MULTI REGION NETWORK -------------------------------
-
- # Load data from multiple brain region
- network_data <- reactive({
-
- req(input$network_multireg)
-
- # Read data tables from all required regions
- list_network <- list()
-
- for (reg in input$network_multireg){
- file_path <- paste0(
- "tables/network/",
- "04_singleRegion_",
- reg,
- "_filtered_",
- input$network_type,
- "Network.csv"
- )
- # Read data
- if (input$network_type == "diff") {
- data <-
- fread(file = file_path) %>%
- dplyr::select(target, predictor, beta_mean.base, beta_mean.dex, z, p_adj)
- #dplyr::select(target, predictor, z) %>%
- #dplyr::rename_with(.fn = ~ reg, .cols = z)
-
- cols <- colnames(data)[3:6]
- data <- data %>%
- dplyr::rename_with(.fn = ~paste0(., ".", reg), .cols = all_of(cols) )
- } else {
- data <-
- fread(file = file_path) %>%
- dplyr::select(target, predictor, beta_mean)
- # cols <- colnames(data)[3]
- data <- data %>%
- dplyr::rename_with(.fn = ~ reg, .cols = beta_mean)
- # dplyr::rename_with(.fn = ~paste0(., ".", reg), .cols = all_of(cols) )
- }
- list_network[[reg]] <- data
- }
-
- data_joined <- list_network %>%
- purrr::reduce(full_join, by = c("target","predictor"))
-
- return(data_joined)
- })
-
-
- # #### TEST
- #
- # list_network <- list()
- #
- # for (reg in c("AMY", "CER", "PFC")){
- # file_path <- paste0(
- # basepath,
- # "tables/coExpression_kimono/03_AnalysisFuncoup/",
- # "04_singleRegion_",
- # reg,
- # "_filtered_",
- # "baseline",
- # "Network.csv"
- # )
- # # Read data
- # data <-
- # fread(file = file_path) %>%
- # #dplyr::select(target, predictor, beta_mean.base, beta_mean.dex, z, p_adj)
- # dplyr::select(target, predictor, beta_mean, beta_mean.dex, z, p_adj)
- #
- # cols <- colnames(data)[3:6]
- # #cols <- colnames(data)[3]
- # data <- data %>%
- # dplyr::rename_with(.fn = ~paste0(., ".", reg), .cols = all_of(cols) )
- # list_network[[reg]] <- data
- # }
- #
- # data_joined <- list_network %>%
- # purrr::reduce(full_join, by = c("target","predictor"))
-
-
- # DE genes of required brain regions
- de_genes <- reactive({
-
- list_de <- list()
- # Read and subset DE genes for coloring
- for (reg in input$network_multireg){
- df_de <- fread(paste0("tables/de/02_",
- reg,"_deseq2_Dex_1_vs_0_lfcShrink.txt"))
- na_indices <- which(is.na(df_de$padj))
- df_de$padj[na_indices] <- 1
- df_de <- df_de[df_de$padj <= 0.1,]
-
- df_de <- df_de %>%
- dplyr::select(Ensembl_ID, padj) %>%
- dplyr::rename_with(.fn = ~ reg, .cols = padj)
-
- list_de[[reg]] <- df_de
- }
-
- de_joined <- list_de %>%
- purrr::reduce(full_join, by = c("Ensembl_ID"))
-
- return(de_joined)
- })
-
-
- # hub genes of required brain regions
- hub_genes <- reactive({
-
- list_hub <- list()
- # Read and subset hub genes for coloring
- for (reg in input$network_multireg) {
- df_hub <-
- fread(
- paste0(
- "tables/network/04_",
- reg,
- "_funcoup_differential",
- "_nodebetweennessNorm_betacutoff0.01.csv"
- )
- ) %>%
- filter(nodebetweenness_norm >= 1) %>%
- dplyr::select(ensembl_id, nodebetweenness_norm) %>%
- dplyr::rename_with(.fn = ~ reg, .cols = nodebetweenness_norm)
-
- list_hub[[reg]] <- df_hub
- }
-
- hub_joined <- list_hub %>%
- purrr::reduce(full_join, by = c("ensembl_id"))
-
- return(hub_joined)
- })
-
-
- # input genes
- input_genes <- reactive({
-
- if (isTruthy(input$network_gene)){
- genes <- input$network_gene
- genes <- stringr::str_split(genes, ",\\s?")[[1]]
- genes <- reformat_genes(genes)
- } else if (isTruthy(input$file1)){
- genes <- read.csv(input$file1$datapath, header = FALSE)$V1
- genes <- reformat_genes(genes)
- }
- })
-
-
- # bring input genes to correct format
- reformat_genes <- function(list_genes){
- if (!startsWith(list_genes[1], "ENSMUSG")){
- format_gene <- sapply(list_genes, stringr::str_to_title)
- format_gene <- mapIds(org.Mm.eg.db, keys = format_gene, column = "ENSEMBL", keytype = "SYMBOL")
- ids_na <- names(format_gene)[which(is.na(format_gene))]
- #print(ids_na)
- if (length(ids_na) > 0) {
- showNotification(paste("No Ensembl ID found for following genes: ",
- ids_na), type = "message", duration = 5)
- }
- format_gene <- format_gene[which(!is.na(format_gene))]
- } else {
- format_gene <- list_genes
- }
- return(format_gene)
- }
-
-
- # Network visualization
- output$network_diff_multi <- renderVisNetwork({
-
- req(input_genes())
- req(network_data())
- req(de_genes())
- req(hub_genes())
-
- # Get Nodes and Edges
- network <- network(network_data(),
- de_genes(),
- hub_genes(),
- input_genes(),
- input$network_type,
- input$id_type,
- input$vis_neighbours)
-
- visNetwork(network$nodes, network$edges) %>%
- visEdges(arrows = "to") %>%
- visOptions(highlightNearest = list(enabled = T, degree = 1, hover = T)) %>%
- visLegend(addEdges = network$ledges, #addNodes = network$lnodes,
- useGroups = FALSE) %>%
- visIgraphLayout()
- })
-
-
- # Data table with network results
- output$network_table <- DT::renderDataTable({
-
- network_table() %>%
- datatable(filter = list(position = 'top'))
- }, server = TRUE) # FALSE to enable download of all pages with button
-
-
- # Download of (filtered) network table
- output$download2 <- downloadHandler(
- filename = function() {
- paste0("network_dexStimMouse_multiRegion.csv")
- },
- content = function(file) {
- indices <- input$network_table_rows_all
- write.csv(network_table()[indices, ], file)
- }
- )
-
-
- # prepare network for visualization
- network <- function(data, de_genes, hub_genes, input_genes, mode, id_type, neighbours){
-
- # input genes
- print(input_genes)
-
- # filter data
- data <- data %>%
- dplyr::rename(from = predictor, to = target) %>%
- dplyr::filter(from != to)
- data <- subset_network(data, input_genes, neighbours)
-
- # color palettes for edges and nodes
- # edge palette --> assumes that data stores only target, predictor and z scores
- # edge_colors <- brewer.pal(ncol(data)-1, "Blues")[2:(ncol(data)-1)]
-
- # Edges
- if(mode == "baseline" | mode == "treatment"){
- # color palettes for edges and nodes
- edge_colors <- brewer.pal(ncol(data)-1, "Blues")[2:(ncol(data)-1)]
-
- # count regions per diff edge that are not na
- data$edge_reg <- apply(data[,3:ncol(data)], 1, function(x) names(which(!is.na(x))) )
- data$count_reg <- sapply(data$edge_reg, length)
- data$title <- paste0("Connection in: ", sapply(data$edge_reg, paste, collapse = ", "),
- "
")
- # assign color to edges according to number of regions
- data$c <- sapply(data$count_reg, function(x) edge_colors[x])
-
- print(head(data))
- # df for edges
- relations <- data.table(from=data$from,
- to=data$to,
- #beta = data$beta_mean,
- color = c,
- title = data$title)
- # df for edge legend
- ledges <- data.frame(color = edge_colors,
- label = 1:(ncol(data)-6),
- font.align = "top")
- } else {
- # remove columns
- data <- data %>% dplyr::select(-contains("beta"), -contains("p_adj"))
-
- # color palettes for edges and nodes
- # edge palette --> assumes that data stores only target, predictor and z scores
- edge_colors <- brewer.pal(ncol(data)-1, "Blues")[2:(ncol(data)-1)]
-
- # count regions per diff edge that are not na
- data$edge_reg <- apply(data[,3:ncol(data)], 1, function(x) names(which(!is.na(x))) )
- data$count_reg <- sapply(data$edge_reg, length)
- data$title <- paste0("Diff. co-expressed in: ", sapply(data$edge_reg, paste, collapse = ", "),
- "
")
- # assign color to edges according to number of regions
- data$c <- sapply(data$count_reg, function(x) edge_colors[x])
-
- print(head(data))
- # df for edges
- relations <- data.table(from=data$from,
- to=data$to,
- #z = data$z,
- #p_adj = data$p_adj,
- color = data$c,
- title = data$title)
- # df for edge legend
- ledges <- data.frame(color = edge_colors,
- label = 1:(ncol(data)-6),
- font.align = "top")
- }
- print(nrow(relations))
-
-
- # Nodes
- # get unique nodes with correct id
- nodes <- data.frame("id" = unique(union(
- c(relations$from, relations$to),
- input_genes
- )), stringsAsFactors = FALSE)
- if (id_type == "Gene Symbol"){
- print(nodes$id)
- nodes$label <- mapIds(org.Mm.eg.db, keys = nodes$id,
- column = "SYMBOL", keytype = "ENSEMBL")
- } else {
- nodes$label <- nodes$id
- }
-
- # count regions where gene is DE or/and hub
- nodes_de <- left_join(x = nodes, y = de_genes,
- by = c("id" = "Ensembl_ID"))
- nodes_hub <- left_join(x = nodes, y = hub_genes,
- by = c("id" = "ensembl_id"))
-
- nodes$de_reg <- apply(nodes_de[,3:ncol(nodes_de)], 1,
- function(x) names(which(!is.na(x))) )
- nodes$de_count <- sapply(nodes$de_reg, length)
-
- nodes$hub_reg <- apply(nodes_hub[,3:ncol(nodes_hub)], 1,
- function(x) names(which(!is.na(x))) )
- nodes$hub_count <- sapply(nodes$hub_reg, length)
-
- # set color according to DE/hub status
- nodes$color <- rep("darkblue", nrow(nodes_de))
- nodes$color[nodes$de_count > 0] <- "orange"
- nodes$color[nodes$hub_count > 0] <- "darkred"
- nodes$color[nodes$de_count > 0 & nodes$hub_count > 0] <- "purple"
-
- #nodes$opacity <- nodes$de_count + nodes$hub_count
- #nodes$opacity <- nodes$opacity/max(nodes$opacity)
-
- nodes$title <- paste0("",nodes$label,"",
- "
DE in regions: ", sapply(nodes$de_reg, paste, collapse = ", "),
- "
Hub in regions: ", sapply(nodes$hub_reg, paste, collapse = ", "),"
")
-
- print(head(nodes))
-
- # df for node data frame
- lnodes <- data.frame(label = c("DE", "hub", "DE & hub", "no DE & no hub"),
- shape = c( "ellipse"), color = c("orange", "darkred", "purple", "darkblue"),
- title = "Informations", id = 1:4)
-
-
- # return(list("edges" = relations, "nodes" = nodes, "ledges" = ledges, "lnodes" = lnodes))
- return(list("edges" = relations, "nodes" = nodes, "ledges" = ledges))
-
- }
-
-
-
- # prepare network table for visualization
- network_table <- reactive({
-
- req(network_data())
- req(input_genes())
-
- print(input$tableContent)
-
-
- # filter data
- # remove self connections
- data <- network_data() %>%
- dplyr::rename(from = predictor, to = target) %>%
- dplyr::filter(from != to)
- data <- subset_network(data, input_genes(), input$vis_neighbours)
-
- # Network table
- if(input$network_type == "baseline" | input$network_type == "treatment"){
-
- # add beta to column name
- cols <- colnames(data)[3:ncol(data)]
- data <- data %>%
- # dplyr::rename_with(.fn = ~ reg, .cols = beta_mean)
- dplyr::rename_with(.fn = ~paste0("beta.", .), .cols = all_of(cols) )
-
- } else {
-
- if (!("beta" %in% input$tableContent)){
- data <- data %>% dplyr::select(-contains("beta"))
- }
- if (!("z" %in% input$tableContent)){
- data <- data %>% dplyr::select(-contains("z"))
- }
- if(!("p" %in% input$tableContent)){
- data <- data %>% dplyr::select(-contains("p_adj"))
- }
-
- }
-
- data
-
- })
-
-
- }
-
- )
-}
\ No newline at end of file
diff --git a/06_Shiny/.Rproj.user/94131CCE/sources/per/t/3F51A6BD b/06_Shiny/.Rproj.user/94131CCE/sources/per/t/3F51A6BD
deleted file mode 100644
index 91cbb3f..0000000
--- a/06_Shiny/.Rproj.user/94131CCE/sources/per/t/3F51A6BD
+++ /dev/null
@@ -1,24 +0,0 @@
-{
- "id": "3F51A6BD",
- "path": "~/Documents/ownCloud/DexStim_RNAseq_Mouse/scripts/06_Shiny/R/network_singleRegion.R",
- "project_path": "R/network_singleRegion.R",
- "type": "r_source",
- "hash": "1168392640",
- "contents": "",
- "dirty": false,
- "created": 1636637354945.0,
- "source_on_save": false,
- "relative_order": 4,
- "properties": {
- "cursorPosition": "129,46",
- "scrollLine": "115"
- },
- "folds": "",
- "lastKnownWriteTime": 1636637425,
- "encoding": "UTF-8",
- "collab_server": "",
- "source_window": "",
- "last_content_update": 1636637425157,
- "read_only": false,
- "read_only_alternatives": []
-}
\ No newline at end of file
diff --git a/06_Shiny/.Rproj.user/94131CCE/sources/per/t/3F51A6BD-contents b/06_Shiny/.Rproj.user/94131CCE/sources/per/t/3F51A6BD-contents
deleted file mode 100644
index 9ef0406..0000000
--- a/06_Shiny/.Rproj.user/94131CCE/sources/per/t/3F51A6BD-contents
+++ /dev/null
@@ -1,240 +0,0 @@
-# module UI function
-networkSingleUI <- function(id, label = "Network Single Region"){
-
- ns <- NS(id)
-
- tagList(
-
- sidebarPanel(
- selectInput(
- ns("network_region"),
- "Choose a brain region:",
- choices = c(
- "Amygdala" = "AMY",
- "Cerebellum" = "CER",
- "Dorsal DG of Hippocampus" = "dDG",
- "Dorsal CA1 of Hippocampus" = "dCA1",
- "Prefrontal Cortex" = "PFC",
- "PVN of Hypothalamus" = "PVN",
- "Ventral DG of Hippocampus" = "vDG",
- "Ventral CA1 of Hippocampus" = "vCA1"
- )
- ),
- searchInput(
- inputId = ns("network_gene"),
- label = "Enter comma-separated list of genes: ",
- placeholder = "e.g. Nr3c1,Fkbp5",
- btnSearch = icon("search", class = "fas fa-search", lib = "font-awesome"),
- btnReset = icon("remove", class = "fas fa-remove", lib = "font-awesome"),
- width = "100%"
- ),
- fileInput(
- inputId = ns("file1"),
- label = "Upload file with list of genes",
- accept = "text/plain",
- buttonLabel = "Upload",
- placeholder = "Upload file with list of genes"),
- radioButtons(
- ns("network_type"),
- label = "Select type of network to display:",
- choiceNames = list("Baseline", "Differential", "Treatment"),
- choiceValues = list("baseline", "diff", "treatment"),
- selected = "diff"
- ),
- radioButtons(
- ns("vis_neighbours"),
- label = "Include gene neighbourhood",
- choiceNames = c("Yes", "No"),
- choiceValues = c(TRUE, FALSE),
- selected = FALSE
- ),
- radioButtons(
- ns("id_type"),
- label = "Select type of gene ID for plot:",
- choices = list("Ensembl", "Gene Symbol"),
- selected = "Gene Symbol"
- ),
- downloadButton(ns("download2"),"Download (filtered) table as csv"),
- width = 3
- ),
- mainPanel(
- # visNetwork
- fluidPage(
- br(),
- tags$style(".fa-project-diagram {color:#2980B9}"),
- h3(p(
- em("Network analysis of gene expression "),
- icon("project-diagram", lib = "font-awesome"),
- style = "color:black;text-align:center"
- )),
- hr(),
- h4(p("Differential network", style = "color:black;text-align:center")),
- visNetworkOutput(ns("network"), height = "600px") %>%
- withSpinner(color="#0dc5c1"),
- DT::dataTableOutput(ns("network_table"))
- )
- )
-
- )
-
-}
-
-
-
-# module server function
-networkSingleServer <- function(id) {
- moduleServer(
- id,
-
- function(input, output, session) {
-
- ### SINGLE REGION NETWORK -------------------------------
-
- # Load data from one brain region
- network_df <- reactive({
- file_path <- paste0(
- "tables/network/",
- "04_singleRegion_",
- input$network_region,
- "_filtered_",
- input$network_type,
- "Network.csv"
- )
- # Read data
- if (input$network_type == "diff") {
- data <-
- fread(file = file_path) %>%
- dplyr::select(target, predictor, beta_mean.base, beta_mean.dex, z, p_adj)
- } else {
- data <-
- fread(file = file_path) %>%
- dplyr::select(target, predictor, beta_mean)
- }
-
- })
-
- # DE genes of region
- de_genes <- reactive({
- # Read and subset DE genes for coloring
- df_de <- fread(paste0("tables/de/02_",
- input$network_region,"_deseq2_Dex_1_vs_0_lfcShrink.txt"))
- na_indices <- which(is.na(df_de$padj))
- df_de$padj[na_indices] <- 1
- df_de <- df_de[df_de$padj <= 0.1,]
- df_de$Ensembl_ID
- })
-
- # hub genes of region
- hub_genes <- reactive({
- # Read and subset hub genes for coloring
- df_hub <- fread(paste0("tables/network/04_",
- input$network_region,"_funcoup_differential",
- "_nodebetweennessNorm_betacutoff0.01.csv")) %>%
- filter(nodebetweenness_norm >= 1)
- df_hub$ensembl_id
- })
-
-
- # input genes
- input_genes <- reactive({
-
- if (isTruthy(input$network_gene)){
- genes <- input$network_gene
- genes <- stringr::str_split(genes, ",\\s?")[[1]]
- genes <- reformat_genes(genes)
- } else if (isTruthy(input$file1)){
- genes <- read.csv(input$file1$datapath, header = FALSE)$V1
- genes <- reformat_genes(genes)
- }
- #genes
- })
-
-
- # bring input genes to correct format
- reformat_genes <- function(list_genes){
- if (!startsWith(list_genes[1], "ENSMUSG")){
- format_gene <- sapply(list_genes, stringr::str_to_title)
- format_gene <- mapIds(org.Mm.eg.db, keys = format_gene, column = "ENSEMBL", keytype = "SYMBOL")
- } else {
- format_gene <- list_genes
- }
- return(format_gene)
- }
-
-
- # Network visualization
- output$network <- renderVisNetwork({
-
- req(input_genes())
- req(network_df())
- req(de_genes())
- req(hub_genes())
-
- # Get Nodes and Edges
- network <- read_network(network_df(),
- de_genes(),
- hub_genes(),
- input_genes(),
- input$network_type,
- input$id_type,
- input$vis_neighbours)
-
- visNetwork(network$nodes, network$edges) %>%
- visEdges(arrows = "to") %>%
- visOptions(highlightNearest = TRUE) %>%
- visLegend(addEdges = network$ledges, addNodes = network$lnodes,
- useGroups = FALSE)
- #visIgraphLayout()
- })
-
- # network table
- network_data <- reactive({
-
- req(input_genes())
- req(network_df())
-
- # input genes
- gene <- input_genes()
-
- # Get data and subset to neighbours of gene of interest
- data <- network_df() %>%
- dplyr::rename("from" = "target", "to" = "predictor")
- data <- simplify_network_df(data)
- data <- subset_network(data, gene, input$vis_neighbours)
-
- # ID type to gene symbol
- if(input$id_type == "Gene Symbol"){
- data$from <- mapIds(org.Mm.eg.db, keys = data$from,
- column = "SYMBOL", keytype = "ENSEMBL")
- data$to <- mapIds(org.Mm.eg.db, keys = data$to,
- column = "SYMBOL", keytype = "ENSEMBL")
- }
-
- data %>%
- dplyr::rename("target" = "from", "predictor" = "to")
- })
-
- # Data table with network results
- output$network_table <- DT::renderDataTable({
-
- network_data() %>%
- datatable(filter = list(position = 'top'))
- }, server = TRUE) # FALSE to enable download of all pages with button
-
-
- # Download of (filtered) network table
- output$download2 <- downloadHandler(
- filename = function() {
- paste0("network_dexStimMouse_", input$region, ".csv")
- },
- content = function(file) {
- indices <- input$network_table_rows_all
- write.csv(network_data()[indices, ], file)
- }
- )
-
- }
-
- )
-
-}
\ No newline at end of file
diff --git a/06_Shiny/.Rproj.user/94131CCE/sources/per/t/65326343 b/06_Shiny/.Rproj.user/94131CCE/sources/per/t/65326343
deleted file mode 100644
index e5619a0..0000000
--- a/06_Shiny/.Rproj.user/94131CCE/sources/per/t/65326343
+++ /dev/null
@@ -1,21 +0,0 @@
-{
- "id": "65326343",
- "path": "~/Documents/ownCloud/DexStim_RNAseq_Mouse/app/ui.R",
- "project_path": null,
- "type": "r_source",
- "hash": "2781797785",
- "contents": "",
- "dirty": false,
- "created": 1636637158435.0,
- "source_on_save": false,
- "relative_order": 2,
- "properties": {},
- "folds": "",
- "lastKnownWriteTime": 1635167585,
- "encoding": "UTF-8",
- "collab_server": "",
- "source_window": "",
- "last_content_update": 1635167585,
- "read_only": false,
- "read_only_alternatives": []
-}
\ No newline at end of file
diff --git a/06_Shiny/.Rproj.user/94131CCE/sources/per/t/65326343-contents b/06_Shiny/.Rproj.user/94131CCE/sources/per/t/65326343-contents
deleted file mode 100644
index 43ba7e8..0000000
--- a/06_Shiny/.Rproj.user/94131CCE/sources/per/t/65326343-contents
+++ /dev/null
@@ -1,183 +0,0 @@
-
-ui <- fluidPage(theme = shinytheme("flatly"),
-titlePanel("Glucocorticoid receptor regulated gene expression in the mouse brain"),
-navbarPage("Explore!",
-
- tabPanel(icon("home"),
- fluidRow(column(tags$img(src="mousejavi_reversebrain.png",
- width="450px",height="320px"),
- width=3),
- column(
- p(strong("Description of project (e.g. paper abstract)."), "Lorem ipsum dolor sit amet,
- consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna
- aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut
- aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate
- velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non
- proident, sunt in culpa qui officia deserunt mollit anim id est laborum.",
- style="text-align:justify;color:black;background-color:#2980B9;padding:15px;border-radius:10px"),
- br(),
- p(strong("Data and code availability. Reference to paper."), "Sed ut perspiciatis unde omnis iste
- natus error sit voluptatem accusantium doloremque laudantium, totam rem aperiam, eaque ipsa quae
- ab illo inventore veritatis et quasi architecto beatae vitae dicta sunt explicabo. Nemo enim
- ipsam voluptatem quia voluptas sit aspernatur aut odit aut fugit, sed quia consequuntur magni
- dolores eos qui ratione voluptatem sequi nesciunt. Neque porro quisquam est, qui dolorem ipsum
- quia dolor sit amet, consectetur, adipisci velit, sed quia non numquam eius modi tempora incidunt
- ut labore et dolore magnam aliquam quaerat voluptatem. Ut enim ad minima veniam, quis nostrum
- exercitationem ullam corporis suscipit laboriosam, nisi ut aliquid ex ea commodi consequatur?
- Quis autem vel eum iure reprehenderit qui in ea voluptate velit esse quam nihil molestiae
- consequatur, vel illum qui dolorem eum fugiat quo voluptas nulla pariatur?",
- style="text-align:justify;color:black;background-color:#AED6F1;padding:15px;border-radius:10px"),
- width = 7
- ),
- column(
- br(),
- tags$img(src="mpilogo.png", width="250px", height="60px"),
- br(),
- br(),
- p("For more information please visit the website of", em("the MPI of Psychiatry"),
- br(),
- a(href="https://www.psych.mpg.de/", "Here", target="_blank"),
- style="text-align:center;color:black"),
- width=2
- ))),
-
- navbarMenu("Diff. Gene Expression",
- tabPanel("Single Brain Region",
- sidebarLayout(
- sidebarPanel(
- selectInput("region", "Choose a brain region:",
- choices = c("Amygdala" = "AMY",
- "Cerebellum" = "CER",
- "Dorsal DG of Hippocampus" = "dDG",
- "Dorsal CA1 of Hippocampus" = "dCA1",
- "Prefrontal Cortex" = "PFC",
- "PVN of Hypothalamus" = "PVN",
- "Ventral DG of Hippocampus" = "vDG",
- "Ventral CA1 of Hippocampus" = "vCA1")),
- downloadButton("download1","Download (filtered) table as csv"),
- width = 3
- ),
- mainPanel(
- fluidPage(
- br(),
- tags$style(".fa-chart-bar {color:#2980B9}"),
- h3(p(em("Differential Gene Expression "),icon("chart-bar",lib = "font-awesome"),style="color:black;text-align:center")),
- hr(),
- splitLayout(cellWidths = c("55%", "45%"),
- plotlyOutput("volcano_plot"),
- plotlyOutput("exp_plot")),
- DT::dataTableOutput("de_table")
- )
- )
- )
- ),
- tabPanel("Comparison Brain Regions",
- # UI defined in upsetPlot module
- upsetPlotUI("upsetDE")
- )
- ),
-
- navbarMenu(
- "Network Analysis",
- tabPanel(
- "Overview",
- sidebarPanel(
- selectInput(
- "overview_region",
- "Choose a brain region:",
- choices = c(
- "Amygdala" = "AMY",
- "Cerebellum" = "CER",
- "Dorsal DG of Hippocampus" = "dDG",
- "Dorsal CA1 of Hippocampus" = "dCA1",
- "Prefrontal Cortex" = "PFC",
- "PVN of Hypothalamus" = "PVN",
- "Ventral DG of Hippocampus" = "vDG",
- "Ventral CA1 of Hippocampus" = "vCA1"
- )
- ),
- radioButtons(
- "overview_metric",
- label = "Select network metric:",
- choices = list("Nodedegree" = "nodedegrees",
- "Nodebetweenness" = "nodebetweenness"),
- selected = "nodebetweenness"
- ),
- radioButtons(
- "overview_network",
- label = "Select network:",
- choices = list("Baseline" = "baseline",
- "Differential" = "differential"),
- selected = "differential"
- ),
- width = 3
- ),
- mainPanel(
- fluidPage(
- br(),
- tags$style(".fa-project-diagram {color:#2980B9}"),
- h3(p(
- em("Network analysis of gene expression: Overview "),
- icon("project-diagram", lib = "font-awesome"),
- style = "color:black;text-align:center"
- )),
- hr(),
- splitLayout(cellWidths = c("50%", "50%"),
- h4(
- p("Baseline network", style = "color:black;text-align:center")
- ),
- h4(
- p("Differential network", style = "color:black;text-align:center")
- )),
- # plotlyOutput("histogram_network")
- splitLayout(
- cellWidths = c("50%", "50%"),
- plotlyOutput("histogram_network"),
- plotlyOutput("barplot_network")
- )
- # DT::dataTableOutput("network_table")
- )
- )
- ),
-
- tabPanel(
- "Network visualization",
- # UI from networkSingle module
- networkSingleUI("singleVisualization")
- ),
-
- tabPanel(
- "Multi-region network visualization",
- # UI from networkMulti module
- networkMultiUI("multiVisualization")
- )
- ),
-
-
- navbarMenu("More",
- tabPanel("Data download",
- # DT::dataTableOutput("table")
- ),
- tabPanel("About",
- # fluidRow(
- # column(6,
- # includeMarkdown("about.md")
- # ),
- # column(3,
- # img(class="img-polaroid",
- # src=paste0("http://upload.wikimedia.org/",
- # "wikipedia/commons/9/92/",
- # "1919_Ford_Model_T_Highboy_Coupe.jpg")),
- # tags$small(
- # "Source: Photographed at the Bay State Antique ",
- # "Automobile Club's July 10, 2005 show at the ",
- # "Endicott Estate in Dedham, MA by ",
- # a(href="http://commons.wikimedia.org/wiki/User:Sfoskett",
- # "User:Sfoskett")
- # )
- # )
- # )
- )
- )
-)
-)
diff --git a/06_Shiny/.Rproj.user/94131CCE/sources/per/t/951FD869 b/06_Shiny/.Rproj.user/94131CCE/sources/per/t/951FD869
deleted file mode 100644
index a6c3cdb..0000000
--- a/06_Shiny/.Rproj.user/94131CCE/sources/per/t/951FD869
+++ /dev/null
@@ -1,24 +0,0 @@
-{
- "id": "951FD869",
- "path": "~/Documents/ownCloud/DexStim_RNAseq_Mouse/app/global.R",
- "project_path": null,
- "type": "r_source",
- "hash": "2758396765",
- "contents": "",
- "dirty": false,
- "created": 1636637160030.0,
- "source_on_save": false,
- "relative_order": 3,
- "properties": {
- "cursorPosition": "20,29",
- "scrollLine": "0"
- },
- "folds": "",
- "lastKnownWriteTime": 1636637200,
- "encoding": "UTF-8",
- "collab_server": "",
- "source_window": "",
- "last_content_update": 1636637200978,
- "read_only": false,
- "read_only_alternatives": []
-}
\ No newline at end of file
diff --git a/06_Shiny/.Rproj.user/94131CCE/sources/per/t/951FD869-contents b/06_Shiny/.Rproj.user/94131CCE/sources/per/t/951FD869-contents
deleted file mode 100644
index 3ea3837..0000000
--- a/06_Shiny/.Rproj.user/94131CCE/sources/per/t/951FD869-contents
+++ /dev/null
@@ -1,21 +0,0 @@
-library(shiny)
-library(shinythemes)
-library(markdown)
-library(plotly)
-library(DT)
-library(data.table)
-library(stringr)
-library(dplyr)
-library(ggplot2)
-library(igraph)
-library(visNetwork)
-library(shinyWidgets)
-library(UpSetR)
-library(org.Mm.eg.db)
-library(scales)
-library(shinycssloaders)
-library(RColorBrewer)
-
-
-#basepath <- "~/Documents/ownCloud/DexStim_RNAseq_Mouse/"
-source("utilities_network.R")
\ No newline at end of file
diff --git a/06_Shiny/.Rproj.user/94131CCE/sources/per/t/95FC14D0 b/06_Shiny/.Rproj.user/94131CCE/sources/per/t/95FC14D0
deleted file mode 100644
index 95179d0..0000000
--- a/06_Shiny/.Rproj.user/94131CCE/sources/per/t/95FC14D0
+++ /dev/null
@@ -1,24 +0,0 @@
-{
- "id": "95FC14D0",
- "path": "~/Documents/ownCloud/DexStim_RNAseq_Mouse/app/server.R",
- "project_path": null,
- "type": "r_source",
- "hash": "898784622",
- "contents": "",
- "dirty": false,
- "created": 1636637147649.0,
- "source_on_save": false,
- "relative_order": 4,
- "properties": {
- "cursorPosition": "130,56",
- "scrollLine": "120"
- },
- "folds": "",
- "lastKnownWriteTime": 1636637341,
- "encoding": "UTF-8",
- "collab_server": "",
- "source_window": "",
- "last_content_update": 1636637341969,
- "read_only": false,
- "read_only_alternatives": []
-}
\ No newline at end of file
diff --git a/06_Shiny/.Rproj.user/94131CCE/sources/per/t/95FC14D0-contents b/06_Shiny/.Rproj.user/94131CCE/sources/per/t/95FC14D0-contents
deleted file mode 100644
index 192f63d..0000000
--- a/06_Shiny/.Rproj.user/94131CCE/sources/per/t/95FC14D0-contents
+++ /dev/null
@@ -1,189 +0,0 @@
-#source(paste0(basepath, "scripts/06_Shiny/utilities_network.R"))
-#source(paste0(basepath, "scripts/06_Shiny/utilities_de.R"))
-
-# Define server logic required to generate and plot
-server <- shinyServer(function(input, output) {
-
- ### DIFFERENTIAL EXPRESSION ----------------------------------
-
- # DE data set
- de_data <- reactive({
- # read DE table to create volcano plot
- table <- fread(paste0("tables/de/02_",
- input$region,"_deseq2_Dex_1_vs_0_lfcShrink.txt"))
- # table$Gene_Symbol <- mapIds(org.Mm.eg.db, keys = table$V1,
- # keytype = "ENSEMBL", column="SYMBOL")
- table <- table %>%
- dplyr::select(Gene_Symbol, Ensembl_ID:padj) %>%
- dplyr::mutate_at(vars(baseMean, log2FoldChange, lfcSE), ~round(.,4)) %>%
- dplyr::mutate_at(vars(pvalue, padj), ~signif(.,6))
- })
-
-
- # Volcano Plot displaying DE results
- output$volcano_plot <- renderPlotly({
-
- # req(de_data())
-
- # add column that indicates if gene is significant
- indices <- input$de_table_rows_all
- table <- de_data()[indices,] %>%
- dplyr::mutate(sig = as.factor(ifelse(padj <= 0.1, "p_adj <= 0.1", "p_adj > 0.1")))
-
- # volcano plot
- # volcano_plot <- plot_ly(data = table, x = ~log2FoldChange, y = ~-log10(padj),
- # color = ~sig, colors = "Set1",
- # text = ~paste0("Gene: ", V1),
- # source = "volcano_plot") %>%
- # layout(title = paste0("Volcano Plot ", input$region))
- volcano_plot <-
- ggplot(data = table, aes(
- x = log2FoldChange,
- y = -log10(padj),
- col = sig,
- text = paste0("Ensembl_ID: ", Ensembl_ID, "\nGene_Symbol: ", Gene_Symbol)
- )) +
- geom_point() +
- scale_colour_brewer(palette = "Set1") +
- theme_light() +
- theme(plot.title = element_text(size = 11), legend.title = element_blank()) +
- ggtitle(label = paste0("Volcano Plot ", input$region))
- ggplotly(volcano_plot,
- source = "volcano_plot")
- })
-
- # Boxplot displaying norm expression values
- output$exp_plot <- renderPlotly({
-
- # req(de_data())
-
- # access data from click event
- d <- event_data("plotly_click", source = "volcano_plot",
- priority = "event")
- # print(d)
- # don't show anythiny if no point was clicked
- if (is.null(d)) return(NULL)
-
- # identify ensembl ID using the DE data
- ensembl_id <- de_data()[input$de_table_rows_all,]$Ensembl_ID[d$pointNumber + 1] # use pointNumber/rowNumber of clicked point
- gene_symbol <- de_data()[input$de_table_rows_all,]$Gene_Symbol[d$pointNumber + 1]
-
- # read table with normalized expression values
- exp_table <- fread(paste0("tables/de/02_",
- input$region,"_deseq2_expression_vsd.txt"))
-
- # subset to row of clicked gene and reformat
- exp_gene <- data.table::transpose(exp_table[V1 == ensembl_id,2:ncol(exp_table)], keep.names = "condition") %>%
- dplyr::rename("expression" = "V1")
- exp_gene$condition <- str_replace(exp_gene$condition, ".*\\_","")
- exp_gene$mouse_id <- str_extract(exp_gene$condition, pattern = "[0-9]+")
- exp_gene$condition <- as.factor(str_replace(exp_gene$condition, "[0-9]+",""))
-
- # boxplot with jitter points of expression values in CNTRL and DEX
- exp_plot <-
- ggplot(exp_gene, aes(x = condition, y = expression)) +
- geom_boxplot() +
- geom_jitter(color = "black",
- size = 0.4,
- alpha = 0.9,
- aes(text = sprintf('mouse_ID: %s', mouse_id))) +
- # scale_fill_manual(values = c("#1F6D84", "#E6A54D")) +
- theme_light() +
- theme(legend.position = "none",
- plot.title = element_text(size = 11)) +
- ggtitle(paste0("Gene expression of ", gene_symbol, " in ", input$region)) +
- xlab("") +
- ylab("Normalized expression")
- # ggplot to plotly
- ggplotly(exp_plot)
-
- })
-
- # Data table with DE results
- output$de_table <- DT::renderDataTable({
- de_data() %>%
- # dplyr::rename("Ensembl_ID" = "V1") %>%
- datatable(filter = list(position = 'top'))
- }, server = TRUE) # FALSE to enable download of all pages with button
-
- # Download of (filtered) DE results
- output$download1 <- downloadHandler(
- filename = function() {
- paste0("DE_dexStimMouse_", input$region, ".csv")
- },
- content = function(file) {
- indices <- input$de_table_rows_all
- write.csv(de_data()[indices, ], file)
- }
- )
-
- # Upset plot and table from upsetPlot module
- upset_de <- upsetPlotServer("upsetDE")
-
-
- ############# NETWORK ANALYSIS ##############################
-
-
- # Network data (nodedegree/nodebetweenness)
- overview_data <- reactive({
-
- # Read nodedegrees/nodebetweenness
- filename <- list.files(path = paste0("tables/network/",
- "03_AnalysisFuncoup"),
- pattern = paste0("*_", input$overview_region,
- "_funcoup_", input$overview_network, "_",
- input$overview_metric,"Norm_betacutoff0.01.csv"),
- full.names = TRUE)
- data <- fread(file = filename)
-
- })
-
-
- # Histogram network overview
- output$histogram_network <- renderPlotly({
-
- req(overview_data())
-
- # Histogram of nodedegree/nodebetweenness
- if (input$overview_metric == "nodedegrees"){
- histogram_network <- ggplot(overview_data(), aes(x=nodedegree)) +
- geom_histogram()
- } else {
- histogram_network <- ggplot(overview_data(), aes(x=nodebetweenness)) +
- geom_histogram()
- }
- ggplotly(histogram_network)
- })
-
- # Barplot network overview
- output$barplot_network <- renderPlotly({
-
- req(overview_data())
-
- # Histogram of nodedegree/nodebetweenness
- if (input$overview_metric == "nodedegrees"){
- barplot_network <- ggplot(overview_data(), aes(x=nodedegree))
- } else {
- barplot_network <- ggplot(overview_data(), aes(x=nodebetweenness)) +
- geom_histogram()
- }
-
- barplot_network <- barplot_network +
- geom_bar()
- ggplotly(barplot_network)
- })
-
- ### SINGLE REGION NETWORK -------------------------------
-
- # Single region network and table from networkSingle module
- net_single <- networkSingleServer("singleVisualization")
-
-
- ### MULTI REGION NETWORK ---------------------------------------
-
- # Multi region network and table from networkMulti module
- net_multi <- networkMultiServer("multiVisualization")
-
-
-})
-
diff --git a/06_Shiny/.Rproj.user/94131CCE/sources/per/t/EB039850 b/06_Shiny/.Rproj.user/94131CCE/sources/per/t/EB039850
deleted file mode 100644
index df31e4c..0000000
--- a/06_Shiny/.Rproj.user/94131CCE/sources/per/t/EB039850
+++ /dev/null
@@ -1,24 +0,0 @@
-{
- "id": "EB039850",
- "path": "~/Documents/ownCloud/DexStim_RNAseq_Mouse/scripts/06_Shiny/R/upset_plot.R",
- "project_path": "R/upset_plot.R",
- "type": "r_source",
- "hash": "204956232",
- "contents": "",
- "dirty": false,
- "created": 1636637642141.0,
- "source_on_save": false,
- "relative_order": 6,
- "properties": {
- "cursorPosition": "104,46",
- "scrollLine": "92"
- },
- "folds": "",
- "lastKnownWriteTime": 1636637694,
- "encoding": "UTF-8",
- "collab_server": "",
- "source_window": "",
- "last_content_update": 1636637694314,
- "read_only": false,
- "read_only_alternatives": []
-}
\ No newline at end of file
diff --git a/06_Shiny/.Rproj.user/94131CCE/sources/per/t/EB039850-contents b/06_Shiny/.Rproj.user/94131CCE/sources/per/t/EB039850-contents
deleted file mode 100644
index b11b130..0000000
--- a/06_Shiny/.Rproj.user/94131CCE/sources/per/t/EB039850-contents
+++ /dev/null
@@ -1,421 +0,0 @@
-# module UI function
-upsetPlotUI <- function(id, label = "Upset Plot"){
-
- ns <- NS(id)
-
- #tabPanel("Comparison Brain Regions",
- tagList(
- sidebarPanel(
- pickerInput(
- inputId = ns("myPicker"),
- label = "Select brain regions",
- choices = c("Amygdala" = "AMY",
- "Cerebellum" = "CER",
- "Dorsal DG of Hippocampus" = "dDG",
- "Dorsal CA1 of Hippocampus" = "dCA1",
- "Prefrontal Cortex" = "PFC",
- "PVN of Hypothalamus" = "PVN",
- "Ventral DG of Hippocampus" = "vDG",
- "Ventral CA1 of Hippocampus" = "vCA1"),
- options = list(
- `actions-box` = TRUE,
- size = 8,
- `selected-text-format` = "count > 3"
- ),
- multiple = TRUE
- ),
- sliderTextInput(
- inputId = ns("padj_upset"),
- label = "Choose a threshold for FDR p-value:",
- choices = c(0.01, 0.05, 0.1),
- selected = 0.1,
- grid = TRUE
- ),
- sliderTextInput(
- inputId = ns("FC_upset"),
- label = "Choose a threshold for logFC:",
- choices = c(0.0, 0.5, 1.0, 2.0),
- selected = 0.0,
- grid = TRUE
- ),
- sliderInput(
- ns("nintersects"),
- label = "Number of intersections",
- min = 2,
- max = 40,
- step = 1,
- value = 20
- ),
- width = 3
- ),
- mainPanel(
- br(),
- tags$style(".fa-brain {color:#2980B9}"),
- h3(p(em("Comparsion of DE genes across brain regions "),icon("brain",lib = "font-awesome"),style="color:black;text-align:center")),
- hr(),
- tags$style(type="text/css",
- ".shiny-output-error { visibility: hidden; }",
- ".shiny-output-error:before { visibility: hidden; }"
- ),
- # plotlyOutput("upset_de")
- #plotOutput("upset_de"),
- plotlyOutput(ns("plotly_upset"), height = "600px"),
- DT::dataTableOutput(ns("de_comp_table"))
- #h3("Intersection plots")
- ))
-
-}
-
-# module server function
-upsetPlotServer <- function(id) {
- moduleServer(
- id,
-
- function(input, output, session) {
-
- # Read data required for upset plot
- read_upset_data <- function(basepath, regions, padj_cutoff = 0.1, fc_cutoff = 0){
-
- # Read DE tables from all required regions
- list_genes_sig <- list()
-
- for (reg in regions){
- res <- fread(file=paste0("tables/de/02_", reg,
- "_deseq2_Dex_1_vs_0_lfcShrink.txt"),
- sep="\t")
- na_indices <- which(is.na(res$padj))
- res$padj[na_indices] <- 1
- res_sig <- res[res$padj <= padj_cutoff,]
- res_sig <- res_sig[abs(res_sig$log2FoldChange) >= fc_cutoff,]
- list_genes_sig[[reg]] <- res_sig$Ensembl_ID
- }
-
- return(list_genes_sig)
- }
-
- # Read data required for data table below upset plot
- upset_datatable <- function(basepath, regions, padj_cutoff = 0.1, fc_cutoff = 0){
-
- # READ DATA
-
- # Read DE tables from all required regions
- list_reg_sig <- list()
-
- for (reg in regions){
- res <- fread(file=paste0("tables/de/02_", reg,
- "_deseq2_Dex_1_vs_0_lfcShrink.txt"),
- sep="\t")
- na_indices <- which(is.na(res$padj))
- res$padj[na_indices] <- 1
- res_sig <- res[res$padj <= padj_cutoff,]
- res_sig <- res_sig[abs(res_sig$log2FoldChange) >= fc_cutoff,]
- list_reg_sig[[reg]] <- res_sig
- }
-
- df <- bind_rows(list_reg_sig, .id="region")
- df <- df[,c("Ensembl_ID", "Gene_Symbol", "region")] %>%
- group_by(Ensembl_ID, Gene_Symbol) %>%
- dplyr::summarise(region = list(region)) %>%
- dplyr::select(Gene_Symbol, Ensembl_ID, region)
- # df <- df[,c("Ensembl_ID", "Gene_Symbol", "region", "padj", "log2FoldChange")] %>%
- # group_by(Ensembl_ID, Gene_Symbol) %>%
- # dplyr::summarise(region = list(region),
- # p_adjusted = list(padj),
- # log2FC = list(log2FoldChange)) %>%
- # dplyr::select(Gene_Symbol, Ensembl_ID, region, p_adjusted, log2FC)
-
- return(df)
- }
-
-
- # Data table below upset plot
- output$de_comp_table <- DT::renderDataTable({
-
- req(input$myPicker)
- data <- upset_datatable(basepath, input$myPicker, input$padj_upset, input$FC_upset)
- })
-
-
- # UPSET PLOT
-
- # TODO: mark here with a comment (SPDX) that following code snippet is
- # under AGPL license https://github.com/pinin4fjords/shinyngs/blob/master/R/upset.R
- # --> whole repo needs to be AGPL but everything that is my own can be MIT
-
- # Accessor for user-selected sets
-
- getSelectedSetNames <- reactive({
- req(input$myPicker)
- input$myPicker
- })
-
-
- # Get the sets we're going to use based on nsets
-
- getSets <- reactive({
- selected_set_names <- getSelectedSetNames()
- req(length(selected_set_names) > 0)
-
- selected_sets <- read_upset_data(basepath, selected_set_names, input$padj_upset, input$FC_upset)
- })
-
-
- # Accessor for the nintersections parameter
-
- getNintersections <- reactive({
- validate(need(!is.null(input$nintersects), "Waiting for nintersects"))
- input$nintersects
- })
-
- # Calculate intersections between sets
-
- calculateIntersections <- reactive({
- selected_sets <- getSets()
-
- withProgress(message = "Calculating set intersections", value = 0, {
- sets <- getSets()
- nsets <- length(sets)
-
- # Get all possible combinations of sets
-
- combinations <- function(items, pick) {
- x <- combn(items, pick)
- lapply(seq_len(ncol(x)), function(i)
- x[, i])
- }
-
- combos <- lapply(1:nsets, function(x) {
- combinations(1:length(selected_sets), x)
- })
- combos <- lapply(1:nsets, function(x) {
- combinations(1:nsets, x)
- })
-
- # Calculate the intersections of all these combinations
-
- withProgress(message = "Running intersect()", value = 0, {
- intersects <- lapply(combos, function(combonos) {
- lapply(combonos, function(combo) {
- Reduce(intersect, selected_sets[combo])
- })
- })
- })
-
- # For UpSet-ness, membership of higher-order intersections takes priority
- # Otherwise just return the number of entries in each intersection
-
- intersects <- lapply(1:length(intersects), function(i) {
- intersectno <- intersects[[i]]
- if (i != length(intersects)){
- members_in_higher_levels <-
- unlist(intersects[(i + 1):length(intersects)])
- } else {
- members_in_higher_levels <- NULL
- }
- lapply(intersectno, function(intersect) {
- length(setdiff(intersect, members_in_higher_levels))
- })
- })
-
- combos <- unlist(combos, recursive = FALSE)
- intersects <- unlist(intersects)
-
-
- # Sort by intersect size
-
- combos <- combos[order(intersects, decreasing = TRUE)]
- intersects <-
- intersects[order(intersects, decreasing = TRUE)]
- list(combinations = combos, intersections = intersects)
-
- })
-
- })
-
- output$plotly_upset <- renderPlotly({
- grid <- upsetGrid()
- set_size_chart <- upsetSetSizeBarChart()
- intersect_size_chart <- upsetIntersectSizeBarChart()
-
- # add axis labels
- intersect_size_chart <-
- intersect_size_chart %>% layout(yaxis = list(title = "Intersections size"))
-
- set_size_chart <-
- set_size_chart %>% layout(xaxis = list(title = "Set size"),
- yaxis = list(title = "Brain region"))
-
- # subplots
- s1 <-
- subplot(
- plotly_empty(type = "scatter", mode = "markers"),
- set_size_chart,
- nrows = 2,
- # widths = c(0.6, 0.4),
- titleX = TRUE,
- titleY = TRUE
- ) %>% layout(showlegend = FALSE)
- s2 <-
- subplot(intersect_size_chart,
- grid,
- nrows = 2,
- shareX = TRUE, titleY = TRUE) %>% layout(showlegend = FALSE)
-
- subplot(s1, s2, widths = c(0.25, 0.75),
- shareX=F,
- shareY=F,
- titleX=T,
- titleY=T)
-
-
- })
-
- # Add some line returns to contrast names
-
- getSetNames <- reactive({
- selected_sets <- getSets()
- names(selected_sets)
- })
-
- # Make the grid of points indicating set membership in intersections
-
- upsetGrid <- reactive({
- selected_sets <- getSets()
- ints <- calculateIntersections()
-
- intersects <- ints$intersections
- combos <- ints$combinations
-
- # Reduce the maximum number of intersections if we don't have that many
- nintersections <- getNintersections()
- nintersections <- min(nintersections, length(combos))
-
- # Fetch the number of sets
- nsets <- length(getSelectedSetNames())
- setnames <- getSelectedSetNames()
-
- lines <-
- data.table::rbindlist(lapply(1:nintersections, function(combono) {
- data.frame(
- combo = combono,
- x = rep(combono, max(2, length(combos[[combono]]))),
- y = (nsets - combos[[combono]]) + 1,
- name = setnames[combos[[combono]]]
- )
- }))
-
- plot_ly(
- type = "scatter",
- mode = "markers",
- marker = list(color = "lightgrey", size = 8),
- customdata = "grid",
- source = "upset_de"
- ) %>% add_trace(
- type = "scatter",
- x = rep(1:nintersections,
- length(selected_sets)),
- y = unlist(lapply(1:length(selected_sets), function(x)
- rep(x - 0.5, nintersections))),
- hoverinfo = "none"
- ) %>% add_trace(
- type = "scatter",
- data = group_by(lines, combo),
- mode = "lines+markers",
- x = lines$x,
- y = lines$y - 0.5,
- line = list(color = "black", width = 3),
- marker = list(color = "black",
- size = 10),
- hoverinfo = "text",
- text = ~ name
- ) %>% layout(
- xaxis = list(
- showticklabels = FALSE,
- showgrid = FALSE,
- zeroline = FALSE
- ),
- yaxis = list(
- showticklabels = FALSE,
- showgrid = TRUE,
- range = c(0, nsets),
- zeroline = FALSE,
- range = 1:nsets
- ),
- margin = list(t = 0, b = 40)
- )
-
- })
-
- # Make the bar chart illustrating set sizes
-
- upsetSetSizeBarChart <- reactive({
- setnames <- getSelectedSetNames()
- selected_sets <- getSets()
-
- plot_ly(
- x = unlist(lapply(selected_sets, length)),
- y = setnames,
- type = "bar",
- orientation = "h",
- marker = list(color = "black"),
- customdata = "setsize",
- source = "upset_de"
- ) %>% layout(
- bargap = 0.4,
- yaxis = list(
- categoryarray = rev(setnames),
- categoryorder = "array"
- )
- )
- })
-
- # Make the bar chart illustrating intersect size
-
- upsetIntersectSizeBarChart <- reactive({
- ints <- calculateIntersections()
- intersects <- ints$intersections
- combos <- ints$combinations
- nintersections <- getNintersections()
-
- p <-
- plot_ly(showlegend = FALSE,
- customdata = "intersectsize",
- source = "upset_de") %>% add_trace(
- x = 1:nintersections,
- y = unlist(intersects[1:nintersections]),
- type = "bar",
- marker = list(color = "black",
- hoverinfo = "none")
- )
-
- bar_numbers <- TRUE
-
- if (bar_numbers) {
- p <-
- p %>% add_trace(
- type = "scatter",
- mode = "text",
- x = 1:nintersections,
- y = unlist(intersects[1:nintersections]) + (max(intersects) * 0.05),
- text = unlist(intersects[1:nintersections]),
- textfont = list(color = "black")
- )
- }
-
- p
- })
-
- observe({
- click <- event_data("plotly_click", source = "upset_de")
- req(click)
- print(click)
- })
-
- return(NULL)
- }
-
-
-
-
- )
-}
\ No newline at end of file
diff --git a/06_Shiny/.Rproj.user/94131CCE/sources/prop/0C9B8A0A b/06_Shiny/.Rproj.user/94131CCE/sources/prop/0C9B8A0A
deleted file mode 100644
index 9e26dfe..0000000
--- a/06_Shiny/.Rproj.user/94131CCE/sources/prop/0C9B8A0A
+++ /dev/null
@@ -1 +0,0 @@
-{}
\ No newline at end of file
diff --git a/06_Shiny/.Rproj.user/94131CCE/sources/prop/0DDD84F5 b/06_Shiny/.Rproj.user/94131CCE/sources/prop/0DDD84F5
deleted file mode 100644
index 2b1fe59..0000000
--- a/06_Shiny/.Rproj.user/94131CCE/sources/prop/0DDD84F5
+++ /dev/null
@@ -1,4 +0,0 @@
-{
- "cursorPosition": "104,46",
- "scrollLine": "92"
-}
\ No newline at end of file
diff --git a/06_Shiny/.Rproj.user/94131CCE/sources/prop/0E49E7B8 b/06_Shiny/.Rproj.user/94131CCE/sources/prop/0E49E7B8
deleted file mode 100644
index 9e26dfe..0000000
--- a/06_Shiny/.Rproj.user/94131CCE/sources/prop/0E49E7B8
+++ /dev/null
@@ -1 +0,0 @@
-{}
\ No newline at end of file
diff --git a/06_Shiny/.Rproj.user/94131CCE/sources/prop/1532276C b/06_Shiny/.Rproj.user/94131CCE/sources/prop/1532276C
deleted file mode 100644
index 0af093c..0000000
--- a/06_Shiny/.Rproj.user/94131CCE/sources/prop/1532276C
+++ /dev/null
@@ -1,4 +0,0 @@
-{
- "cursorPosition": "129,46",
- "scrollLine": "115"
-}
\ No newline at end of file
diff --git a/06_Shiny/.Rproj.user/94131CCE/sources/prop/15E20D6C b/06_Shiny/.Rproj.user/94131CCE/sources/prop/15E20D6C
deleted file mode 100644
index 9e26dfe..0000000
--- a/06_Shiny/.Rproj.user/94131CCE/sources/prop/15E20D6C
+++ /dev/null
@@ -1 +0,0 @@
-{}
\ No newline at end of file
diff --git a/06_Shiny/.Rproj.user/94131CCE/sources/prop/8BA97ED7 b/06_Shiny/.Rproj.user/94131CCE/sources/prop/8BA97ED7
deleted file mode 100644
index 8096394..0000000
--- a/06_Shiny/.Rproj.user/94131CCE/sources/prop/8BA97ED7
+++ /dev/null
@@ -1,4 +0,0 @@
-{
- "cursorPosition": "20,29",
- "scrollLine": "0"
-}
\ No newline at end of file
diff --git a/06_Shiny/.Rproj.user/94131CCE/sources/prop/8D825508 b/06_Shiny/.Rproj.user/94131CCE/sources/prop/8D825508
deleted file mode 100644
index 5aa7743..0000000
--- a/06_Shiny/.Rproj.user/94131CCE/sources/prop/8D825508
+++ /dev/null
@@ -1,4 +0,0 @@
-{
- "cursorPosition": "124,2",
- "scrollLine": "122"
-}
\ No newline at end of file
diff --git a/06_Shiny/.Rproj.user/94131CCE/sources/prop/9889CFFF b/06_Shiny/.Rproj.user/94131CCE/sources/prop/9889CFFF
deleted file mode 100644
index 6426554..0000000
--- a/06_Shiny/.Rproj.user/94131CCE/sources/prop/9889CFFF
+++ /dev/null
@@ -1,4 +0,0 @@
-{
- "cursorPosition": "219,31",
- "scrollLine": "211"
-}
\ No newline at end of file
diff --git a/06_Shiny/.Rproj.user/94131CCE/sources/prop/CD86E44A b/06_Shiny/.Rproj.user/94131CCE/sources/prop/CD86E44A
deleted file mode 100644
index 0bcef40..0000000
--- a/06_Shiny/.Rproj.user/94131CCE/sources/prop/CD86E44A
+++ /dev/null
@@ -1,4 +0,0 @@
-{
- "cursorPosition": "130,56",
- "scrollLine": "120"
-}
\ No newline at end of file
diff --git a/06_Shiny/.Rproj.user/94131CCE/sources/prop/CFBC27D1 b/06_Shiny/.Rproj.user/94131CCE/sources/prop/CFBC27D1
deleted file mode 100644
index 9e26dfe..0000000
--- a/06_Shiny/.Rproj.user/94131CCE/sources/prop/CFBC27D1
+++ /dev/null
@@ -1 +0,0 @@
-{}
\ No newline at end of file
diff --git a/06_Shiny/.Rproj.user/94131CCE/sources/prop/INDEX b/06_Shiny/.Rproj.user/94131CCE/sources/prop/INDEX
deleted file mode 100644
index c0e95cf..0000000
--- a/06_Shiny/.Rproj.user/94131CCE/sources/prop/INDEX
+++ /dev/null
@@ -1,10 +0,0 @@
-~%2FDocuments%2FownCloud%2FDexStim_RNAseq_Mouse%2Fapp%2Fglobal.R="8BA97ED7"
-~%2FDocuments%2FownCloud%2FDexStim_RNAseq_Mouse%2Fapp%2Fserver.R="CD86E44A"
-~%2FDocuments%2FownCloud%2FDexStim_RNAseq_Mouse%2Fapp%2Fui.R="0E49E7B8"
-~%2FDocuments%2FownCloud%2FDexStim_RNAseq_Mouse%2Fscripts%2F06_Shiny%2FR%2Fnetwork_multiRegion.R="9889CFFF"
-~%2FDocuments%2FownCloud%2FDexStim_RNAseq_Mouse%2Fscripts%2F06_Shiny%2FR%2Fnetwork_singleRegion.R="1532276C"
-~%2FDocuments%2FownCloud%2FDexStim_RNAseq_Mouse%2Fscripts%2F06_Shiny%2FR%2Fupset_plot.R="0DDD84F5"
-~%2FDocuments%2FownCloud%2FDexStim_RNAseq_Mouse%2Fscripts%2F06_Shiny%2Fapp.R="0C9B8A0A"
-~%2FDocuments%2FownCloud%2FDexStim_RNAseq_Mouse%2Fscripts%2F06_Shiny%2Fserver.R="8D825508"
-~%2FDocuments%2FownCloud%2FDexStim_RNAseq_Mouse%2Fscripts%2F06_Shiny%2Fui.R="15E20D6C"
-~%2FDocuments%2FownCloud%2FDexStim_RNAseq_Mouse%2Fscripts%2F06_Shiny%2Fupset_plot.R="CFBC27D1"
diff --git a/06_Shiny/.Rproj.user/shared/notebooks/patch-chunk-names b/06_Shiny/.Rproj.user/shared/notebooks/patch-chunk-names
deleted file mode 100644
index e69de29..0000000
diff --git a/06_Shiny/.Rproj.user/shared/notebooks/paths b/06_Shiny/.Rproj.user/shared/notebooks/paths
deleted file mode 100644
index d9e2a9e..0000000
--- a/06_Shiny/.Rproj.user/shared/notebooks/paths
+++ /dev/null
@@ -1,5 +0,0 @@
-/Users/nathalie_gerstner/Documents/ownCloud/DexStim_RNAseq_Mouse/scripts/06_Shiny/R/network_singleRegion.R="2E73C7DD"
-/Users/nathalie_gerstner/Documents/ownCloud/DexStim_RNAseq_Mouse/scripts/06_Shiny/app.R="1F978958"
-/Users/nathalie_gerstner/Documents/ownCloud/DexStim_RNAseq_Mouse/scripts/06_Shiny/server.R="9E289B0A"
-/Users/nathalie_gerstner/Documents/ownCloud/DexStim_RNAseq_Mouse/scripts/06_Shiny/ui.R="D511866B"
-/Users/nathalie_gerstner/Documents/ownCloud/DexStim_RNAseq_Mouse/scripts/06_Shiny/upset_plot.R="91BB5B50"
diff --git a/06_Shiny/R/.DS_Store b/06_Shiny/R/.DS_Store
deleted file mode 100644
index 5008ddf..0000000
Binary files a/06_Shiny/R/.DS_Store and /dev/null differ
diff --git a/06_Shiny/R/network_multiRegion.R b/06_Shiny/R/network_multiRegion.R
deleted file mode 100644
index 0a2e053..0000000
--- a/06_Shiny/R/network_multiRegion.R
+++ /dev/null
@@ -1,515 +0,0 @@
-# module UI function
-networkMultiUI <- function(id, label = "Network Multiple Regions"){
-
- ns <- NS(id)
-
- tagList(
-
- sidebarPanel(
- pickerInput(
- inputId = ns("network_multireg"),
- label = "Select brain regions",
- choices = c("Amygdala" = "AMY",
- "Cerebellum" = "CER",
- "Dorsal DG of Hippocampus" = "dDG",
- "Dorsal CA1 of Hippocampus" = "dCA1",
- "Prefrontal Cortex" = "PFC",
- "PVN of Hypothalamus" = "PVN",
- "Ventral DG of Hippocampus" = "vDG",
- "Ventral CA1 of Hippocampus" = "vCA1"),
- options = list(
- `actions-box` = TRUE,
- size = 8,
- `selected-text-format` = "count > 3"
- ),
- multiple = TRUE
- ),
- searchInput(
- inputId = ns("network_gene"),
- label = "Enter comma-separated list of genes: ",
- placeholder = "e.g. Nr3c1,Fkbp5",
- btnSearch = icon("search", class = "fas fa-search", lib = "font-awesome"),
- btnReset = icon("remove", class = "fas fa-remove", lib = "font-awesome"),
- width = "100%"
- ),
- fileInput(
- inputId = ns("file1"),
- label = "Upload file with list of genes",
- accept = "text/plain",
- buttonLabel = "Upload",
- placeholder = "Upload file with list of genes"),
- radioButtons(
- ns("network_type"),
- label = "Select type of network to display:",
- choiceNames = list("Baseline", "Differential", "Treatment"),
- choiceValues = list("baseline", "diff", "treatment"),
- selected = "diff"
- ),
- radioButtons(
- ns("vis_neighbours"),
- label = "Include gene neighbourhood",
- choiceNames = c("Yes", "No"),
- choiceValues = c(TRUE, FALSE),
- selected = FALSE
- ),
- radioButtons(
- ns("id_type"),
- label = "Select type of gene ID:",
- choices = list("Ensembl", "Gene Symbol"),
- selected = "Gene Symbol"
- ),
- checkboxGroupInput(
- ns("tableContent"),
- label = "Table content:",
- choices = list(
- "z-scores" = "z",
- "p-values" = "p",
- "beta values" = "beta"
- ),
- selected = "z"
- ),
- downloadButton(ns("download2"),"Download (filtered) table as csv"),
- width = 3
- ),
- mainPanel(
- # visNetwork
- fluidPage(
- br(),
- tags$style(".fa-project-diagram {color:#2980B9}"),
- h3(p(
- em("Network analysis of gene expression "),
- icon("project-diagram", lib = "font-awesome"),
- style = "color:black;text-align:center"
- )),
- hr(),
- h4(span(textOutput(ns("network_type")), style = "color:black;text-align:center")),
- strong("Visualize"),span(" a network in "), strong("multiple brain regions"),
- span(" at baseline, after Dexamethasone treatment or on a differential level.
- Please select the brain regions you are interested in on the left panel.
- You can enter your "), strong("genes of interest"), span(" either as a comma-separated list or upload a file
- with the respective gene IDs in gene symbol or ENSEMBL format. Furthermore, you have the
- option to "), strong("display neighbouring genes"), span(" and the respective connections in addition to
- your genes of interest. Gene IDs can be displayed in the network as gene symbol or
- ENSEMBL id. You have the option to "), strong("filter and download"), span(" the
- network data table."),
- br(),br(),
- visNetworkOutput(ns("network_diff_multi"), height = "600px")
- %>% withSpinner(color="#0dc5c1"),
- DT::dataTableOutput(ns("network_table"))
- )
- )
-
-
- )
-}
-
-
-
-# module server function
-networkMultiServer <- function(id) {
- moduleServer(
- id,
-
- function(input, output, session) {
-
- ### MULTI REGION NETWORK -------------------------------
-
- # Display correct type of network in title
- output$network_type <- renderText({
- v <- c("Baseline Network", "Differential Network", "Treatment Network")
- k <- c("baseline", "diff", "treatment")
- l <- setNames(as.list(v), k)
-
- n <- l[[input$network_type]]
- })
-
- # Load data from multiple brain region
- network_data <- reactive({
-
- req(input$network_multireg)
-
- # Read data tables from all required regions
- list_network <- list()
-
- for (reg in input$network_multireg){
- file_path <- paste0(
- basepath,
- "tables/coExpression_kimono/03_AnalysisFuncoup/",
- "04_singleRegion_",
- reg,
- "_filtered_",
- input$network_type,
- "Network.csv"
- )
- # Read data
- if (input$network_type == "diff") {
- data <-
- fread(file = file_path) %>%
- dplyr::select(target, predictor, beta_mean.base, beta_mean.dex, z, p_adj)
- #dplyr::select(target, predictor, z) %>%
- #dplyr::rename_with(.fn = ~ reg, .cols = z)
-
- cols <- colnames(data)[3:6]
- data <- data %>%
- dplyr::rename_with(.fn = ~paste0(., ".", reg), .cols = all_of(cols) )
- } else {
- data <-
- fread(file = file_path) %>%
- dplyr::select(target, predictor, beta_mean)
- # cols <- colnames(data)[3]
- data <- data %>%
- dplyr::rename_with(.fn = ~ reg, .cols = beta_mean)
- # dplyr::rename_with(.fn = ~paste0(., ".", reg), .cols = all_of(cols) )
- }
- list_network[[reg]] <- data
- }
-
- data_joined <- list_network %>%
- purrr::reduce(full_join, by = c("target","predictor"))
-
- return(data_joined)
- })
-
-
- # #### TEST
- #
- # list_network <- list()
- #
- # for (reg in c("AMY", "CER", "PFC")){
- # file_path <- paste0(
- # basepath,
- # "tables/coExpression_kimono/03_AnalysisFuncoup/",
- # "04_singleRegion_",
- # reg,
- # "_filtered_",
- # "baseline",
- # "Network.csv"
- # )
- # # Read data
- # data <-
- # fread(file = file_path) %>%
- # #dplyr::select(target, predictor, beta_mean.base, beta_mean.dex, z, p_adj)
- # dplyr::select(target, predictor, beta_mean, beta_mean.dex, z, p_adj)
- #
- # cols <- colnames(data)[3:6]
- # #cols <- colnames(data)[3]
- # data <- data %>%
- # dplyr::rename_with(.fn = ~paste0(., ".", reg), .cols = all_of(cols) )
- # list_network[[reg]] <- data
- # }
- #
- # data_joined <- list_network %>%
- # purrr::reduce(full_join, by = c("target","predictor"))
-
-
- # DE genes of required brain regions
- de_genes <- reactive({
-
- list_de <- list()
- # Read and subset DE genes for coloring
- for (reg in input$network_multireg){
- df_de <- fread(paste0(basepath, "tables/02_",
- reg,"_deseq2_Dex_1_vs_0_lfcShrink.txt"))
- na_indices <- which(is.na(df_de$padj))
- df_de$padj[na_indices] <- 1
- df_de <- df_de[df_de$padj <= 0.1,]
-
- df_de <- df_de %>%
- dplyr::select(Ensembl_ID, padj) %>%
- dplyr::rename_with(.fn = ~ reg, .cols = padj)
-
- list_de[[reg]] <- df_de
- }
-
- de_joined <- list_de %>%
- purrr::reduce(full_join, by = c("Ensembl_ID"))
-
- return(de_joined)
- })
-
-
- # hub genes of required brain regions
- hub_genes <- reactive({
-
- list_hub <- list()
- # Read and subset hub genes for coloring
- for (reg in input$network_multireg) {
- df_hub <-
- fread(
- paste0(
- basepath,
- "tables/coExpression_kimono/03_AnalysisFuncoup/04_",
- reg,
- "_funcoup_differential",
- "_nodebetweennessNorm_betacutoff0.01.csv"
- )
- ) %>%
- filter(nodebetweenness_norm >= 1) %>%
- dplyr::select(ensembl_id, nodebetweenness_norm) %>%
- dplyr::rename_with(.fn = ~ reg, .cols = nodebetweenness_norm)
-
- list_hub[[reg]] <- df_hub
- }
-
- hub_joined <- list_hub %>%
- purrr::reduce(full_join, by = c("ensembl_id"))
-
- return(hub_joined)
- })
-
-
- # input genes
- input_genes <- reactive({
-
- if (isTruthy(input$network_gene)){
- genes <- input$network_gene
- genes <- stringr::str_split(genes, ",\\s?")[[1]]
- genes <- reformat_genes(genes)
- } else if (isTruthy(input$file1)){
- genes <- read.csv(input$file1$datapath, header = FALSE)$V1
- genes <- reformat_genes(genes)
- }
- })
-
-
- # bring input genes to correct format
- reformat_genes <- function(list_genes){
- if (!startsWith(list_genes[1], "ENSMUSG")){
- format_gene <- sapply(list_genes, stringr::str_to_title)
- format_gene <- mapIds(org.Mm.eg.db, keys = format_gene, column = "ENSEMBL", keytype = "SYMBOL")
- ids_na <- names(format_gene)[which(is.na(format_gene))]
- #print(ids_na)
- if (length(ids_na) > 0) {
- showNotification(paste("No Ensembl ID found for following genes: ",
- ids_na), type = "message", duration = 5)
- }
- format_gene <- format_gene[which(!is.na(format_gene))]
- } else {
- format_gene <- list_genes
- }
- return(format_gene)
- }
-
-
- # Network visualization
- output$network_diff_multi <- renderVisNetwork({
-
- req(input_genes())
- req(network_data())
- req(de_genes())
- req(hub_genes())
-
- # Get Nodes and Edges
- network <- network(network_data(),
- de_genes(),
- hub_genes(),
- input_genes(),
- input$network_type,
- input$id_type,
- input$vis_neighbours)
-
- visNetwork(network$nodes, network$edges) %>%
- visEdges(arrows = "to") %>%
- visOptions(highlightNearest = list(enabled = T, degree = 1, hover = T)) %>%
- visLegend(addEdges = network$ledges, #addNodes = network$lnodes,
- useGroups = FALSE) %>%
- visIgraphLayout()
- })
-
-
- # Data table with network results
- output$network_table <- DT::renderDataTable({
-
- network_table() %>%
- datatable(filter = list(position = 'top'))
- }, server = TRUE) # FALSE to enable download of all pages with button
-
-
- # Download of (filtered) network table
- output$download2 <- downloadHandler(
- filename = function() {
- paste0("network_dexStimMouse_multiRegion.csv")
- },
- content = function(file) {
- indices <- input$network_table_rows_all
- write.csv(network_table()[indices, ], file)
- }
- )
-
-
- # prepare network for visualization
- network <- function(data, de_genes, hub_genes, input_genes, mode, id_type, neighbours){
-
- # input genes
- print(input_genes)
-
- # filter data
- data <- data %>%
- dplyr::rename(from = predictor, to = target) %>%
- dplyr::filter(from != to)
- data <- subset_network(data, input_genes, neighbours)
-
- # color palettes for edges and nodes
- # edge palette --> assumes that data stores only target, predictor and z scores
- # edge_colors <- brewer.pal(ncol(data)-1, "Blues")[2:(ncol(data)-1)]
-
- # Edges
- if(mode == "baseline" | mode == "treatment"){
- # color palettes for edges and nodes
- edge_colors <- brewer.pal(ncol(data)-1, "Blues")[2:(ncol(data)-1)]
-
- # count regions per diff edge that are not na
- data$edge_reg <- apply(data[,3:ncol(data)], 1, function(x) names(which(!is.na(x))) )
- data$count_reg <- sapply(data$edge_reg, length)
- data$title <- paste0("Connection in: ", sapply(data$edge_reg, paste, collapse = ", "),
- "
")
- # assign color to edges according to number of regions
- data$c <- sapply(data$count_reg, function(x) edge_colors[x])
-
- print(head(data))
- # df for edges
- relations <- data.table(from=data$from,
- to=data$to,
- #beta = data$beta_mean,
- color = c,
- title = data$title)
- # df for edge legend
- ledges <- data.frame(color = edge_colors,
- label = 1:(ncol(data)-6),
- font.align = "top")
- } else {
- # remove columns
- data <- data %>% dplyr::select(-contains("beta"), -contains("p_adj"))
-
- # color palettes for edges and nodes
- # edge palette --> assumes that data stores only target, predictor and z scores
- edge_colors <- brewer.pal(ncol(data)-1, "Blues")[2:(ncol(data)-1)]
-
- # count regions per diff edge that are not na
- data$edge_reg <- apply(data[,3:ncol(data)], 1, function(x) names(which(!is.na(x))) )
- data$count_reg <- sapply(data$edge_reg, length)
- data$title <- paste0("Diff. co-expressed in: ", sapply(data$edge_reg, paste, collapse = ", "),
- "
")
- # assign color to edges according to number of regions
- data$c <- sapply(data$count_reg, function(x) edge_colors[x])
-
- print(head(data))
- # df for edges
- relations <- data.table(from=data$from,
- to=data$to,
- #z = data$z,
- #p_adj = data$p_adj,
- color = data$c,
- title = data$title)
- # df for edge legend
- ledges <- data.frame(color = edge_colors,
- label = 1:(ncol(data)-6),
- font.align = "top")
- }
- print(nrow(relations))
-
-
- # Nodes
- # get unique nodes with correct id
- nodes <- data.frame("id" = unique(union(
- c(relations$from, relations$to),
- input_genes
- )), stringsAsFactors = FALSE)
- if (id_type == "Gene Symbol"){
- print(nodes$id)
- nodes$label <- mapIds(org.Mm.eg.db, keys = nodes$id,
- column = "SYMBOL", keytype = "ENSEMBL")
- } else {
- nodes$label <- nodes$id
- }
-
- # count regions where gene is DE or/and hub
- nodes_de <- left_join(x = nodes, y = de_genes,
- by = c("id" = "Ensembl_ID"))
- nodes_hub <- left_join(x = nodes, y = hub_genes,
- by = c("id" = "ensembl_id"))
-
- nodes$de_reg <- apply(nodes_de[,3:ncol(nodes_de)], 1,
- function(x) names(which(!is.na(x))) )
- nodes$de_count <- sapply(nodes$de_reg, length)
-
- nodes$hub_reg <- apply(nodes_hub[,3:ncol(nodes_hub)], 1,
- function(x) names(which(!is.na(x))) )
- nodes$hub_count <- sapply(nodes$hub_reg, length)
-
- # set color according to DE/hub status
- nodes$color <- rep("darkblue", nrow(nodes_de))
- nodes$color[nodes$de_count > 0] <- "orange"
- nodes$color[nodes$hub_count > 0] <- "darkred"
- nodes$color[nodes$de_count > 0 & nodes$hub_count > 0] <- "purple"
-
- #nodes$opacity <- nodes$de_count + nodes$hub_count
- #nodes$opacity <- nodes$opacity/max(nodes$opacity)
-
- nodes$title <- paste0("",nodes$label,"",
- "
DE in regions: ", sapply(nodes$de_reg, paste, collapse = ", "),
- "
Hub in regions: ", sapply(nodes$hub_reg, paste, collapse = ", "),"
")
-
- print(head(nodes))
-
- # df for node data frame
- lnodes <- data.frame(label = c("DE", "hub", "DE & hub", "no DE & no hub"),
- shape = c( "ellipse"), color = c("orange", "darkred", "purple", "darkblue"),
- title = "Informations", id = 1:4)
-
-
- # return(list("edges" = relations, "nodes" = nodes, "ledges" = ledges, "lnodes" = lnodes))
- return(list("edges" = relations, "nodes" = nodes, "ledges" = ledges))
-
- }
-
-
-
- # prepare network table for visualization
- network_table <- reactive({
-
- req(network_data())
- req(input_genes())
-
- print(input$tableContent)
-
-
- # filter data
- # remove self connections
- data <- network_data() %>%
- dplyr::rename(from = predictor, to = target) %>%
- dplyr::filter(from != to)
- data <- subset_network(data, input_genes(), input$vis_neighbours)
-
- # Network table
- if(input$network_type == "baseline" | input$network_type == "treatment"){
-
- # add beta to column name
- cols <- colnames(data)[3:ncol(data)]
- data <- data %>%
- # dplyr::rename_with(.fn = ~ reg, .cols = beta_mean)
- dplyr::rename_with(.fn = ~paste0("beta.", .), .cols = all_of(cols) )
-
- } else {
-
- if (!("beta" %in% input$tableContent)){
- data <- data %>% dplyr::select(-contains("beta"))
- }
- if (!("z" %in% input$tableContent)){
- data <- data %>% dplyr::select(-contains("z"))
- }
- if(!("p" %in% input$tableContent)){
- data <- data %>% dplyr::select(-contains("p_adj"))
- }
-
- }
-
- data
-
- })
-
-
- }
-
- )
-}
\ No newline at end of file
diff --git a/06_Shiny/R/network_singleRegion.R b/06_Shiny/R/network_singleRegion.R
deleted file mode 100644
index af3b095..0000000
--- a/06_Shiny/R/network_singleRegion.R
+++ /dev/null
@@ -1,260 +0,0 @@
-# module UI function
-networkSingleUI <- function(id, label = "Network Single Region"){
-
- ns <- NS(id)
-
- tagList(
-
- sidebarPanel(
- selectInput(
- ns("network_region"),
- "Choose a brain region:",
- choices = c(
- "Amygdala" = "AMY",
- "Cerebellum" = "CER",
- "Dorsal DG of Hippocampus" = "dDG",
- "Dorsal CA1 of Hippocampus" = "dCA1",
- "Prefrontal Cortex" = "PFC",
- "PVN of Hypothalamus" = "PVN",
- "Ventral DG of Hippocampus" = "vDG",
- "Ventral CA1 of Hippocampus" = "vCA1"
- )
- ),
- searchInput(
- inputId = ns("network_gene"),
- label = "Enter comma-separated list of genes: ",
- placeholder = "e.g. Nr3c1,Fkbp5",
- btnSearch = icon("search", class = "fas fa-search", lib = "font-awesome"),
- btnReset = icon("remove", class = "fas fa-remove", lib = "font-awesome"),
- width = "100%"
- ),
- fileInput(
- inputId = ns("file1"),
- label = "Upload file with list of genes",
- accept = "text/plain",
- buttonLabel = "Upload",
- placeholder = "Upload file with list of genes"),
- radioButtons(
- ns("network_type"),
- label = "Select type of network to display:",
- choiceNames = list("Baseline", "Differential", "Treatment"),
- choiceValues = list("baseline", "diff", "treatment"),
- selected = "diff"
- ),
- radioButtons(
- ns("vis_neighbours"),
- label = "Include gene neighbourhood",
- choiceNames = c("Yes", "No"),
- choiceValues = c(TRUE, FALSE),
- selected = FALSE
- ),
- radioButtons(
- ns("id_type"),
- label = "Select type of gene ID for plot:",
- choices = list("Ensembl", "Gene Symbol"),
- selected = "Gene Symbol"
- ),
- downloadButton(ns("download2"),"Download (filtered) table as csv"),
- width = 3
- ),
- mainPanel(
- # visNetwork
- fluidPage(
- br(),
- tags$style(".fa-project-diagram {color:#2980B9}"),
- h3(p(
- em("Network analysis of gene expression "),
- icon("project-diagram", lib = "font-awesome"),
- style = "color:black;text-align:center"
- )),
- hr(),
- h4(span(textOutput(ns("network_type")), style = "color:black;text-align:center")),
- strong("Visualize"),span(" a network in a "), strong("single brain region"),
- span(" at baseline, after Dexamethasone treatment or on a differential level.
- Please choose the brain region you are interested in on the left panel.
- You can enter your "), strong("genes of interest"), span(" either as a comma-separated list or upload a file
- with the respective gene IDs in gene symbol or ENSEMBL format. Furthermore, you have the
- option to "), strong("display neighbouring genes"), span(" and the respective connections in addition to
- your genes of interest. Gene IDs can be displayed in the network as gene symbol or
- ENSEMBL id. You have the option to "), strong("filter and download"), span(" the
- network data table."),
- br(),br(),
- visNetworkOutput(ns("network"), height = "600px") %>%
- withSpinner(color="#0dc5c1"),
- DT::dataTableOutput(ns("network_table"))
- )
- )
-
- )
-
-}
-
-
-
-# module server function
-networkSingleServer <- function(id) {
- moduleServer(
- id,
-
- function(input, output, session) {
-
- ### SINGLE REGION NETWORK -------------------------------
-
- # Display correct type of network in title
- output$network_type <- renderText({
- v <- c("Baseline Network", "Differential Network", "Treatment Network")
- k <- c("baseline", "diff", "treatment")
- l <- setNames(as.list(v), k)
-
- n <- l[[input$network_type]]
- })
-
- # Load data from one brain region
- network_df <- reactive({
- file_path <- paste0(
- basepath,
- "tables/coExpression_kimono/03_AnalysisFuncoup/",
- "04_singleRegion_",
- input$network_region,
- "_filtered_",
- input$network_type,
- "Network.csv"
- )
- # Read data
- if (input$network_type == "diff") {
- data <-
- fread(file = file_path) %>%
- dplyr::select(target, predictor, beta_mean.base, beta_mean.dex, z, p_adj)
- } else {
- data <-
- fread(file = file_path) %>%
- dplyr::select(target, predictor, beta_mean)
- }
-
- })
-
- # DE genes of region
- de_genes <- reactive({
- # Read and subset DE genes for coloring
- df_de <- fread(paste0(basepath, "tables/02_",
- input$network_region,"_deseq2_Dex_1_vs_0_lfcShrink.txt"))
- na_indices <- which(is.na(df_de$padj))
- df_de$padj[na_indices] <- 1
- df_de <- df_de[df_de$padj <= 0.1,]
- df_de$Ensembl_ID
- })
-
- # hub genes of region
- hub_genes <- reactive({
- # Read and subset hub genes for coloring
- df_hub <- fread(paste0(basepath, "tables/coExpression_kimono/03_AnalysisFuncoup/04_",
- input$network_region,"_funcoup_differential",
- "_nodebetweennessNorm_betacutoff0.01.csv")) %>%
- filter(nodebetweenness_norm >= 1)
- df_hub$ensembl_id
- })
-
-
- # input genes
- input_genes <- reactive({
-
- if (isTruthy(input$network_gene)){
- genes <- input$network_gene
- genes <- stringr::str_split(genes, ",\\s?")[[1]]
- genes <- reformat_genes(genes)
- } else if (isTruthy(input$file1)){
- genes <- read.csv(input$file1$datapath, header = FALSE)$V1
- genes <- reformat_genes(genes)
- }
- #genes
- })
-
-
- # bring input genes to correct format
- reformat_genes <- function(list_genes){
- if (!startsWith(list_genes[1], "ENSMUSG")){
- format_gene <- sapply(list_genes, stringr::str_to_title)
- format_gene <- mapIds(org.Mm.eg.db, keys = format_gene, column = "ENSEMBL", keytype = "SYMBOL")
- } else {
- format_gene <- list_genes
- }
- return(format_gene)
- }
-
-
- # Network visualization
- output$network <- renderVisNetwork({
-
- req(input_genes())
- req(network_df())
- req(de_genes())
- req(hub_genes())
-
- # Get Nodes and Edges
- network <- read_network(network_df(),
- de_genes(),
- hub_genes(),
- input_genes(),
- input$network_type,
- input$id_type,
- input$vis_neighbours)
-
- visNetwork(network$nodes, network$edges) %>%
- visEdges(arrows = "to") %>%
- visOptions(highlightNearest = TRUE) %>%
- visLegend(addEdges = network$ledges, addNodes = network$lnodes,
- useGroups = FALSE)
- #visIgraphLayout()
- })
-
- # network table
- network_data <- reactive({
-
- req(input_genes())
- req(network_df())
-
- # input genes
- gene <- input_genes()
-
- # Get data and subset to neighbours of gene of interest
- data <- network_df() %>%
- dplyr::rename("from" = "target", "to" = "predictor")
- data <- simplify_network_df(data)
- data <- subset_network(data, gene, input$vis_neighbours)
-
- # ID type to gene symbol
- if(input$id_type == "Gene Symbol"){
- data$from <- mapIds(org.Mm.eg.db, keys = data$from,
- column = "SYMBOL", keytype = "ENSEMBL")
- data$to <- mapIds(org.Mm.eg.db, keys = data$to,
- column = "SYMBOL", keytype = "ENSEMBL")
- }
-
- data %>%
- dplyr::rename("target" = "from", "predictor" = "to")
- })
-
- # Data table with network results
- output$network_table <- DT::renderDataTable({
-
- network_data() %>%
- datatable(filter = list(position = 'top'))
- }, server = TRUE) # FALSE to enable download of all pages with button
-
-
- # Download of (filtered) network table
- output$download2 <- downloadHandler(
- filename = function() {
- paste0("network_dexStimMouse_", input$region, ".csv")
- },
- content = function(file) {
- indices <- input$network_table_rows_all
- write.csv(network_data()[indices, ], file)
- }
- )
-
- }
-
- )
-
-}
\ No newline at end of file
diff --git a/06_Shiny/R/upsetPlot_hub.R b/06_Shiny/R/upsetPlot_hub.R
deleted file mode 100644
index a4ac022..0000000
--- a/06_Shiny/R/upsetPlot_hub.R
+++ /dev/null
@@ -1,448 +0,0 @@
-# module UI function
-comparisonHubGenesUI <- function(id, label = "Upset Plot Hub Genes"){
-
- ns <- NS(id)
-
- #tabPanel("Comparison Brain Regions",
- tagList(
- sidebarPanel(
- pickerInput(
- inputId = ns("myPicker"),
- label = "Select brain regions",
- choices = c("Amygdala" = "AMY",
- "Cerebellum" = "CER",
- "Dorsal DG of Hippocampus" = "dDG",
- "Dorsal CA1 of Hippocampus" = "dCA1",
- "Prefrontal Cortex" = "PFC",
- "PVN of Hypothalamus" = "PVN",
- "Ventral DG of Hippocampus" = "vDG",
- "Ventral CA1 of Hippocampus" = "vCA1"),
- options = list(
- `actions-box` = TRUE,
- size = 8,
- `selected-text-format` = "count > 3"
- ),
- multiple = TRUE
- ),
- radioButtons(
- ns("network_type"),
- label = "Select type of network to display:",
- choiceNames = list("Baseline", "Differential", "Treatment"),
- choiceValues = list("baseline", "differential", "treatment"),
- selected = "differential"
- ),
- sliderInput(
- inputId = ns("normBetween"),
- label = "Choose a threshold for the normalized nodebetweenness:",
- min = 0.5,
- max = 1.5,
- step = 0.05,
- value = 1.0
- ),
- sliderInput(
- ns("nintersects"),
- label = "Number of intersections",
- min = 2,
- max = 40,
- step = 1,
- value = 20
- ),
- downloadButton(ns("download"),"Download (filtered) table as csv"),
- width = 3
- ),
- mainPanel(
- br(),
- tags$style(".fa-brain {color:#2980B9}"),
- h3(p(em("Comparsion of hub genes across brain regions "),icon("brain",lib = "font-awesome"),style="color:black;text-align:center")),
- hr(),
-
- tags$style(type="text/css",
- ".shiny-output-error { visibility: hidden; }",
- ".shiny-output-error:before { visibility: hidden; }"
- ),
- strong("Compare"),span(" hub genes either at baseline, after Dexamethasone treatment or at the differential level"),
- strong(" between different brain regions"), span(". Please choose the brain regions you are interested
- in on the left panel. You can also select a different threshold for the normalized
- nodebetweenness. The table on the bottom displays all genes in the dataset with the brain regions
- they are hub genes in."),
- br(),br(),
- plotlyOutput(ns("plotly_upset"), height = "600px"),
- DT::dataTableOutput(ns("upset_table"))
- #h3("Intersection plots")
- ))
-
-}
-
-# module server function
-comparisonHubGenesServer <- function(id) {
- moduleServer(
- id,
-
- function(input, output, session) {
-
- # Read data required for upset plot
- read_upset_data <- function(basepath, regions, between_cutoff = 1.0){
-
- # Read DE tables from all required regions
- list_genes_sig <- list()
-
- for (reg in regions){
- f <- Sys.glob(file.path(paste0(basepath, "tables/coExpression_kimono/",
- "03_AnalysisFuncoup/"),
- paste0("*_", reg, "_funcoup_", input$network_type,
- "_nodebetweennessNorm_betacutoff0.01.csv")))
- res <- fread(file=f, sep=",")
- #na_indices <- which(is.na(res$padj))
- #res$padj[na_indices] <- 1
- res_sig <- res[res$nodebetweenness_norm >= between_cutoff,]
- list_genes_sig[[reg]] <- res_sig$ensembl_id
- }
-
- return(list_genes_sig)
- }
-
- # Read data required for data table below upset plot
- upset_datatable <- function(basepath, regions, between_cutoff = 1.0){
-
- # READ DATA
-
- # Read DE tables from all required regions
- list_reg_sig <- list()
-
- for (reg in regions){
- f <- Sys.glob(file.path(paste0(basepath, "tables/coExpression_kimono/",
- "03_AnalysisFuncoup/"),
- paste0("*_", reg, "_funcoup_", input$network_type,
- "_nodebetweennessNorm_betacutoff0.01.csv")))
- res <- fread(file=f, sep=",")
- #na_indices <- which(is.na(res$padj))
- #res$padj[na_indices] <- 1
- res_sig <- res[res$nodebetweenness_norm >= between_cutoff,]
- list_reg_sig[[reg]] <- res_sig
- }
-
- df <- bind_rows(list_reg_sig, .id="region")
- df <- df[,c("ensembl_id", "region")] %>%
- group_by(ensembl_id) %>%
- dplyr::summarise(region = list(region)) %>%
- dplyr::select(ensembl_id, region)
- df$gene_symbol <- mapIds(org.Mm.eg.db, keys = df$ensembl_id,
- column = "SYMBOL", keytype = "ENSEMBL")
- df <- dplyr::select(df, gene_symbol, ensembl_id, region)
-
- return(df)
- }
-
-
- # reactive table
- upset_data <- reactive({
-
- req(input$myPicker)
- data <- upset_datatable(basepath, input$myPicker, input$normBetween) %>%
- mutate(region = sapply(region, toString))
- data
-
- })
-
-
- # Data table below upset plot
- output$upset_table <- DT::renderDataTable({
-
- upset_data() %>%
- datatable(filter = list(position = 'top'))
- }, server = TRUE)
-
- # Download of (filtered) network table
- output$download <- downloadHandler(
- filename = "hubGenes.csv",
- content = function(file) {
- indices <- input$upset_table_rows_all
- print(upset_data())
- write.csv(upset_data()[indices, ], file)
- }
- )
-
-
- # UPSET PLOT
-
- # TODO: mark here with a comment (SPDX) that following code snippet is
- # under AGPL license https://github.com/pinin4fjords/shinyngs/blob/master/R/upset.R
- # --> whole repo needs to be AGPL but everything that is my own can be MIT
-
- # Accessor for user-selected sets
-
- getSelectedSetNames <- reactive({
- req(input$myPicker)
- input$myPicker
- })
-
-
- # Get the sets we're going to use based on nsets
-
- getSets <- reactive({
- selected_set_names <- getSelectedSetNames()
- req(length(selected_set_names) > 0)
-
- selected_sets <- read_upset_data(basepath, selected_set_names, input$normBetween)
- })
-
-
- # Accessor for the nintersections parameter
-
- getNintersections <- reactive({
- validate(need(!is.null(input$nintersects), "Waiting for nintersects"))
- input$nintersects
- })
-
- # Calculate intersections between sets
-
- calculateIntersections <- reactive({
- selected_sets <- getSets()
-
- withProgress(message = "Calculating set intersections", value = 0, {
- sets <- getSets()
- nsets <- length(sets)
-
- # Get all possible combinations of sets
-
- combinations <- function(items, pick) {
- x <- combn(items, pick)
- lapply(seq_len(ncol(x)), function(i)
- x[, i])
- }
-
- combos <- lapply(1:nsets, function(x) {
- combinations(1:length(selected_sets), x)
- })
- combos <- lapply(1:nsets, function(x) {
- combinations(1:nsets, x)
- })
-
- # Calculate the intersections of all these combinations
-
- withProgress(message = "Running intersect()", value = 0, {
- intersects <- lapply(combos, function(combonos) {
- lapply(combonos, function(combo) {
- Reduce(intersect, selected_sets[combo])
- })
- })
- })
-
- # For UpSet-ness, membership of higher-order intersections takes priority
- # Otherwise just return the number of entries in each intersection
-
- intersects <- lapply(1:length(intersects), function(i) {
- intersectno <- intersects[[i]]
- if (i != length(intersects)){
- members_in_higher_levels <-
- unlist(intersects[(i + 1):length(intersects)])
- } else {
- members_in_higher_levels <- NULL
- }
- lapply(intersectno, function(intersect) {
- length(setdiff(intersect, members_in_higher_levels))
- })
- })
-
- combos <- unlist(combos, recursive = FALSE)
- intersects <- unlist(intersects)
-
-
- # Sort by intersect size
-
- combos <- combos[order(intersects, decreasing = TRUE)]
- intersects <-
- intersects[order(intersects, decreasing = TRUE)]
- list(combinations = combos, intersections = intersects)
-
- })
-
- })
-
- output$plotly_upset <- renderPlotly({
- grid <- upsetGrid()
- set_size_chart <- upsetSetSizeBarChart()
- intersect_size_chart <- upsetIntersectSizeBarChart()
-
- # add axis labels
- intersect_size_chart <-
- intersect_size_chart %>% layout(yaxis = list(title = "Intersections size"))
-
- set_size_chart <-
- set_size_chart %>% layout(xaxis = list(title = "Set size"),
- yaxis = list(title = "Brain region"))
-
- # subplots
- s1 <-
- subplot(
- plotly_empty(type = "scatter", mode = "markers"),
- set_size_chart,
- nrows = 2,
- # widths = c(0.6, 0.4),
- titleX = TRUE,
- titleY = TRUE
- ) %>% layout(showlegend = FALSE)
- s2 <-
- subplot(intersect_size_chart,
- grid,
- nrows = 2,
- shareX = TRUE, titleY = TRUE) %>% layout(showlegend = FALSE)
-
- subplot(s1, s2, widths = c(0.25, 0.75),
- shareX=F,
- shareY=F,
- titleX=T,
- titleY=T)
-
-
- })
-
- # Add some line returns to contrast names
-
- getSetNames <- reactive({
- selected_sets <- getSets()
- names(selected_sets)
- })
-
- # Make the grid of points indicating set membership in intersections
-
- upsetGrid <- reactive({
- selected_sets <- getSets()
- ints <- calculateIntersections()
-
- intersects <- ints$intersections
- combos <- ints$combinations
-
- # Reduce the maximum number of intersections if we don't have that many
- nintersections <- getNintersections()
- nintersections <- min(nintersections, length(combos))
-
- # Fetch the number of sets
- nsets <- length(getSelectedSetNames())
- setnames <- getSelectedSetNames()
-
- lines <-
- data.table::rbindlist(lapply(1:nintersections, function(combono) {
- data.frame(
- combo = combono,
- x = rep(combono, max(2, length(combos[[combono]]))),
- y = (nsets - combos[[combono]]) + 1,
- name = setnames[combos[[combono]]]
- )
- }))
-
- plot_ly(
- type = "scatter",
- mode = "markers",
- marker = list(color = "lightgrey", size = 8),
- customdata = "grid",
- source = "upset_hub"
- ) %>% add_trace(
- type = "scatter",
- x = rep(1:nintersections,
- length(selected_sets)),
- y = unlist(lapply(1:length(selected_sets), function(x)
- rep(x - 0.5, nintersections))),
- hoverinfo = "none"
- ) %>% add_trace(
- type = "scatter",
- data = group_by(lines, combo),
- mode = "lines+markers",
- x = lines$x,
- y = lines$y - 0.5,
- line = list(color = "black", width = 3),
- marker = list(color = "black",
- size = 10),
- hoverinfo = "text",
- text = ~ name
- ) %>% layout(
- xaxis = list(
- showticklabels = FALSE,
- showgrid = FALSE,
- zeroline = FALSE
- ),
- yaxis = list(
- showticklabels = FALSE,
- showgrid = TRUE,
- range = c(0, nsets),
- zeroline = FALSE,
- range = 1:nsets
- ),
- margin = list(t = 0, b = 40)
- )
-
- })
-
- # Make the bar chart illustrating set sizes
-
- upsetSetSizeBarChart <- reactive({
- setnames <- getSelectedSetNames()
- selected_sets <- getSets()
-
- plot_ly(
- x = unlist(lapply(selected_sets, length)),
- y = setnames,
- type = "bar",
- orientation = "h",
- marker = list(color = "black"),
- customdata = "setsize",
- source = "upset_hub"
- ) %>% layout(
- bargap = 0.4,
- yaxis = list(
- categoryarray = rev(setnames),
- categoryorder = "array"
- )
- )
- })
-
- # Make the bar chart illustrating intersect size
-
- upsetIntersectSizeBarChart <- reactive({
- ints <- calculateIntersections()
- intersects <- ints$intersections
- combos <- ints$combinations
- nintersections <- getNintersections()
-
- p <-
- plot_ly(showlegend = FALSE,
- customdata = "intersectsize",
- source = "upset_hub") %>% add_trace(
- x = 1:nintersections,
- y = unlist(intersects[1:nintersections]),
- type = "bar",
- marker = list(color = "black",
- hoverinfo = "none")
- )
-
- bar_numbers <- TRUE
-
- if (bar_numbers) {
- p <-
- p %>% add_trace(
- type = "scatter",
- mode = "text",
- x = 1:nintersections,
- y = unlist(intersects[1:nintersections]) + (max(intersects) * 0.05),
- text = unlist(intersects[1:nintersections]),
- textfont = list(color = "black")
- )
- }
-
- p
- })
-
- observe({
- click <- event_data("plotly_click", source = "upset_hub")
- req(click)
- print(click)
- })
-
- return(NULL)
- }
-
-
-
-
- )
-}
\ No newline at end of file
diff --git a/06_Shiny/R/upset_plot.R b/06_Shiny/R/upset_plot.R
deleted file mode 100644
index a190690..0000000
--- a/06_Shiny/R/upset_plot.R
+++ /dev/null
@@ -1,454 +0,0 @@
-# module UI function
-upsetPlotUI <- function(id, label = "Upset Plot"){
-
- ns <- NS(id)
-
- #tabPanel("Comparison Brain Regions",
- tagList(
- sidebarPanel(
- pickerInput(
- inputId = ns("myPicker"),
- label = "Select brain regions",
- choices = c("Amygdala" = "AMY",
- "Cerebellum" = "CER",
- "Dorsal DG of Hippocampus" = "dDG",
- "Dorsal CA1 of Hippocampus" = "dCA1",
- "Prefrontal Cortex" = "PFC",
- "PVN of Hypothalamus" = "PVN",
- "Ventral DG of Hippocampus" = "vDG",
- "Ventral CA1 of Hippocampus" = "vCA1"),
- options = list(
- `actions-box` = TRUE,
- size = 8,
- `selected-text-format` = "count > 3"
- ),
- multiple = TRUE
- ),
- sliderTextInput(
- inputId = ns("padj_upset"),
- label = "Choose a threshold for FDR p-value:",
- choices = c(0.01, 0.05, 0.1),
- selected = 0.1,
- grid = TRUE
- ),
- sliderTextInput(
- inputId = ns("FC_upset"),
- label = "Choose a threshold for logFC:",
- choices = c(0.0, 0.5, 1.0, 2.0),
- selected = 0.0,
- grid = TRUE
- ),
- sliderInput(
- ns("nintersects"),
- label = "Number of intersections",
- min = 2,
- max = 40,
- step = 1,
- value = 20
- ),
- downloadButton(ns("download"),"Download (filtered) table as csv"),
- width = 3
- ),
- mainPanel(
- br(),
- tags$style(".fa-brain {color:#2980B9}"),
- h3(p(em("Comparsion of DE genes across brain regions "),icon("brain",lib = "font-awesome"),style="color:black;text-align:center")),
- hr(),
- tags$style(type="text/css",
- ".shiny-output-error { visibility: hidden; }",
- ".shiny-output-error:before { visibility: hidden; }"
- ),
- strong("Compare"),span(" the differentially expressed (DE) genes after Dexamethasone treatment"),
- strong(" between different brain regions"), span(". Please choose the brain regions you are interested
- in on the left panel. You can also select a different FDR p-value threshold and a different fold
- change cutoff. The table on the bottom displays all genes in the dataset with the brain regions
- they are differentially expressed in."),
- br(),br(),
- # plotlyOutput("upset_de")
- #plotOutput("upset_de"),
- plotlyOutput(ns("plotly_upset"), height = "600px"),
- DT::dataTableOutput(ns("upset_table"))
- #h3("Intersection plots")
- ))
-
-}
-
-# module server function
-upsetPlotServer <- function(id) {
- moduleServer(
- id,
-
- function(input, output, session) {
-
- # Read data required for upset plot
- read_upset_data <- function(basepath, regions, padj_cutoff = 0.1, fc_cutoff = 0){
-
- # Read DE tables from all required regions
- list_genes_sig <- list()
-
- for (reg in regions){
- res <- fread(file=paste0(basepath, "tables/02_", reg,
- "_deseq2_Dex_1_vs_0_lfcShrink.txt"),
- sep="\t")
- na_indices <- which(is.na(res$padj))
- res$padj[na_indices] <- 1
- res_sig <- res[res$padj <= padj_cutoff,]
- res_sig <- res_sig[abs(res_sig$log2FoldChange) >= fc_cutoff,]
- list_genes_sig[[reg]] <- res_sig$Ensembl_ID
- }
-
- return(list_genes_sig)
- }
-
- # Read data required for data table below upset plot
- upset_datatable <- function(basepath, regions, padj_cutoff = 0.1, fc_cutoff = 0){
-
- # READ DATA
-
- # Read DE tables from all required regions
- list_reg_sig <- list()
-
- for (reg in regions){
- res <- fread(file=paste0(basepath, "tables/02_", reg,
- "_deseq2_Dex_1_vs_0_lfcShrink.txt"),
- sep="\t")
- na_indices <- which(is.na(res$padj))
- res$padj[na_indices] <- 1
- res_sig <- res[res$padj <= padj_cutoff,]
- res_sig <- res_sig[abs(res_sig$log2FoldChange) >= fc_cutoff,]
- list_reg_sig[[reg]] <- res_sig
- }
-
- df <- bind_rows(list_reg_sig, .id="region")
- df <- df[,c("Ensembl_ID", "region")] %>%
- group_by(Ensembl_ID) %>%
- dplyr::summarise(region = list(region)) %>%
- dplyr::select(Ensembl_ID, region) %>%
- dplyr::distinct(Ensembl_ID, .keep_all = TRUE)
- df$Gene_Symbol <- mapIds(org.Mm.eg.db, keys = df$Ensembl_ID,
- column = "SYMBOL", keytype = "ENSEMBL")
- df <- dplyr::select(df, Gene_Symbol, Ensembl_ID, region)
-
- # df <- df[,c("Ensembl_ID", "Gene_Symbol", "region", "padj", "log2FoldChange")] %>%
- # group_by(Ensembl_ID, Gene_Symbol) %>%
- # dplyr::summarise(region = list(region),
- # p_adjusted = list(padj),
- # log2FC = list(log2FoldChange)) %>%
- # dplyr::select(Gene_Symbol, Ensembl_ID, region, p_adjusted, log2FC)
-
- return(df)
- }
-
- # reactive table
- upset_data <- reactive({
-
- req(input$myPicker)
- data <- upset_datatable(basepath, input$myPicker, input$padj_upset, input$FC_upset) %>%
- mutate(region = sapply(region, toString))
- data
-
- })
-
-
- # Data table below upset plot
- output$upset_table <- DT::renderDataTable({
-
- upset_data() %>%
- datatable(filter = list(position = 'top'))
- }, server = TRUE)
-
- # Download of (filtered) network table
- output$download <- downloadHandler(
- filename = "DEGenes.csv",
- content = function(file) {
- indices <- input$upset_table_rows_all
- print(upset_data())
- write.csv(upset_data()[indices, ], file)
- }
- )
-
-
-
- # UPSET PLOT
-
- # TODO: mark here with a comment (SPDX) that following code snippet is
- # under AGPL license https://github.com/pinin4fjords/shinyngs/blob/master/R/upset.R
- # --> whole repo needs to be AGPL but everything that is my own can be MIT
-
- # Accessor for user-selected sets
-
- getSelectedSetNames <- reactive({
- req(input$myPicker)
- input$myPicker
- })
-
-
- # Get the sets we're going to use based on nsets
-
- getSets <- reactive({
- selected_set_names <- getSelectedSetNames()
- req(length(selected_set_names) > 0)
-
- selected_sets <- read_upset_data(basepath, selected_set_names, input$padj_upset, input$FC_upset)
- })
-
-
- # Accessor for the nintersections parameter
-
- getNintersections <- reactive({
- validate(need(!is.null(input$nintersects), "Waiting for nintersects"))
- input$nintersects
- })
-
- # Calculate intersections between sets
-
- calculateIntersections <- reactive({
- selected_sets <- getSets()
-
- withProgress(message = "Calculating set intersections", value = 0, {
- sets <- getSets()
- nsets <- length(sets)
-
- # Get all possible combinations of sets
-
- combinations <- function(items, pick) {
- x <- combn(items, pick)
- lapply(seq_len(ncol(x)), function(i)
- x[, i])
- }
-
- combos <- lapply(1:nsets, function(x) {
- combinations(1:length(selected_sets), x)
- })
- combos <- lapply(1:nsets, function(x) {
- combinations(1:nsets, x)
- })
-
- # Calculate the intersections of all these combinations
-
- withProgress(message = "Running intersect()", value = 0, {
- intersects <- lapply(combos, function(combonos) {
- lapply(combonos, function(combo) {
- Reduce(intersect, selected_sets[combo])
- })
- })
- })
-
- # For UpSet-ness, membership of higher-order intersections takes priority
- # Otherwise just return the number of entries in each intersection
-
- intersects <- lapply(1:length(intersects), function(i) {
- intersectno <- intersects[[i]]
- if (i != length(intersects)){
- members_in_higher_levels <-
- unlist(intersects[(i + 1):length(intersects)])
- } else {
- members_in_higher_levels <- NULL
- }
- lapply(intersectno, function(intersect) {
- length(setdiff(intersect, members_in_higher_levels))
- })
- })
-
- combos <- unlist(combos, recursive = FALSE)
- intersects <- unlist(intersects)
-
-
- # Sort by intersect size
-
- combos <- combos[order(intersects, decreasing = TRUE)]
- intersects <-
- intersects[order(intersects, decreasing = TRUE)]
- list(combinations = combos, intersections = intersects)
-
- })
-
- })
-
- output$plotly_upset <- renderPlotly({
- grid <- upsetGrid()
- set_size_chart <- upsetSetSizeBarChart()
- intersect_size_chart <- upsetIntersectSizeBarChart()
-
- # add axis labels
- intersect_size_chart <-
- intersect_size_chart %>% layout(yaxis = list(title = "Intersections size"))
-
- set_size_chart <-
- set_size_chart %>% layout(xaxis = list(title = "Set size"),
- yaxis = list(title = "Brain region"))
-
- # subplots
- s1 <-
- subplot(
- plotly_empty(type = "scatter", mode = "markers"),
- set_size_chart,
- nrows = 2,
- # widths = c(0.6, 0.4),
- titleX = TRUE,
- titleY = TRUE
- ) %>% layout(showlegend = FALSE)
- s2 <-
- subplot(intersect_size_chart,
- grid,
- nrows = 2,
- shareX = TRUE, titleY = TRUE) %>% layout(showlegend = FALSE)
-
- subplot(s1, s2, widths = c(0.25, 0.75),
- shareX=F,
- shareY=F,
- titleX=T,
- titleY=T)
-
-
- })
-
- # Add some line returns to contrast names
-
- getSetNames <- reactive({
- selected_sets <- getSets()
- names(selected_sets)
- })
-
- # Make the grid of points indicating set membership in intersections
-
- upsetGrid <- reactive({
- selected_sets <- getSets()
- ints <- calculateIntersections()
-
- intersects <- ints$intersections
- combos <- ints$combinations
-
- # Reduce the maximum number of intersections if we don't have that many
- nintersections <- getNintersections()
- nintersections <- min(nintersections, length(combos))
-
- # Fetch the number of sets
- nsets <- length(getSelectedSetNames())
- setnames <- getSelectedSetNames()
-
- lines <-
- data.table::rbindlist(lapply(1:nintersections, function(combono) {
- data.frame(
- combo = combono,
- x = rep(combono, max(2, length(combos[[combono]]))),
- y = (nsets - combos[[combono]]) + 1,
- name = setnames[combos[[combono]]]
- )
- }))
-
- plot_ly(
- type = "scatter",
- mode = "markers",
- marker = list(color = "lightgrey", size = 8),
- customdata = "grid",
- source = "upset_de"
- ) %>% add_trace(
- type = "scatter",
- x = rep(1:nintersections,
- length(selected_sets)),
- y = unlist(lapply(1:length(selected_sets), function(x)
- rep(x - 0.5, nintersections))),
- hoverinfo = "none"
- ) %>% add_trace(
- type = "scatter",
- data = group_by(lines, combo),
- mode = "lines+markers",
- x = lines$x,
- y = lines$y - 0.5,
- line = list(color = "black", width = 3),
- marker = list(color = "black",
- size = 10),
- hoverinfo = "text",
- text = ~ name
- ) %>% layout(
- xaxis = list(
- showticklabels = FALSE,
- showgrid = FALSE,
- zeroline = FALSE
- ),
- yaxis = list(
- showticklabels = FALSE,
- showgrid = TRUE,
- range = c(0, nsets),
- zeroline = FALSE,
- range = 1:nsets
- ),
- margin = list(t = 0, b = 40)
- )
-
- })
-
- # Make the bar chart illustrating set sizes
-
- upsetSetSizeBarChart <- reactive({
- setnames <- getSelectedSetNames()
- selected_sets <- getSets()
-
- plot_ly(
- x = unlist(lapply(selected_sets, length)),
- y = setnames,
- type = "bar",
- orientation = "h",
- marker = list(color = "black"),
- customdata = "setsize",
- source = "upset_de"
- ) %>% layout(
- bargap = 0.4,
- yaxis = list(
- categoryarray = rev(setnames),
- categoryorder = "array"
- )
- )
- })
-
- # Make the bar chart illustrating intersect size
-
- upsetIntersectSizeBarChart <- reactive({
- ints <- calculateIntersections()
- intersects <- ints$intersections
- combos <- ints$combinations
- nintersections <- getNintersections()
-
- p <-
- plot_ly(showlegend = FALSE,
- customdata = "intersectsize",
- source = "upset_de") %>% add_trace(
- x = 1:nintersections,
- y = unlist(intersects[1:nintersections]),
- type = "bar",
- marker = list(color = "black",
- hoverinfo = "none")
- )
-
- bar_numbers <- TRUE
-
- if (bar_numbers) {
- p <-
- p %>% add_trace(
- type = "scatter",
- mode = "text",
- x = 1:nintersections,
- y = unlist(intersects[1:nintersections]) + (max(intersects) * 0.05),
- text = unlist(intersects[1:nintersections]),
- textfont = list(color = "black")
- )
- }
-
- p
- })
-
- observe({
- click <- event_data("plotly_click", source = "upset_de")
- req(click)
- print(click)
- })
-
- return(NULL)
- }
-
-
-
-
- )
-}
\ No newline at end of file
diff --git a/06_Shiny/app.R b/06_Shiny/app.R
deleted file mode 100644
index 84b346c..0000000
--- a/06_Shiny/app.R
+++ /dev/null
@@ -1,2 +0,0 @@
-
-shinyApp(ui,server)
\ No newline at end of file
diff --git a/06_Shiny/global.R b/06_Shiny/global.R
deleted file mode 100644
index 935eb3d..0000000
--- a/06_Shiny/global.R
+++ /dev/null
@@ -1,21 +0,0 @@
-library(shiny)
-library(shinythemes)
-library(markdown)
-library(plotly)
-library(DT)
-library(data.table)
-library(stringr)
-library(dplyr)
-library(ggplot2)
-#library(igraph)
-library(visNetwork)
-library(shinyWidgets)
-#library(UpSetR)
-library(org.Mm.eg.db)
-library(scales)
-library(shinycssloaders)
-library(RColorBrewer)
-
-
-basepath <- "~/Documents/ownCloud/DexStim_RNAseq_Mouse/"
-source(paste0(basepath, "scripts/06_Shiny/utilities_network.R"))
\ No newline at end of file
diff --git a/06_Shiny/server.R b/06_Shiny/server.R
deleted file mode 100644
index 9988378..0000000
--- a/06_Shiny/server.R
+++ /dev/null
@@ -1,200 +0,0 @@
-source(paste0(basepath, "scripts/06_Shiny/utilities_network.R"))
-#source(paste0(basepath, "scripts/06_Shiny/utilities_de.R"))
-
-# Define server logic required to generate and plot
-server <- shinyServer(function(input, output) {
-
- ### DIFFERENTIAL EXPRESSION ----------------------------------
-
- # DE data set
- de_data <- reactive({
- # read DE table to create volcano plot
- table <- fread(paste0(basepath, "tables/02_",
- input$region,"_deseq2_Dex_1_vs_0_lfcShrink.txt"))
- # table$Gene_Symbol <- mapIds(org.Mm.eg.db, keys = table$V1,
- # keytype = "ENSEMBL", column="SYMBOL")
- table <- table %>%
- dplyr::select(Gene_Symbol, Ensembl_ID:padj) %>%
- dplyr::mutate_at(vars(baseMean, log2FoldChange, lfcSE), ~round(.,4)) %>%
- dplyr::mutate_at(vars(pvalue, padj), ~signif(.,6))
- })
-
-
- # Volcano Plot displaying DE results
- output$volcano_plot <- renderPlotly({
-
- # req(de_data())
-
- # add column that indicates if gene is significant
- indices <- input$de_table_rows_all
- table <- de_data()[indices,] %>%
- dplyr::mutate(sig = as.factor(ifelse(padj <= 0.1, "FDR <= 0.1", "FDR > 0.1")))
-
- # volcano plot
- # volcano_plot <- plot_ly(data = table, x = ~log2FoldChange, y = ~-log10(padj),
- # color = ~sig, colors = "Set1",
- # text = ~paste0("Gene: ", V1),
- # source = "volcano_plot") %>%
- # layout(title = paste0("Volcano Plot ", input$region))
- volcano_plot <-
- ggplot(data = table, aes(
- x = log2FoldChange,
- y = -log10(padj),
- col = sig,
- text = paste0("Ensembl_ID: ", Ensembl_ID, "\nGene_Symbol: ", Gene_Symbol)
- )) +
- geom_point() +
- scale_colour_manual(breaks = c("FDR <= 0.1", "FDR > 0.1"),
- values = c("orange", "darkgrey")) +
- ylab("-log10(FDR)")+
- theme_light() +
- theme(plot.title = element_text(size = 11),
- legend.title = element_blank()) +
- ggtitle(label = paste0("Volcano Plot ", input$region))
- ggplotly(volcano_plot,
- source = "volcano_plot")
- })
-
- # Boxplot displaying norm expression values
- output$exp_plot <- renderPlotly({
-
- # req(de_data())
-
- # access data from click event
- d <- event_data("plotly_click", source = "volcano_plot",
- priority = "event")
- # print(d)
- # don't show anythiny if no point was clicked
- if (is.null(d)) return(NULL)
-
- # identify ensembl ID using the DE data
- ensembl_id <- de_data()[input$de_table_rows_all,]$Ensembl_ID[d$pointNumber + 1] # use pointNumber/rowNumber of clicked point
- gene_symbol <- de_data()[input$de_table_rows_all,]$Gene_Symbol[d$pointNumber + 1]
-
- # read table with normalized expression values
- exp_table <- fread(paste0(basepath, "tables/02_",
- input$region,"_deseq2_expression_vsd.txt"))
-
- # subset to row of clicked gene and reformat
- exp_gene <- data.table::transpose(exp_table[V1 == ensembl_id,2:ncol(exp_table)], keep.names = "condition") %>%
- dplyr::rename("expression" = "V1")
- exp_gene$condition <- str_replace(exp_gene$condition, ".*\\_","")
- exp_gene$mouse_id <- str_extract(exp_gene$condition, pattern = "[0-9]+")
- exp_gene$condition <- as.factor(str_replace(exp_gene$condition, "[0-9]+",""))
-
- # boxplot with jitter points of expression values in CNTRL and DEX
- exp_plot <-
- ggplot(exp_gene, aes(x = condition, y = expression, fill = condition)) +
- geom_boxplot() +
- geom_jitter(color = "black",
- size = 0.4,
- alpha = 0.9,
- aes(text = sprintf('mouse_ID: %s', mouse_id))) +
- scale_fill_manual("",
- breaks = c("CNTRL", "DEX"),
- labels = c("Baseline", "Treatment"),
- values = c("#B0BFBB", "#46866E")) +
- scale_x_discrete(breaks = c("CNTRL", "DEX"),
- labels = c("Baseline", "Treatment")) +
- theme_light() +
- theme(legend.position = "none",
- plot.title = element_text(size = 11)) +
- ggtitle(paste0("Gene expression of ", gene_symbol, " in ", input$region)) +
- xlab("") +
- ylab("Normalized expression")
- # ggplot to plotly
- ggplotly(exp_plot)
-
- })
-
- # Data table with DE results
- output$de_table <- DT::renderDataTable({
- de_data() %>%
- # dplyr::rename("Ensembl_ID" = "V1") %>%
- datatable(filter = list(position = 'top'))
- }, server = TRUE) # FALSE to enable download of all pages with button
-
- # Download of (filtered) DE results
- output$download1 <- downloadHandler(
- filename = function() {
- paste0("DE_dexStimMouse_", input$region, ".csv")
- },
- content = function(file) {
- indices <- input$de_table_rows_all
- write.csv(de_data()[indices, ], file)
- }
- )
-
- # Upset plot and table from upsetPlot module
- upset_de <- upsetPlotServer("upsetDE")
-
-
- ############# NETWORK ANALYSIS ##############################
-
-
- # Network data (nodedegree/nodebetweenness)
- overview_data <- reactive({
-
- # Read nodedegrees/nodebetweenness
- filename <- list.files(path = paste0(basepath, "tables/coExpression_kimono/",
- "03_AnalysisFuncoup"),
- pattern = paste0("*_", input$overview_region,
- "_funcoup_", input$overview_network, "_",
- input$overview_metric,"Norm_betacutoff0.01.csv"),
- full.names = TRUE)
- data <- fread(file = filename)
-
- })
-
- # Upset plot and table from upsetPlot module
- upset_hub <- comparisonHubGenesServer("compHubGenes")
-
-
- # Histogram network overview
- output$histogram_network <- renderPlotly({
-
- req(overview_data())
-
- # Histogram of nodedegree/nodebetweenness
- if (input$overview_metric == "nodedegrees"){
- histogram_network <- ggplot(overview_data(), aes(x=nodedegree)) +
- geom_histogram()
- } else {
- histogram_network <- ggplot(overview_data(), aes(x=nodebetweenness)) +
- geom_histogram()
- }
- ggplotly(histogram_network)
- })
-
- # Barplot network overview
- output$barplot_network <- renderPlotly({
-
- req(overview_data())
-
- # Histogram of nodedegree/nodebetweenness
- if (input$overview_metric == "nodedegrees"){
- barplot_network <- ggplot(overview_data(), aes(x=nodedegree))
- } else {
- barplot_network <- ggplot(overview_data(), aes(x=nodebetweenness)) +
- geom_histogram()
- }
-
- barplot_network <- barplot_network +
- geom_bar()
- ggplotly(barplot_network)
- })
-
- ### SINGLE REGION NETWORK -------------------------------
-
- # Single region network and table from networkSingle module
- net_single <- networkSingleServer("singleVisualization")
-
-
- ### MULTI REGION NETWORK ---------------------------------------
-
- # Multi region network and table from networkMulti module
- net_multi <- networkMultiServer("multiVisualization")
-
-
-})
-
diff --git a/06_Shiny/ui.R b/06_Shiny/ui.R
deleted file mode 100644
index f46823e..0000000
--- a/06_Shiny/ui.R
+++ /dev/null
@@ -1,239 +0,0 @@
-
-ui <- fluidPage(theme = shinytheme("flatly"),
-titlePanel(title=div(img(src="DiffBrainNet_logo.png", height = 50, width = 200), "Glucocorticoid receptor regulated gene expression in the mouse brain")),
-navbarPage("Explore!",
-
- tabPanel(icon("home"),
- fluidRow(column(tags$img(src="mousejavi_reversebrain.png",
- width="100%", height= "100%"),
- #width="450px",height="320px"),
- width=floor(0.4*12)),
- column(
- p(strong("Abstract."), "Network analysis can identify the molecular connectivity
- that is underpinning function at baseline, after a stimulus or a disease state. We inferred
- regression-based prior-knowledge guided gene networks in 8 brain regions of the mouse brain:
- the prefrontal cortex, the amygdala, the paraventricular nucleus of the hypothalamus, the dorsal
- and ventral Cornu ammonis 1, the dorsal and ventral dentate gyrus and the cerebellar cortex.
- We constructed networks at baseline and treatment levels using KiMONo (Ogris et al.) and at
- the differential level using DiffGRN (Kim et al.). DiffGRN uses the regression coefficients
- of the baseline and treatment networks to calculate differential connections, thus providing
- us with the actual functional couplings of the genes after the stimulus that differ from the
- baseline ones.",br(),
- "As a stimulus we used dexamethasone. Dexamethasone is a synthetic glucocorticoid that is used
-
- to activate the glucocorticoid receptors. Glucocorticoid receptors, when coupled with glucocorticoids
- like dexamethasone, act as transcription factors modulating the transcriptional landscape.
- Glucocorticoid receptors chromatin binding is one of the main results of stress-axis activation,
- which in turn is an important component of the biology of stress-related psychiatric disorders.
- Thus, dexamethasone mimics some aspects of the altered transcriptomic landscape that is associated
- with stress-related psychiatric disorders. We provide differential networks and differential
- expression analysis (DESeq2, Love et al.) that can be used to analyse the effects of dexamethasone
- both at the molecular connectivity and at the gene level in each brain region.", br(),
- "DiffBrainNet is an analysis framework and a resource for studying the transcriptional landscape
- of 8 mouse brain regions at baseline, dexamethasone-treatment and differential levels. It can be
- used to pinpoint molecular pathways important for the basic function and response to glucocorticoids
- in a brain-region specific manner. DiffBrainNet can also support the identification and analysis
- of biological processes regulated by brain and psychiatric diseases risk genes at the baseline and
- differential levels.",
- style="text-align:justify;color:black;background-color:#2980B9;padding:15px;border-radius:10px"),
- br(),
- p(strong("Data and code availability. Reference to paper."), "Sed ut perspiciatis unde omnis iste
- natus error sit voluptatem accusantium doloremque laudantium, totam rem aperiam, eaque ipsa quae
- ab illo inventore veritatis et quasi architecto beatae vitae dicta sunt explicabo. Nemo enim
- ipsam voluptatem quia voluptas sit aspernatur aut odit aut fugit, sed quia consequuntur magni
- dolores eos qui ratione voluptatem sequi nesciunt. Neque porro quisquam est, qui dolorem ipsum
- quia dolor sit amet, consectetur, adipisci velit, sed quia non numquam eius modi tempora incidunt
- ut labore et dolore magnam aliquam quaerat voluptatem. Ut enim ad minima veniam, quis nostrum
- exercitationem ullam corporis suscipit laboriosam, nisi ut aliquid ex ea commodi consequatur?
- Quis autem vel eum iure reprehenderit qui in ea voluptate velit esse quam nihil molestiae
- consequatur, vel illum qui dolorem eum fugiat quo voluptas nulla pariatur?",
- style="text-align:justify;color:black;background-color:#AED6F1;padding:15px;border-radius:10px"),
- width = floor(0.45*12)
- ),
- column(
- br(),
- tags$img(src="mpilogo.png", width="250px", height="60px"),
- br(),
- br(),
- p("For more information please visit the website of", em("the MPI of Psychiatry"),
- br(),
- a(href="https://www.psych.mpg.de/", "Here", target="_blank"),
- style="text-align:center;color:black"),
- width=ceiling(0.15*12)
- ))),
-
- navbarMenu("Diff. Gene Expression",
- tabPanel("Single Brain Region",
- sidebarLayout(
- sidebarPanel(
- selectInput("region", "Choose a brain region:",
- choices = c("Amygdala" = "AMY",
- "Cerebellum" = "CER",
- "Dorsal DG of Hippocampus" = "dDG",
- "Dorsal CA1 of Hippocampus" = "dCA1",
- "Prefrontal Cortex" = "PFC",
- "PVN of Hypothalamus" = "PVN",
- "Ventral DG of Hippocampus" = "vDG",
- "Ventral CA1 of Hippocampus" = "vCA1")),
- downloadButton("download1","Download (filtered) table as csv"),
- width = 3
- ),
- mainPanel(
- fluidPage(
- br(),
- tags$style(".fa-chart-bar {color:#2980B9}"),
- h3(p(em("Differential Gene Expression "),icon("chart-bar",lib = "font-awesome"),style="color:black;text-align:center")),
- hr(),
- span("Explore the transcriptomic response to Dexamethasone treatment of a brain region of your
- interest on a "), strong("differential expression level"), span(". You can select one of 8
- different brain regions on the left panel. The volcano plot shows the log2-transformed fold change
- and the -log10-transformed FDR corrected p-value of the genes detected in our dataset. Please "),
- strong("click on a point/gene"), span(" in the volcano plot to see its
- normalized expression levels before and after Dexamethasone treatment. You can filter and
- download the table of the differentially expressed genes."),
- br(),br(),
- # style="text-align:left;color:black"),
- splitLayout(cellWidths = c("55%", "45%"),
- plotlyOutput("volcano_plot"),
- plotlyOutput("exp_plot")),
- DT::dataTableOutput("de_table")
- )
- )
- )
- ),
- tabPanel("Comparison Brain Regions",
- # UI defined in upsetPlot module
- upsetPlotUI("upsetDE")
- )
- ),
-
- navbarMenu(
- "Network Analysis",
- tabPanel(
- "Introduction diff. networks",
- fluidPage(
- br(),
- tags$style(".fa-project-diagram {color:#2980B9}"),
- h3(p(
- em("Introduction: Differential network analysis"),
- icon("project-diagram", lib = "font-awesome"),
- style = "color:black;text-align:center"
- )),
- hr(),
-
- tags$img(src="DiffNetworks.png",
- width="60%", height= "60%",
- style="display: block; margin-left: auto; margin-right: auto;")
- )
-
-
-
- ),
-
- tabPanel(
- "Network overview",
- sidebarPanel(
- selectInput(
- "overview_region",
- "Choose a brain region:",
- choices = c(
- "Amygdala" = "AMY",
- "Cerebellum" = "CER",
- "Dorsal DG of Hippocampus" = "dDG",
- "Dorsal CA1 of Hippocampus" = "dCA1",
- "Prefrontal Cortex" = "PFC",
- "PVN of Hypothalamus" = "PVN",
- "Ventral DG of Hippocampus" = "vDG",
- "Ventral CA1 of Hippocampus" = "vCA1"
- )
- ),
- radioButtons(
- "overview_metric",
- label = "Select network metric:",
- choices = list("Nodedegree" = "nodedegrees",
- "Nodebetweenness" = "nodebetweenness"),
- selected = "nodebetweenness"
- ),
- radioButtons(
- "overview_network",
- label = "Select network:",
- choices = list("Baseline" = "baseline",
- "Differential" = "differential"),
- selected = "differential"
- ),
- width = 3
- ),
- mainPanel(
- fluidPage(
- br(),
- tags$style(".fa-project-diagram {color:#2980B9}"),
- h3(p(
- em("Network analysis of gene expression: Overview "),
- icon("project-diagram", lib = "font-awesome"),
- style = "color:black;text-align:center"
- )),
- hr(),
- splitLayout(cellWidths = c("50%", "50%"),
- h4(
- p("Baseline network", style = "color:black;text-align:center")
- ),
- h4(
- p("Differential network", style = "color:black;text-align:center")
- )),
- # plotlyOutput("histogram_network")
- splitLayout(
- cellWidths = c("50%", "50%"),
- plotlyOutput("histogram_network"),
- plotlyOutput("barplot_network")
- )
- # DT::dataTableOutput("network_table")
- )
- )
- ),
-
- tabPanel(
- "Comparison hub genes",
- comparisonHubGenesUI("compHubGenes")
- ),
-
- tabPanel(
- "Network visualization",
- # UI from networkSingle module
- networkSingleUI("singleVisualization")
- ),
-
- tabPanel(
- "Multi-region network visualization",
- # UI from networkMulti module
- networkMultiUI("multiVisualization")
- )
- ),
-
-
- navbarMenu("More",
- tabPanel("Data download",
- # DT::dataTableOutput("table")
- ),
- tabPanel("About",
- # fluidRow(
- # column(6,
- # includeMarkdown("about.md")
- # ),
- # column(3,
- # img(class="img-polaroid",
- # src=paste0("http://upload.wikimedia.org/",
- # "wikipedia/commons/9/92/",
- # "1919_Ford_Model_T_Highboy_Coupe.jpg")),
- # tags$small(
- # "Source: Photographed at the Bay State Antique ",
- # "Automobile Club's July 10, 2005 show at the ",
- # "Endicott Estate in Dedham, MA by ",
- # a(href="http://commons.wikimedia.org/wiki/User:Sfoskett",
- # "User:Sfoskett")
- # )
- # )
- # )
- )
- )
-)
-)
diff --git a/06_Shiny/utilities_network.R b/06_Shiny/utilities_network.R
deleted file mode 100644
index 0d449fc..0000000
--- a/06_Shiny/utilities_network.R
+++ /dev/null
@@ -1,178 +0,0 @@
-### FUNCTIONS -------------------------------------
-
-
-# remove duplicated edges from network dataframe
-simplify_network_df <- function(network_dt){
-
- network_dt <- network_dt %>%
- dplyr::filter(from != to) #%>%
- # dplyr::mutate(from_sort = pmin(from, to), to_sort = pmax(from, to)) %>%
- # dplyr::distinct(from_sort, to_sort, .keep_all = TRUE) %>%
- # dplyr::select(!c(from_sort, to_sort))
-}
-
-
-# subset network to gene of interest and if required also
-# neighbours of genes
-subset_network <- function(network_dt, subset_gene, neighbours){
-
- if (neighbours) {
- e_interest <- network_dt %>%
- filter(from %in% subset_gene | to %in% subset_gene)
- e_interest <- unique(c(e_interest$from, e_interest$to))
- network_subset <- network_dt %>%
- filter(from %in% e_interest & to %in% e_interest)
- } else {
- network_subset <- network_dt %>%
- filter(from %in% subset_gene & to %in% subset_gene)
- }
-
- return(network_subset)
-}
-
-
-# function to generate baseline/diff network, single regions
-read_network <- function(data, de_genes, hub_genes, gene, mode, id_type, neighbours){
-
- # input genes
- print(gene)
-
- # Edges
- if(mode == "baseline" | mode == "treatment"){
- c <- rep("grey", nrow(data))
- relations <- data.table(from=data$predictor,
- to=data$target,
- beta = data$beta_mean,
- color = c)
- # width = rescale(abs(data$beta_mean), to = c(0,1)))
- ledges <- data.frame(color = c("grey"),
- label = c("beta"),
- #arrows = c("right", "right"),
- font.align = "top")
- } else {
- c <- rep("#db9512", nrow(data))
- c[data$z > 0] <- "#128bdb"
- relations <- data.table(from=data$predictor,
- to=data$target,
- z = data$z,
- p_adj = data$p_adj,
- color = c)
- ledges <- data.frame(color = c("#db9512", "#128bdb"),
- label = c("z < 0", "z > 0"),
- arrows = c("right", "right"),
- font.align = "top")
- }
- relations <- simplify_network_df(relations)
- relations <- subset_network(relations, gene, neighbours)
- print(nrow(relations))
-
- # Nodes
- # Generate dataframe for node layout
- node_vec <- unique(c(relations$from, relations$to))
- if (id_type == "Gene Symbol"){
- labels <- mapIds(org.Mm.eg.db, keys = node_vec,
- column = "SYMBOL", keytype = "ENSEMBL")
- } else {
- labels <- node_vec
- }
-
- # set colours according to DE status of gene
- col <- rep("darkblue", length(node_vec))
- col[node_vec %in% de_genes] <- "orange"
- col[node_vec %in% hub_genes] <- "darkred"
- col[node_vec %in% de_genes & node_vec %in% hub_genes] <- "purple"
- nodes <- data.table(id = node_vec,
- label = labels,
- color = col)
-
- # nodes data.frame for legend
- lnodes <- data.frame(label = c("DE", "hub", "DE & hub", "no DE & no hub"),
- font.color = c("black", "white", "white", "white"),
- shape = c( "ellipse"),
- color = c("orange", "darkred", "purple", "darkblue"),
- title = "Informations", id = 1:4)
-
- return(list("edges" = relations, "nodes" = nodes, "ledges" = ledges, "lnodes" = lnodes))
-
-}
-
-
-# function to generate baseline/diff network, multiple regions
-read_network_multi <- function(regions, gene_unsplit, mode, id_type){
-
- list_reg <- list()
-
- for (reg in regions){
-
- # Read data
- data <-
- fread(
- file = paste0(
- basepath,
- "tables/coExpression_kimono/03_AnalysisFuncoup/",
- "04_singleRegion_",
- reg,
- "_filtered_diffNetwork.csv"
- )
- ) %>%
- dplyr::select(target, predictor, beta_mean.base, beta_mean.dex, z, p_adj)
-
- cols <- colnames(data)[3:6]
- data <- data %>%
- dplyr::rename_with(.fn = ~paste0(., ".", reg), .cols = all_of(cols) )
-
- list_reg[[reg]] <- data
-
- }
-
- data_joined <- list_reg %>%
- purrr::reduce(full_join, by = c("target","predictor"))
-
- # # Read data
- # data <- fread(file = paste0(basepath, "tables/coExpression_kimono/03_AnalysisFuncoup/",
- # "04_singleRegion_",region,"_filtered_", mode, "Network.csv"))
- gene <- stringr::str_split(gene_unsplit, ",\\s?")[[1]]
- print(gene)
- if (!startsWith(gene_unsplit, "ENSMUSG")){
- gene <- mapIds(org.Mm.eg.db, keys = gene, column = "ENSEMBL", keytype = "SYMBOL")
- }
-
- # Edges
- c <- rep("#db9512", nrow(data))
- c[data$z > 0] <- "#128bdb"
- relations <- data.table(from=data$target,
- to=data$predictor,
- z = data$z,
- p_adj = data$p_adj,
- color = c)
- # print(relations)
- relations <- simplify_network_df(relations)
- relations <- subset_network(relations, gene)
- print(nrow(relations))
-
- # Nodes
- node_vec <- unique(c(relations$from, relations$to))
- if (id_type == "Gene Symbol"){
- labels <- mapIds(org.Mm.eg.db, keys = node_vec,
- column = "SYMBOL", keytype = "ENSEMBL")
- } else {
- labels <- node_vec
- }
- nodes <- data.table(id = node_vec,
- label = labels)
- # title = node_vec)
- return(list("edges" = relations, "nodes" = nodes))
-
-}
-
-
-# function to generate network stats
-network_stats <- function(df){
-
- # Initialize data frame for stats
- stats <- data.frame(#network = character(),
- type = character(),
- value = numeric())
-
- stats <- rbind(stats, list("type" = "edges", "value" = nr_edges_base))
-}
diff --git a/06_Shiny/v2_dashboard/server.R b/06_Shiny/v2_dashboard/server.R
deleted file mode 100644
index e69de29..0000000
diff --git a/06_Shiny/v2_dashboard/ui.R b/06_Shiny/v2_dashboard/ui.R
deleted file mode 100644
index ec90b2f..0000000
--- a/06_Shiny/v2_dashboard/ui.R
+++ /dev/null
@@ -1,91 +0,0 @@
-library(semantic.dashboard)
-library(shiny)
-library(markdown)
-library(plotly)
-
-ui <- dashboardPage(dashboardHeader("Explore!"),
- ## Sidebar content
- dashboardSidebar(sidebarMenu(
- menuItem(tabName = "home", text = "Home", icon = icon("home")),
- menuItem(tabName = "de", text = "Diff. Gene Expression", icon = "heart"))
- ),
- dashboardBody(
- tabItems(
- # First tab content
- tabItem(tabName = "de",
- fluidRow(
- selectInput("region", "Choose a brain region:",
- choices = c("AMY", "CER", "dDG",
- "dCA1", "PFC",
- "vDG", "vCA1")),
- plotlyOutput("volcano_output"),
- plotlyOutput("exp_plot")
- ))
- )
- ))
-
-
-library(data.table)
-library(stringr)
-library(ggplot2)
-
-
-# Define server logic required to generate and plot
-server <- function(input, output) {
-
- output$volcano_plot <- renderPlotly({
-
- table <- fread(paste0("/Users/nathalie_gerstner/Documents/ownCloud/DexStim_RNAseq_Mouse/tables/02_",
- input$region,"_deseq2_Dex_1_vs_0_lfcShrink.txt"))
- # table <- fread(paste0("/Users/nathalie_gerstner/Documents/ownCloud/DexStim_RNAseq_Mouse/tables/02_",
- # "AMY","_deseq2_Dex_1_vs_0_lfcShrink.txt"))
- table$sig <- as.factor(ifelse(table$padj <= 0.1, "p_adj <= 0.1", "p_adj > 0.1"))
-
- # plot volcano plot
- volcano_plot <- plot_ly(data = table, x = ~log2FoldChange, y = ~-log10(padj),
- color = ~sig, colors = "Set1",
- text = ~paste0("Gene: ", V1)) %>%
- layout(title = paste0("Volcano Plot ", input$region))
- })
-
- output$exp_plot <- renderPlotly({
- d <- event_data("plotly_click")
- print(d)
- if (is.null(d)) return(NULL)
-
- table <- fread(paste0("/Users/nathalie_gerstner/Documents/ownCloud/DexStim_RNAseq_Mouse/tables/02_",
- input$region,"_deseq2_Dex_1_vs_0_lfcShrink.txt"))
- ensembl_id <- table$V1[d$pointNumber + 1]
-
- exp_table <- fread(paste0("/Users/nathalie_gerstner/Documents/ownCloud/DexStim_RNAseq_Mouse/tables/02_",
- input$region,"_deseq2_expression_vsd.txt"))
- # exp_table <- fread(paste0("/Users/nathalie_gerstner/Documents/ownCloud/DexStim_RNAseq_Mouse/tables/02_",
- # "AMY","_deseq2_expression_vsd.txt"))
-
- exp_gene <- transpose(exp_table[V1 == ensembl_id,2:ncol(exp_table)], keep.names = "col")
- exp_gene$col <- str_replace(exp_gene$col, ".*\\_","")
- exp_gene$mouse_id <- str_extract(exp_gene$col, pattern = "[0-9]+")
- exp_gene$col <- as.factor(str_replace(exp_gene$col, "[0-9]+",""))
-
- exp_plot <- ggplot(exp_gene, aes(x = col, y = V1)) +
- geom_boxplot() +
- geom_jitter(color="black", size=0.4, alpha=0.9) +
- theme_light() +
- theme(
- legend.position="none",
- plot.title = element_text(size=11)
- ) +
- ggtitle("A boxplot with jitter") +
- xlab("")
- ggplotly(exp_plot)
-
- # gSel <- tg %>% filter(dose %in% d$y) %>% group_by(supp) %>% mutate(newLen=floor(len)) %>%
- # ggplot(aes(x=supp, fill=as.factor(newLen))) + geom_bar()
- # ggplotly(gSel)
-
-
- })
-}
-
-shinyApp(ui, server)
-
\ No newline at end of file
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diff --git a/07_PlotsManuscript/01_FigureII_C.R b/07_PlotsManuscript/01_FigureII_C.R
deleted file mode 100644
index 69f79c8..0000000
--- a/07_PlotsManuscript/01_FigureII_C.R
+++ /dev/null
@@ -1,89 +0,0 @@
-##################################################
-## Project: DexStim Mouse Brain
-## Date: 21.11.2021
-## Author: Nathalie
-##################################################
-# GO Enrichment plot for manuscript
-# Unique DE and hub genes in PFC
-
-# set working directory to source file location
-setwd("~/Documents/ownCloud/DexStim_RNAseq_Mouse/scripts/07_PlotsManuscript")
-
-library(readxl)
-library(dplyr)
-library(stringr)
-library(ggplot2)
-
-
-# 1. GO enrichment unique DE and hub genes in PFC
-
-data_hub <- read_xlsx(path = "~/Documents/ownCloud/DexStim_RNAseq_Mouse/manuscript/Figures/Figure II_PFC/FUMA_diff unique hubgenes_gene2func67664/PFC diff unique hubgenes_FUMA.xlsx",
- sheet = "GO bp")
-
-data_DE <- read_xlsx(path = "~/Documents/ownCloud/DexStim_RNAseq_Mouse/manuscript/Figures/Figure II_PFC/DE PFC/FUMA_gene2func66314/PFC unique DE genes _FUMA.xlsx",
- sheet = "GO bp")
-
-
-# data for left panel with top 14 terms for DE
-top_DE <- data_DE %>%
- dplyr::filter(N_overlap_A > 24)
-top_DE <- top_DE[order(top_DE$`my adj p BH`, decreasing = FALSE),][1:14,]
-
-top_DE_hub <- data_hub %>%
- filter(GeneSet %in% top_DE$GeneSet)
-
-top_DE_comb <- bind_rows("de" = top_DE, "hub" = top_DE_hub, .id = "group")
-
-
-# data for right panel with top 14 terms for hubs
-top_hub <- data_hub %>%
- dplyr::filter(N_overlap_A > 2)
-top_hub <- top_hub[order(top_hub$`my adj p BH`, decreasing = FALSE),][1:14,]
-
-top_hub_DE <- data_DE %>%
- filter(GeneSet %in% top_hub$GeneSet)
-
-missing_term <- setdiff(top_hub$GeneSet, top_hub_DE$GeneSet)
-add_entry <- data.frame("GeneSet" = missing_term)
-top_hub_DE <- bind_rows(top_hub_DE, add_entry)
-
-top_hub_comb <- bind_rows("hub" = top_hub, "de" = top_hub_DE, .id = "group")
-
-
-# combine everything into one df for plotting
-top_data <- bind_rows("de" = top_DE_comb, "hub" = top_hub_comb, .id = "panel")
-# remove the "GO_" from GO terms
-top_data$GeneSet <- str_replace_all(top_data$GeneSet, '^GO_', ' ')
-# remove the "_" from GO terms
-top_data$GeneSet <- str_replace_all(top_data$GeneSet, "_", " ")
-# only capitalize first letter per word
-top_data$GeneSet <- str_to_title(top_data$GeneSet)
-# of and to should start with lower case
-top_data$GeneSet <- str_replace_all(top_data$GeneSet, "Of", "of")
-top_data$GeneSet <- str_replace_all(top_data$GeneSet, "To", "to")
-
-# labeller function for facet titles
-facet_names <- c(
- 'de'="Top 14 GO terms for DE genes",
- 'hub'="Top 14 GO terms for hub genes"
-)
-
-# barplot
-g <- ggplot(top_data, aes(x = GeneSet, y = `[-log10 fdr`, fill = group)) +
- geom_bar(position = position_dodge2(reverse=TRUE), stat="identity") +
-
- scale_colour_manual(values = c("grey", "black"), guide = FALSE) +
- scale_fill_manual(name = "Geneset",
- values = c("orange", "darkred"),
- labels = c("DE genes", "hub genes")) +
- coord_flip() +
- scale_x_discrete(limits = rev) +
- xlab("GOterm") +
- ylab("-log10(FDR)") +
- ggtitle("GO terms enriched for unique DE and hub genes in PFC") +
- facet_wrap(~panel, scales="free", labeller = as_labeller(facet_names)) +
- theme_light() +
- theme(text = element_text(size= 14))
-print(g)
-ggsave("01_FigureII_C.pdf", g, width = 14, height = 8)
-
diff --git a/07_PlotsManuscript/02_FigureII_F.R b/07_PlotsManuscript/02_FigureII_F.R
deleted file mode 100644
index 6b2283e..0000000
--- a/07_PlotsManuscript/02_FigureII_F.R
+++ /dev/null
@@ -1,83 +0,0 @@
-##################################################
-## Project: DexStim Mouse Brain
-## Date: 22.11.2021
-## Author: Nathalie
-##################################################
-# Pathway enrichment plot for manuscript
-# Abcd1 and neighbourhood in diff network of PFC
-
-library(readxl)
-
-# 1. Pathway enrichment for Abcd1 and neighbours
-
-data <- read_xlsx(path = "~/Documents/ownCloud/DexStim_RNAseq_Mouse/manuscript/Figures/Figure II_PFC/Abcd1 gene neighbourhood_FUMA_gene2func67210/Abcd1_diffnetwork_FUMA.xlsx",
- sheet = "KEGG AND REACTOME")
-
-data <- arrange(data, desc(`[-log10FDR]`))
-data <- data[1:20,]
-
-
-# remove the "_" from GO terms
-data$GeneSet <- str_replace_all(data$GeneSet, "_", " ")
-# only capitalize first letter per word
-data$GeneSet <- str_to_title(data$GeneSet)
-# of and to should start with lower case
-data$GeneSet <- str_replace_all(data$GeneSet, "Of", "of")
-data$GeneSet <- str_replace_all(data$GeneSet, "To", "to")
-data$GeneSet <- str_replace_all(data$GeneSet, " In ", " in ")
-data$GeneSet <- str_replace_all(data$GeneSet, " Into ", " into ")
-# Ii from "Polymerase II" back to upper case
-data$GeneSet <- str_replace_all(data$GeneSet, "Reactome", "Reactome:")
-data$GeneSet <- str_replace_all(data$GeneSet, "Kegg", "KEGG:")
-# GeneSet as factor
-data$GeneSet <- factor(data$GeneSet, levels = data$GeneSet)
-
-
-
-# bar plot gene ratio
-gr <- ggplot(data, aes(x = GeneSet, y = `Genes ratio`) ) +
- geom_bar(position = position_dodge2(reverse=TRUE), stat="identity", fill = "#0F5057") +
- scale_fill_manual() +
- theme_light() +
- coord_flip() +
- scale_x_discrete(limits = rev) +
- theme(text = element_text(size= 14)) +
- ylab("% Gene Ratio") +
- labs(x = NULL)
-
-# bar plot odds ratio
-or <- ggplot(data, aes(x = GeneSet, y = `OR[log2(a*d)-log2(b*c)]`) ) +
- geom_bar(position = position_dodge2(reverse=TRUE), stat="identity", fill = "#FAA916") +
- scale_fill_manual() +
- theme_light() +
- coord_flip() +
- scale_x_discrete(limits = rev) +
- theme(text = element_text(size= 14)) +
- ylab("Odds Ratio") +
- labs(x = NULL)
-
-# barplot fdr p-value
-fdr <- ggplot(data, aes(x = GeneSet, y = `[-log10FDR]`) ) +
- geom_bar(position = position_dodge2(reverse=TRUE), stat="identity", fill = "#D45E60") +
- geom_hline(yintercept = -log10(0.05),linetype="dashed", color = "red") +
- theme_light() +
- coord_flip() +
- scale_x_discrete(limits = rev) +
- theme(text = element_text(size= 14)) +
- ylab("-log10(FDR)") +
- labs(x = NULL)
-
-# combined barplot
-comb <- ggarrange(gr,
- or +
- theme(axis.text.y = element_blank(),
- axis.ticks.y = element_blank(),
- axis.title.y = element_blank() ),
- fdr +
- theme(axis.text.y = element_blank(),
- axis.ticks.y = element_blank(),
- axis.title.y = element_blank() ),
- nrow = 1,
- widths = c(1.9,1,1))
-
-ggexport(comb, filename = "02_FigureII_F.pdf", width = 14, height = 8)
diff --git a/07_PlotsManuscript/03_FigureIII_B.R b/07_PlotsManuscript/03_FigureIII_B.R
deleted file mode 100644
index 977faf3..0000000
--- a/07_PlotsManuscript/03_FigureIII_B.R
+++ /dev/null
@@ -1,83 +0,0 @@
-##################################################
-## Project: DexStim Mouse Brain
-## Date: 29.11.2021
-## Author: Nathalie
-##################################################
-# GO enrichment plot for manuscript
-# vCA1 unique DE genes enrichments
-
-library(readxl)
-library(dplyr)
-library(stringr)
-library(ggpubr)
-
-# 1. GO enrichment for vCA unique DE genes
-
-data <- read_xlsx(path = "~/Documents/ownCloud/DexStim_RNAseq_Mouse/manuscript/Figures/Figure III_vCA1/DE vCA1/FUMA_gene2func67310 (2)/vCA1 Unique DE genes_FUMA enrichments.xlsx",
- sheet = "GO bp")
-
-data <- arrange(data, desc(`[-log10 of FDR`))
-data <- data[1:20,]
-
-
-# remove the "GO_" from GO terms
-data$GeneSet <- str_replace_all(data$GeneSet, '^GO_', ' ')
-# remove the "_" from GO terms
-data$GeneSet <- str_replace_all(data$GeneSet, "_", " ")
-# only capitalize first letter per word
-data$GeneSet <- str_to_title(data$GeneSet)
-# of and to should start with lower case
-data$GeneSet <- str_replace_all(data$GeneSet, "Of", "of")
-data$GeneSet <- str_replace_all(data$GeneSet, "To", "to")
-data$GeneSet <- str_replace_all(data$GeneSet, " In ", " in ")
-# GeneSet as factor
-data$GeneSet <- factor(data$GeneSet, levels = data$GeneSet)
-
-
-# bar plot gene ratio
-gr <- ggplot(data, aes(x = GeneSet, y = `Genes ratio`) ) +
- geom_bar(position = position_dodge2(reverse=TRUE), stat="identity", fill = "#0F5057") +
- scale_fill_manual() +
- theme_light() +
- coord_flip() +
- scale_x_discrete(limits = rev) +
- theme(text = element_text(size= 14)) +
- ylab("% Gene Ratio") +
- labs(x = NULL)
-
-# bar plot odds ratio
-or <- ggplot(data, aes(x = GeneSet, y = `OR[log2(a*d)-log2(b*c)]`) ) +
- geom_bar(position = position_dodge2(reverse=TRUE), stat="identity", fill = "#FAA916") +
- scale_fill_manual() +
- theme_light() +
- coord_flip() +
- scale_x_discrete(limits = rev) +
- theme(text = element_text(size= 14)) +
- ylab("Odds Ratio") +
- labs(x = NULL)
-
-# barplot fdr p-value
-fdr <- ggplot(data, aes(x = GeneSet, y = `[-log10 of FDR`) ) +
- geom_bar(position = position_dodge2(reverse=TRUE), stat="identity", fill = "#D45E60") +
- geom_hline(yintercept = -log10(0.05),linetype="dashed", color = "red") +
- theme_light() +
- coord_flip() +
- scale_x_discrete(limits = rev) +
- theme(text = element_text(size= 14)) +
- ylab("-log10(FDR)") +
- labs(x = NULL)
-
-# combined barplot
-comb <- ggarrange(gr,
- or +
- theme(axis.text.y = element_blank(),
- axis.ticks.y = element_blank(),
- axis.title.y = element_blank() ),
- fdr +
- theme(axis.text.y = element_blank(),
- axis.ticks.y = element_blank(),
- axis.title.y = element_blank() ),
- nrow = 1,
- widths = c(2.5,1,1))
-
-ggexport(comb, filename = "03_FigureIII_B.pdf", width = 14, height = 8)
diff --git a/07_PlotsManuscript/04_FigureIII_C.R b/07_PlotsManuscript/04_FigureIII_C.R
deleted file mode 100644
index ba9e55f..0000000
--- a/07_PlotsManuscript/04_FigureIII_C.R
+++ /dev/null
@@ -1,84 +0,0 @@
-##################################################
-## Project: DexStim Mouse Brain
-## Date: 29.11.2021
-## Author: Nathalie
-##################################################
-# GO enrichment plot for manuscript
-# vCA1 unique DE genes with neighbours enrichments
-
-
-library(readxl)
-library(dplyr)
-library(stringr)
-library(ggpubr)
-
-# 1. GO enrichment for vCA unique DE genes and their diff neighbours
-
-data <- read_xlsx(path = "~/Documents/ownCloud/DexStim_RNAseq_Mouse/manuscript/Figures/Figure III_vCA1/network vCA1/Unique DEs and their diff neighbours_FUMA_gene2func67313/vCA1 Unique DE genes+diff neighbours_FUMA enrichments.xlsx",
- sheet = "GO bp")
-
-data <- arrange(data, desc(`[-LOG10 of FDR`))
-data <- data[1:20,]
-
-# remove the "GO_" from GO terms
-data$GeneSet <- str_replace_all(data$GeneSet, '^GO_', ' ')
-# remove the "_" from GO terms
-data$GeneSet <- str_replace_all(data$GeneSet, "_", " ")
-# only capitalize first letter per word
-data$GeneSet <- str_to_title(data$GeneSet)
-# of and to should start with lower case
-data$GeneSet <- str_replace_all(data$GeneSet, "Of", "of")
-data$GeneSet <- str_replace_all(data$GeneSet, "To", "to")
-data$GeneSet <- str_replace_all(data$GeneSet, " In ", " in ")
-data$GeneSet <- str_replace_all(data$GeneSet, " Into ", " into ")
-# GeneSet as factor
-data$GeneSet <- factor(data$GeneSet, levels = data$GeneSet)
-
-
-# bar plot gene ratio
-gr <- ggplot(data, aes(x = GeneSet, y = `Genes ratio`) ) +
- geom_bar(position = position_dodge2(reverse=TRUE), stat="identity", fill = "#0F5057") +
- scale_fill_manual() +
- theme_light() +
- coord_flip() +
- scale_x_discrete(limits = rev) +
- theme(text = element_text(size= 14)) +
- ylab("% Gene Ratio") +
- labs(x = NULL)
-
-# bar plot odds ratio
-or <- ggplot(data, aes(x = GeneSet, y = `OR[log2(a*d)-log2(b*c)]`) ) +
- geom_bar(position = position_dodge2(reverse=TRUE), stat="identity", fill = "#FAA916") +
- scale_fill_manual() +
- theme_light() +
- coord_flip() +
- scale_x_discrete(limits = rev) +
- theme(text = element_text(size= 14)) +
- ylab("Odds Ratio") +
- labs(x = NULL)
-
-# barplot fdr p-value
-fdr <- ggplot(data, aes(x = GeneSet, y = `[-LOG10 of FDR`) ) +
- geom_bar(position = position_dodge2(reverse=TRUE), stat="identity", fill = "#D45E60") +
- geom_hline(yintercept = -log10(0.05),linetype="dashed", color = "red") +
- theme_light() +
- coord_flip() +
- scale_x_discrete(limits = rev) +
- theme(text = element_text(size= 14)) +
- ylab("-log10(FDR)") +
- labs(x = NULL)
-
-# combined barplot
-comb <- ggarrange(gr,
- or +
- theme(axis.text.y = element_blank(),
- axis.ticks.y = element_blank(),
- axis.title.y = element_blank() ),
- fdr +
- theme(axis.text.y = element_blank(),
- axis.ticks.y = element_blank(),
- axis.title.y = element_blank() ),
- nrow = 1,
- widths = c(1.8,1,1))
-
-ggexport(comb, filename = "03_FigureIII_C.pdf", width = 12, height = 8)
diff --git a/07_PlotsManuscript/05_FigureIV_Bbase.R b/07_PlotsManuscript/05_FigureIV_Bbase.R
deleted file mode 100644
index f996410..0000000
--- a/07_PlotsManuscript/05_FigureIV_Bbase.R
+++ /dev/null
@@ -1,90 +0,0 @@
-##################################################
-## Project: DexStim Mouse Brain
-## Date: 29.11.2021
-## Author: Nathalie
-##################################################
-# GO enrichment plot for manuscript
-# Tcf4 baseline network in PFC
-
-library(readxl)
-library(dplyr)
-library(stringr)
-library(ggpubr)
-
-# 1. GO enrichment for vCA unique DE genes and their diff neighbours
-
-data <- read_xlsx(path = "~/Documents/ownCloud/DexStim_RNAseq_Mouse/manuscript/Figures/Figure IV_GWAS/TCF4/FUMA_baselin Tcf4PFCnetwork_gene2func68958/FUMA Tcf4BaselinePFCnetwork.xlsx",
- sheet = "GO bp")
-
-data <- arrange(data, desc(`[-log10]`))
-data <- data[1:25,]
-
-# remove the "GO_" from GO terms
-data$GeneSet <- str_replace_all(data$GeneSet, '^GO_', ' ')
-# remove the "_" from GO terms
-data$GeneSet <- str_replace_all(data$GeneSet, "_", " ")
-# only capitalize first letter per word
-data$GeneSet <- str_to_title(data$GeneSet)
-# of and to should start with lower case
-data$GeneSet <- str_replace_all(data$GeneSet, "Of", "of")
-data$GeneSet <- str_replace_all(data$GeneSet, "To", "to")
-data$GeneSet <- str_replace_all(data$GeneSet, " In ", " in ")
-data$GeneSet <- str_replace_all(data$GeneSet, " Into ", " into ")
-# make some terms shorter with abbreviations
-data$GeneSet <- str_replace_all(data$GeneSet, "Positive Regulation", "Pos. Reg.")
-data$GeneSet <- str_replace_all(data$GeneSet, "Negative Regulation", "Neg. Reg.")
-# Ii from "Polymerase II" back to upper case
-data$GeneSet <- str_replace_all(data$GeneSet, "Ii", "II")
-# GeneSet as factor
-data$GeneSet <- factor(data$GeneSet, levels = data$GeneSet)
-
-
-# bar plot gene ratio
-gr <- ggplot(data, aes(x = GeneSet, y = `Genes ratio`) ) +
- geom_bar(position = position_dodge2(reverse=TRUE), stat="identity", fill = "#0F5057") +
- scale_fill_manual() +
- theme_light() +
- coord_flip() +
- scale_x_discrete(limits = rev) +
- theme(text = element_text(size= 14)) +
- ylab("% Gene Ratio") +
- labs(x = NULL)
-
-# bar plot odds ratio
-or <- ggplot(data, aes(x = GeneSet, y = `OR[log2(a*d)-log2(b*c)]`) ) +
- geom_bar(position = position_dodge2(reverse=TRUE), stat="identity", fill = "#FAA916") +
- scale_fill_manual() +
- theme_light() +
- coord_flip() +
- scale_x_discrete(limits = rev) +
- theme(text = element_text(size= 14)) +
- ylab("Odds Ratio") +
- labs(x = NULL)
-
-# barplot fdr p-value
-fdr <- ggplot(data, aes(x = GeneSet, y = `[-log10]`) ) +
- geom_bar(position = position_dodge2(reverse=TRUE), stat="identity", fill = "#D45E60") +
- geom_hline(yintercept = -log10(0.05),linetype="dashed", color = "red") +
- theme_light() +
- coord_flip() +
- scale_x_discrete(limits = rev) +
- theme(text = element_text(size= 14)) +
- ylab("-log10(FDR)") +
- labs(x = NULL)
-
-# combined barplot
-comb <- ggarrange(gr,
- or +
- theme(axis.text.y = element_blank(),
- axis.ticks.y = element_blank(),
- axis.title.y = element_blank() ),
- fdr +
- theme(axis.text.y = element_blank(),
- axis.ticks.y = element_blank(),
- axis.title.y = element_blank() ),
- nrow = 1,
- widths = c(1.9,1,1))
-
-ggexport(comb, filename = "05_FigureIV_Bbase.pdf", width = 14, height = 10)
-
-
diff --git a/07_PlotsManuscript/05_FigureIV_Bdiff.R b/07_PlotsManuscript/05_FigureIV_Bdiff.R
deleted file mode 100644
index ddd3f64..0000000
--- a/07_PlotsManuscript/05_FigureIV_Bdiff.R
+++ /dev/null
@@ -1,88 +0,0 @@
-##################################################
-## Project: DexStim Mouse Brain
-## Date: 29.11.2021
-## Author: Nathalie
-##################################################
-# GO enrichment plot for manuscript
-# Tcf4 differential network in PFC
-
-library(readxl)
-library(dplyr)
-library(stringr)
-library(ggpubr)
-
-# 1. GO enrichment for vCA unique DE genes and their diff neighbours
-
-data <- read_xlsx(path = "~/Documents/ownCloud/DexStim_RNAseq_Mouse/manuscript/Figures/Figure IV_GWAS/TCF4/FUMA_diffTcf4PFCnetwork_gene2func68788/FUMA_diffPFCTcf4.xlsx",
- sheet = "GO bp")
-
-data <- arrange(data, desc(`[-log10]`))
-data <- data[1:25,]
-
-# remove the "GO_" from GO terms
-data$GeneSet <- str_replace_all(data$GeneSet, '^GO_', ' ')
-# remove the "_" from GO terms
-data$GeneSet <- str_replace_all(data$GeneSet, "_", " ")
-# only capitalize first letter per word
-data$GeneSet <- str_to_title(data$GeneSet)
-# of and to should start with lower case
-data$GeneSet <- str_replace_all(data$GeneSet, "Of", "of")
-data$GeneSet <- str_replace_all(data$GeneSet, "To", "to")
-data$GeneSet <- str_replace_all(data$GeneSet, " In ", " in ")
-data$GeneSet <- str_replace_all(data$GeneSet, " Into ", " into ")
-# make some terms shorter with abbreviations
-data$GeneSet <- str_replace_all(data$GeneSet, "Positive Regulation", "Pos. Reg.")
-data$GeneSet <- str_replace_all(data$GeneSet, "Negative Regulation", "Neg. Reg.")
-# Ii from "Polymerase II" back to upper case
-data$GeneSet <- str_replace_all(data$GeneSet, "Ii", "II")
-# GeneSet as factor
-data$GeneSet <- factor(data$GeneSet, levels = data$GeneSet)
-
-
-# bar plot gene ratio
-gr <- ggplot(data, aes(x = GeneSet, y = `Genes ratio`) ) +
- geom_bar(position = position_dodge2(reverse=TRUE), stat="identity", fill = "#0F5057") +
- scale_fill_manual() +
- theme_light() +
- coord_flip() +
- scale_x_discrete(limits = rev) +
- theme(text = element_text(size= 14)) +
- ylab("% Gene Ratio") +
- labs(x = NULL)
-
-# bar plot odds ratio
-or <- ggplot(data, aes(x = GeneSet, y = `OR[log2(a*d)-log2(b*c)]`) ) +
- geom_bar(position = position_dodge2(reverse=TRUE), stat="identity", fill = "#FAA916") +
- scale_fill_manual() +
- theme_light() +
- coord_flip() +
- scale_x_discrete(limits = rev) +
- theme(text = element_text(size= 14)) +
- ylab("Odds Ratio") +
- labs(x = NULL)
-
-# barplot fdr p-value
-fdr <- ggplot(data, aes(x = GeneSet, y = `[-log10]`) ) +
- geom_bar(position = position_dodge2(reverse=TRUE), stat="identity", fill = "#D45E60") +
- geom_hline(yintercept = -log10(0.05),linetype="dashed", color = "red") +
- theme_light() +
- coord_flip() +
- scale_x_discrete(limits = rev) +
- theme(text = element_text(size= 14)) +
- ylab("-log10(FDR)") +
- labs(x = NULL)
-
-# combined barplot
-comb <- ggarrange(gr,
- or +
- theme(axis.text.y = element_blank(),
- axis.ticks.y = element_blank(),
- axis.title.y = element_blank() ),
- fdr +
- theme(axis.text.y = element_blank(),
- axis.ticks.y = element_blank(),
- axis.title.y = element_blank() ),
- nrow = 1,
- widths = c(1.9,1,1))
-
-ggexport(comb, filename = "05_FigureIV_Bdiff.pdf", width = 14, height = 10)
\ No newline at end of file
diff --git a/07_PlotsManuscript/06_FigureSIII_A.R b/07_PlotsManuscript/06_FigureSIII_A.R
deleted file mode 100644
index 01122cd..0000000
--- a/07_PlotsManuscript/06_FigureSIII_A.R
+++ /dev/null
@@ -1,72 +0,0 @@
-##################################################
-## Project: DexStim Mouse Brain
-## Date: 29.11.2021
-## Author: Nathalie
-##################################################
-# GWAS enrichment plot for manuscript
-# vCA1
-
-library(readxl)
-library(dplyr)
-library(ggplot2)
-library(ggpubr)
-
-# 1. Pathway enrichment for Abcd1 and neighbours
-
-data <- read_xlsx(path = "~/Documents/ownCloud/DexStim_RNAseq_Mouse/manuscript/Figures/Figure III_vCA1/network vCA1/Unique DEs and their diff neighbours_FUMA_gene2func67313/vCA1 Unique DE genes+diff neighbours_FUMA enrichments.xlsx",
- sheet = "GWAS")
-
-data <- arrange(data, desc(`[-log10 of FDR`))
-data <- data[data$`my adj p BH` <= 0.05,]
-data <- data[!is.na(data$`my adj p BH`),]
-
-# GeneSet as factor
-data$GeneSet <- factor(data$GeneSet, levels = data$GeneSet)
-
-# bar plot gene ratio
-gr <- ggplot(data, aes(x = GeneSet, y = `Genes ratio`) ) +
- geom_bar(position = position_dodge2(reverse=TRUE), stat="identity", fill = "#0F5057") +
- scale_fill_manual() +
- theme_light() +
- coord_flip() +
- scale_x_discrete(limits = rev) +
- theme(text = element_text(size= 14)) +
- ylab("% Gene Ratio") +
- labs(x = NULL)
-
-# bar plot odds ratio
-or <- ggplot(data, aes(x = GeneSet, y = `OR[log2(a*d)-log2(b*c)]`) ) +
- geom_bar(position = position_dodge2(reverse=TRUE), stat="identity", fill = "#FAA916") +
- scale_fill_manual() +
- theme_light() +
- coord_flip() +
- scale_x_discrete(limits = rev) +
- theme(text = element_text(size= 14)) +
- ylab("Odds Ratio") +
- labs(x = NULL)
-
-# barplot fdr p-value
-fdr <- ggplot(data, aes(x = GeneSet, y = `[-log10 of FDR`) ) +
- geom_bar(position = position_dodge2(reverse=TRUE), stat="identity", fill = "#D45E60") +
- geom_hline(yintercept = -log10(0.05),linetype="dashed", color = "red") +
- theme_light() +
- coord_flip() +
- scale_x_discrete(limits = rev) +
- theme(text = element_text(size= 14)) +
- ylab("-log10(FDR)") +
- labs(x = NULL)
-
-# combined barplot
-comb <- ggarrange(gr,
- or +
- theme(axis.text.y = element_blank(),
- axis.ticks.y = element_blank(),
- axis.title.y = element_blank() ),
- fdr +
- theme(axis.text.y = element_blank(),
- axis.ticks.y = element_blank(),
- axis.title.y = element_blank() ),
- nrow = 1,
- widths = c(1.8,1,1))
-
-ggexport(comb, filename = "06_FigureSIII_A.pdf", width = 14, height = 8)
diff --git a/07_PlotsManuscript/07_FigureSIV.R b/07_PlotsManuscript/07_FigureSIV.R
deleted file mode 100644
index 9afb5a5..0000000
--- a/07_PlotsManuscript/07_FigureSIV.R
+++ /dev/null
@@ -1,120 +0,0 @@
-##################################################
-## Project: DexStim Mouse Brain
-## Date: 29.11.2021
-## Author: Nathalie
-##################################################
-# Pathway enrichment plot for manuscript
-# Tcf4 Differential Expression
-
-library(stringr)
-library(ggplot2)
-
-regions <- c("AMY", "CER", "dCA1", "dDG", "PFC", "PVN", "vCA1", "vDG")
-
-ensembl_tcf4 <- "ENSMUSG00000053477"
-
-# Panel A: Expression values
-df <- data.frame("region" = character(),
- "treatment" = character(),
- "sample" = character(),
- "expression" = numeric())
-
-for (reg in regions){
-
- # read vsd normalized data --> better raw data?
- data_exp <- read.table(paste0("~/Documents/ownCloud/DexStim_RNAseq_Mouse/tables/02_",
- reg, "_deseq2_expression_vsd.txt"),
- header = TRUE, sep = "\t")
- # subset Tcf4 row
- tcf4 <- data_exp[ensembl_tcf4,]
-
- # get column indices for cntrl samples
- indices_cntrl <- str_detect(colnames(data_exp), "CNTRL")
-
- # df for cntrl samples
- exp_cntrl <- data.frame("expression" = t(tcf4[,indices_cntrl]),
- "sample" = rownames(t(tcf4[,indices_cntrl]))) %>%
- rename(expression = ENSMUSG00000053477) %>%
- mutate("treatment" = "CNTRL", "region" = reg)
- df <- rbind(df, exp_cntrl)
-
- # df for dex samples
- exp_dex <- data.frame("expression" = t(tcf4[,!indices_cntrl]),
- "sample" = rownames(t(tcf4[,!indices_cntrl]))) %>%
- rename(expression = ENSMUSG00000053477) %>%
- mutate("treatment" = "DEX", "region" = reg)
- df <- rbind(df, exp_dex)
-
-}
-
-df$treatment <- factor(df$treatment)
-df$region <- factor(df$region)
-
-ggplot(df, aes(x = region, y = expression, fill = treatment)) +
- geom_boxplot() +
- scale_fill_manual("",
- breaks = c("CNTRL", "DEX"),
- labels = c("Baseline", "Treatment"),
- values = c("#B0BFBB", "#46866E")) +
- theme_light() +
- theme(text = element_text(size= 14)) +
- xlab("Brain Region") +
- ylab("Norm. Expression Level")
-
-ggsave(filename = "07_FigureSIV_A.pdf", width = 12, height = 8)
-
-
-# Panel B: Fold Change in AMY, vDG and dDG
-
-list_tcf4 <- list()
-
-for (reg in c("AMY", "dDG", "vDG")){
-# for (reg in regions){
-
- # read DE results
- data_exp <- read.table(paste0("~/Documents/ownCloud/DexStim_RNAseq_Mouse/tables/02_",
- reg, "_deseq2_Dex_1_vs_0_lfcShrink.txt"),
- header = TRUE, sep = "\t")
- tcf4 <- as.data.frame(data_exp[data_exp$Ensembl_ID == ensembl_tcf4,])
- print(tcf4)
-
- list_tcf4[[reg]] <- tcf4
-
-}
-
-df_tcf4 <- bind_rows(list_tcf4, .id = "region")
-
-
-# bar plot Fold Change
-fc <- ggplot(df_tcf4, aes(x = region, y = log2FoldChange) ) +
- geom_bar(position = position_dodge2(reverse=TRUE), stat="identity", fill = "darkgrey") +
- theme_light() +
- coord_flip() +
- scale_x_discrete(limits = rev) +
- theme(text = element_text(size= 14)) +
- ylab("log2(FoldChange)") +
- xlab("Brain Region")
-
-# barplot fdr p-value
-fdr <- ggplot(df_tcf4, aes(x = region, y = -log10(padj)) ) +
- geom_bar(position = position_dodge2(reverse=TRUE), stat="identity", fill = "#D45E60") +
- geom_hline(yintercept = -log10(0.1),linetype="dashed", color = "red") +
- theme_light() +
- coord_flip() +
- scale_x_discrete(limits = rev) +
- theme(text = element_text(size= 14)) +
- ylab("-log10(FDR)") +
- xlab("")
-
-# combined barplot
-comb <- ggarrange(fc,
- fdr +
- theme(axis.text.y = element_blank(),
- axis.ticks.y = element_blank(),
- axis.title.y = element_blank() ),
- nrow = 1,
- widths = c(1.3,1))
-
-# ggexport(comb, filename = "07_FigureSIV_B_allRegions.pdf", width = 12, height = 8)
-ggexport(comb, filename = "07_FigureSIV_B.pdf", width = 12, height = 8)
-
diff --git a/07_PlotsManuscript/08_FigureIII_A.R b/07_PlotsManuscript/08_FigureIII_A.R
deleted file mode 100644
index f71384a..0000000
--- a/07_PlotsManuscript/08_FigureIII_A.R
+++ /dev/null
@@ -1,46 +0,0 @@
-##################################################
-## Project: DexStim Mouse Brain
-## Date: 30.11.2021
-## Author: Nathalie
-##################################################
-# Numbers/Percentages of DE and hub genes in vCA1
-# including also number of unique ones
-
-library(ggplot2)
-
-# correct numbers? --> DE percentage different than before
-DE_genes <- 465
-hub_genes <- 260
-
-unique_DE <- 25
-unique_hub <- 24
-
-# data frame for plotting
-df <- data.frame("type" = c("DE", "DE", "hub", "hub"),
- "unique" = c(TRUE, FALSE, TRUE, FALSE),
- "number" = c(unique_DE, DE_genes - unique_DE,
- unique_hub, hub_genes - unique_hub),
- "text" = c(paste0(round(unique_DE/DE_genes*100,1),"%"), "",
- paste0(round(unique_hub/hub_genes*100,1), "%"), ""))
-
-# barplot
-ggplot(df, aes(x = type, y = number, fill = type)) +
- geom_bar(position = "stack", stat="identity", aes(alpha = unique)) +
- geom_text(aes(y = number, label = text), vjust = 1.5) +
- scale_alpha_manual("",
- breaks = c(FALSE, TRUE),
- values = c(1.0,0.5),
- labels = c("Shared genes", "Unique genes")) +
- xlab("") +
- ylab("# DE and hub genes in vCA1") +
- scale_fill_manual("",
- breaks = c("DE", "hub"),
- labels = c("DE genes", "Hub genes"),
- values = c("orange", "darkred")) +
- scale_x_discrete(breaks = c("DE", "hub"),
- labels = c("DE genes", "Hub genes")) +
- theme_light() +
- theme(text = element_text(size= 14)) +
- guides(fill = "none")
-
-ggsave(filename = "08_FigureIII_A.pdf", width = 8, height = 6)
diff --git a/07_PlotsManuscript/09_FigureII_DandE.R b/07_PlotsManuscript/09_FigureII_DandE.R
deleted file mode 100644
index 2d195b9..0000000
--- a/07_PlotsManuscript/09_FigureII_DandE.R
+++ /dev/null
@@ -1,104 +0,0 @@
-##################################################
-## Project: DexStim Mouse Brain
-## Date: 30.11.2021
-## Author: Nathalie
-##################################################
-# Expression level plots Syn3 and Abcd1 in PFC
-
-library(stringr)
-library(ggplot2)
-
-reg <- "PFC"
-
-ensembl_syn3 <- "ENSMUSG00000059602"
-ensembl_abcd1 <- "ENSMUSG00000031378"
-
-# read vsd normalized data
-data_exp <- read.table(paste0("~/Documents/ownCloud/DexStim_RNAseq_Mouse/tables/02_",
- reg, "_deseq2_expression_vsd.txt"),
- header = TRUE, sep = "\t")
-
-
-### Syn3 ----------------
-
-# subset Syn3 row
-syn3 <- data_exp[ensembl_syn3,]
-
-# get column indices for cntrl samples
-indices_cntrl <- str_detect(colnames(data_exp), "CNTRL")
-
-# df for cntrl samples
-exp_cntrl <- data.frame("expression" = t(syn3[,indices_cntrl]),
- "sample" = rownames(t(syn3[,indices_cntrl]))) %>%
- rename(expression = all_of(ensembl_syn3)) %>%
- mutate("treatment" = "CNTRL")
-
-# df for dex samples
-exp_dex <- data.frame("expression" = t(syn3[,!indices_cntrl]),
- "sample" = rownames(t(syn3[,!indices_cntrl]))) %>%
- rename(expression = all_of(ensembl_syn3)) %>%
- mutate("treatment" = "DEX")
-
-# combine cntrl and dex df
-df <- rbind(exp_cntrl, exp_dex)
-df$treatment <- factor(df$treatment)
-
-ggplot(df, aes(x = treatment, y = expression, fill = treatment)) +
- geom_boxplot() +
- geom_jitter() +
- scale_fill_manual("",
- breaks = c("CNTRL", "DEX"),
- labels = c("Baseline", "Treatment"),
- values = c("#B0BFBB", "#46866E")) +
- scale_x_discrete(breaks = c("CNTRL", "DEX"),
- labels = c("Baseline", "Treatment")) +
- theme_light() +
- theme(text = element_text(size= 16)) +
- xlab("") +
- ylab("Norm. Expression Level") +
- guides(fill = "none")
-
-ggsave(filename = "09_FigureII_D.pdf", width = 7, height = 6)
-
-
-
-### Abcd1 ----------------
-
-# subset Abcd1 row
-abcd1 <- data_exp[ensembl_abcd1,]
-
-# get column indices for cntrl samples
-indices_cntrl <- str_detect(colnames(data_exp), "CNTRL")
-
-# df for cntrl samples
-exp_cntrl <- data.frame("expression" = t(abcd1[,indices_cntrl]),
- "sample" = rownames(t(abcd1[,indices_cntrl]))) %>%
- rename(expression = all_of(ensembl_abcd1)) %>%
- mutate("treatment" = "CNTRL")
-
-# df for dex samples
-exp_dex <- data.frame("expression" = t(abcd1[,!indices_cntrl]),
- "sample" = rownames(t(abcd1[,!indices_cntrl]))) %>%
- rename(expression = all_of(ensembl_abcd1)) %>%
- mutate("treatment" = "DEX")
-
-# combine cntrl and dex df
-df <- rbind(exp_cntrl, exp_dex)
-df$treatment <- factor(df$treatment)
-
-ggplot(df, aes(x = treatment, y = expression, fill = treatment)) +
- geom_boxplot() +
- geom_jitter() +
- scale_fill_manual("",
- breaks = c("CNTRL", "DEX"),
- labels = c("Baseline", "Treatment"),
- values = c("#B0BFBB", "#46866E")) +
- scale_x_discrete(breaks = c("CNTRL", "DEX"),
- labels = c("Baseline", "Treatment")) +
- theme_light() +
- theme(text = element_text(size= 16)) +
- xlab("") +
- ylab("Norm. Expression Level") +
- guides(fill = "none")
-
-ggsave(filename = "09_FigureII_E.pdf", width = 7, height = 6)
diff --git a/07_PlotsManuscript/10_FigureSII_A.R b/07_PlotsManuscript/10_FigureSII_A.R
deleted file mode 100644
index 9430e6f..0000000
--- a/07_PlotsManuscript/10_FigureSII_A.R
+++ /dev/null
@@ -1,90 +0,0 @@
-##################################################
-## Project: DexStim Mouse Brain
-## Date: 30.11.2021
-## Author: Nathalie
-##################################################
-# Number of DE genes and unique percentage
-
-library(data.table)
-library(ggplot2)
-library(dplyr)
-library(tidyverse)
-
-regions <-
- c("AMY", "PFC", "PVN", "CER", "vDG", "dDG", "vCA1", "dCA1")
-
-
-# 1. Read DE tables from all regions ----------
-
-list_reg_sig <- list()
-list_genes_sig <- list()
-
-for (reg in regions) {
- res <-
- fread(
- file = paste0(
- "~/Documents/ownCloud/DexStim_RNAseq_Mouse/tables",
- "/02_",
- reg,
- "_deseq2_Dex_1_vs_0_lfcShrink.txt"
- ),
- sep = "\t"
- )
- na_indices <- which(is.na(res$padj))
- res$padj[na_indices] <- 1
- res_sig <- res[res$padj <= 0.1, ]
- # res_sig <- res[res$log2FoldChange >= 1]
- list_reg_sig[[reg]] <- res_sig
- list_genes_sig[[reg]] <- rownames(res_sig)
-}
-
-# 2. Concatenate DE tables -----------------
-
-data <- bind_rows(list_reg_sig, .id = "region") %>%
- group_by(Ensembl_ID) %>%
- summarise(region = list(region))
-
-data_unique <- data %>%
- mutate(nr_regions = lengths(region)) %>%
- mutate(unique = (nr_regions == 1)) %>%
- unnest(cols = c(region)) %>%
- mutate("combined_id" = paste0(region, "-", Ensembl_ID))
-
-data_barplot <- data_unique %>%
- group_by(region, unique) %>%
- count() %>%
- group_by(region) %>%
- mutate(sum = sum(n))
-
-
-# 3. Stacked barplot -------------------------
-
-ggplot(data_barplot, aes(x = region, y = n, alpha = unique)) +
- geom_bar(position = "stack", stat = "identity", fill = "orange") +
- scale_alpha_manual(
- name = "",
- labels = c("DE in multiple regions", "DE unique"),
- values = c(1.0, 0.5)
- ) +
- xlab("Brain region") +
- ylab("# DE genes") +
- theme_light() +
- theme(
- axis.title.x = element_text(size = 15),
- axis.title.y = element_text(size = 15),
- axis.text.x = element_text(size = 12),
- axis.text.y = element_text(size = 12),
- legend.text = element_text(size = 12),
- # legend.title = element_blank(),
- legend.position = "top"
- ) +
- geom_text(aes(label = paste0(round((n / sum) * 100, digits = 1
- ), "%")),
- position = position_stack(vjust = 0.5),
- size = 4,
- show.legend = FALSE)
-ggsave(
- "10_FigureSII_A.pdf",
- width = 8,
- height = 6
-)
diff --git a/07_PlotsManuscript/11_FigureSII_B.R b/07_PlotsManuscript/11_FigureSII_B.R
deleted file mode 100644
index 3858189..0000000
--- a/07_PlotsManuscript/11_FigureSII_B.R
+++ /dev/null
@@ -1,92 +0,0 @@
-##################################################
-## Project: DexStim Mouse Brain
-## Date: 30.11.2021
-## Author: Nathalie
-##################################################
-# Number of hub genes and unique percentage
-
-library(data.table)
-library(ggplot2)
-library(dplyr)
-library(tidyverse)
-
-regions <-
- c("AMY", "PFC", "PVN", "CER", "vDG", "dDG", "vCA1", "dCA1")
-
-
-# 1. Read DE tables from all regions ----------
-
-list_reg_sig <- list()
-list_genes_sig <- list()
-
-for (reg in regions) {
- res <-
- fread(
- file = paste0(
- "~/Documents/ownCloud/DexStim_RNAseq_Mouse/tables/coExpression_kimono/03_AnalysisFuncoup/",
- "/04_",
- reg,
- "_funcoup_differential_nodebetweennessNorm_betacutoff0.01.csv"
- ),
- sep = ","
- )
- na_indices <- which(is.na(res$nodebetweenness_norm))
- res$padj[na_indices] <- 0
- res_sig <- res[res$nodebetweenness_norm >= 1.0, ]
- list_reg_sig[[reg]] <- res_sig
- list_genes_sig[[reg]] <- rownames(res_sig)
-}
-
-
-# 2. Concatenate hub tables -----------------
-
-data <- bind_rows(list_reg_sig, .id = "region") %>%
- group_by(ensembl_id) %>%
- summarise(region = list(region))
-
-data_unique <- data %>%
- mutate(nr_regions = lengths(region)) %>%
- mutate(unique = (nr_regions == 1)) %>%
- unnest(cols = c(region)) %>%
- mutate("combined_id" = paste0(region, "-", ensembl_id))
-
-data_barplot <- data_unique %>%
- group_by(region, unique) %>%
- count() %>%
- group_by(region) %>%
- mutate(sum = sum(n))
-
-
-# 3. Stacked barplot -------------------------
-
-ggplot(data_barplot, aes(x = region, y = n, alpha = unique)) +
- geom_bar(position = "stack", stat = "identity", fill = "darkred") +
- scale_alpha_manual(
- name = "",
- labels = c("Hub in multiple regions", "Hub unique"),
- values = c(1.0, 0.5)
- ) +
- xlab("Brain region") +
- ylab("# Hub genes") +
- theme_light() +
- theme(
- axis.title.x = element_text(size = 15),
- axis.title.y = element_text(size = 15),
- axis.text.x = element_text(size = 12),
- axis.text.y = element_text(size = 12),
- legend.text = element_text(size = 12),
- # legend.title = element_blank(),
- legend.position = "top"
- ) +
- geom_text(aes(label = paste0(round((n / sum) * 100, digits = 1
- ), "%")),
- position = position_stack(vjust = 0.5),
- size = 4,
- color = "white",
- show.legend = FALSE)
-
-ggsave(
- "11_FigureSII_B.pdf",
- width = 8,
- height = 6
-)
diff --git a/07_PlotsManuscript/12_FigureSII_C.R b/07_PlotsManuscript/12_FigureSII_C.R
deleted file mode 100644
index 9083131..0000000
--- a/07_PlotsManuscript/12_FigureSII_C.R
+++ /dev/null
@@ -1,248 +0,0 @@
-##################################################
-## Project: DexStim Mouse Brain
-## Date: 30.11.2021
-## Author: Nathalie
-##################################################
-# GO enrichment for DE genes across all regions
-
-library(clusterProfiler)
-library(org.Mm.eg.db)
-library(ggplot2)
-library(dplyr)
-library(stringr)
-library(writexl)
-
-
-basepath <- "~/Documents/ownCloud/DexStim_RNAseq_Mouse/"
-
-
-# 0. Read genes DE in all regions and background -----------------
-genes <- read.table(file = paste0(basepath, "tables/06_overlap_AMY-CER-PFC-PVN-dDG-vDG-dCA1-vCA1_entrezID.txt"),
- header = FALSE)[,1]
-
-# background are all genes in out dataset
-background <- read.table(file = paste0(basepath, "tables/06_background_entrezID.txt"),
- header = FALSE)[,1]
-
-
-
-# 1.1 GO enrichment for genes DE in all regions ---------------------
-
-# GO enrichment
-min_genes <- round(length(genes)/10)
-ego <- enrichGO(gene = as.character(genes),
- universe = as.character(background),
- OrgDb = org.Mm.eg.db,
- ont = "BP",
- pAdjustMethod = "BH",
- minGSSize = min_genes, # min number of genes associated with GO term
- maxGSSize = 10000, # max number of genes associated with GO term
- readable = TRUE)@result
-head(ego, n = 20)
-
-
-# 1.2 GO enrichment for upregulated genes DE in all regions ---------------------
-genes_up <- read.table(file = paste0(basepath, "tables/06_overlap_AMY-CER-PFC-PVN-dDG-vDG-dCA1-vCA1_up_entrezID.txt"),
- header = FALSE)[,1]
-
-# GO enrichment
-min_genes <- round(length(genes_up)/10)
-ego_up <- enrichGO(gene = as.character(genes_up),
- universe = as.character(background),
- OrgDb = org.Mm.eg.db,
- ont = "BP",
- pAdjustMethod = "BH",
- minGSSize = min_genes, # min number of genes associated with GO term
- maxGSSize = 10000, # max number of genes associated with GO term
- readable = TRUE)@result
-head(ego_up, n = 20)
-
-
-
-
-# 1.3 GO enrichment for downregulated genes DE in all regions ---------------------
-genes_down <- read.table(
- file = paste0(basepath,
- "tables/06_overlap_AMY-CER-PFC-PVN-dDG-vDG-dCA1-vCA1_down_entrezID.txt"),
- header = FALSE)[,1]
-
-# GO enrichment
-min_genes <- length(genes_down)/10
-ego_down <- enrichGO(gene = as.character(genes_down),
- universe = as.character(background),
- OrgDb = org.Mm.eg.db,
- ont = "BP",
- pAdjustMethod = "BH",
- minGSSize = min_genes, # min number of genes associated with GO term
- maxGSSize = 10000, # max number of genes associated with GO term
- readable = TRUE)@result
-head(ego_down, n = 20)
-
-
-# 1.4 Merge dataframes from all, up and downregulated genes --------------------
-
-data_go <- left_join(ego, ego_down, by = c("ID", "Description"),
- suffix = c(".all", ".down"))
-data_go <- left_join(data_go, ego_up, by = c("ID", "Description"),
- suffix = c("", ".up"))
-# apparently not all entrez ids are valid --> not all taken into account in enrichment analysis
-ngenes_rel <- 165
-nback_rel <- 12089
-# add columns relevant for gene ratio and odds ratio
-data_go <- data_go %>%
- mutate(n_genes = as.numeric(str_extract(data_go$BgRatio.all, "^\\d+"))) %>%
- mutate(gr = Count.all/n_genes*100) %>%
- mutate(b = ngenes_rel - Count.all,
- c = n_genes - Count.all,
- d = nback_rel - Count.all,
- or = log2(Count.all*d)-log2(b*c))
-
-data_heat <- data_go[1:30,c("Description", "p.adjust.down", "p.adjust")] %>%
- tidyr::pivot_longer(cols = p.adjust.down:p.adjust)
-
-
-# 1.5 Barplot -------------------------------
-# gene ratio
-bp.1 <-
- ggplot(data = data_go[1:25,], aes(
- x = factor(Description, levels = rev(data_go$Description[1:25])),
- y = gr
- )) +
- geom_bar(stat = "identity", position = "stack",
- fill = "#0F5057") +
- ylab("% Gene Ratio") +
- xlab("") +
- theme_light() +
- theme(
- axis.title.y = element_text(size = 14),
- axis.text.x = element_text(size = 12),
- axis.title.x = element_text(size =14),
- axis.text.y = element_text(size = 12),
- legend.position = "top",
- legend.text = element_text(size = 10)
- ) +
- coord_flip()
-
-# odds ratio
-bp.2 <-
- ggplot(data = data_go[1:25,], aes(
- x = factor(Description, levels = rev(data_go$Description[1:25])),
- y = or
- )) +
- geom_bar(stat = "identity", position = "stack",
- fill = "#FAA916") +
- ylab("Odds Ratio") +
- theme_light() +
- theme(
- axis.title.y = element_text(size = 14),
- axis.text.x = element_text(size = 12),
- axis.title.x = element_text(size =14),
- axis.text.y = element_text(size = 12),
- legend.position = "top",
- legend.text = element_text(size = 10)
- ) +
- coord_flip()
-
-# p-value
-bp.3 <-
- ggplot(data = data_go[1:25,], aes(
- x = factor(Description, levels = rev(data_go$Description[1:25])),
- y = -log10(p.adjust.all)
- )) +
- geom_bar(stat = "identity", position = "stack",
- fill = "#D45E60") +
- geom_hline(yintercept = -log10(0.05),linetype="dashed", color = "red") +
- ylab("-log10(FDR)") +
- theme_light() +
- theme(
- axis.title.y = element_text(size = 14),
- axis.text.x = element_text(size = 12),
- axis.title.x = element_text(size =14),
- axis.text.y = element_text(size = 12),
- legend.position = "top",
- legend.text = element_text(size = 10)
- ) +
- coord_flip()
-
-hm.1 <-
- ggplot(data = data_heat, aes(
- x = factor(Description, levels = rev(data_go$Description[1:25])),
- y = name,
- fill = value <= 0.01
- )) +
- geom_tile() +
- scale_y_discrete(name ="sig. GO term",
- limits=c("p.adjust","p.adjust.down"),
- labels=c("upreg.", "downreg.")) +
- scale_fill_manual(
- name = "GO term sig.",
- values = c("darkgrey", "black")
- ) +
- ylab("sig. GO term") +
- theme_light() +
- theme(
- axis.title.y = element_text(size = 14),
- axis.text.x = element_text(size = 12),
- axis.title.x = element_text(size =14),
- axis.text.y = element_text(size = 12),
- #legend.position = "top",
- legend.text = element_text(size = 10)
- ) +
- coord_flip()
-
-# combined plot
-comb <- ggarrange(bp.1,
- bp.2 +
- theme(axis.text.y = element_blank(),
- axis.ticks.y = element_blank(),
- axis.title.y = element_blank() ),
- bp.3 +
- theme(axis.text.y = element_blank(),
- axis.ticks.y = element_blank(),
- axis.title.y = element_blank() ),
- hm.1 +
- theme(axis.text.y = element_blank(),
- axis.ticks.y = element_blank(),
- axis.title.y = element_blank() ),
- nrow = 1,
- widths = c(2.5,1,1,1))
-
-# Save plot
-ggexport(
- comb,
- filename = "12_FigureSII_C.pdf",
- height = 10,
- width = 15
-)
-
-
-# bring table to a format similar as other tables
-tf <- data_go[1:25,]
-
-# add Category column
-tf$Category <- "GO_bp"
-# add GeneSet column
-tf$GeneSet <- tf$Description %>%
- stringr::str_replace_all(" ", "_") %>%
- stringr::str_to_upper()
-tf$GeneSet <- paste0("GO_", tf$GeneSet)
-# add -log10 transformed FDR pvalue
-tf$`[-log10 FDR]` <- -log10(tf$p.adjust.all)
-
-# subset to columns that should be printed
-tf <- tf %>%
- dplyr::select(Category, GeneSet, n_genes, Count, b, c, d, gr, or, pvalue.all,
- p.adjust.all, `[-log10 FDR]`, geneID.all) %>%
- dplyr::rename(N_genes = n_genes,
- N_overlap_A = Count,
- `Input that does not overlap_B` = b,
- `GO term genes - overlap_C` = c,
- `Not GO term genes and not in my geneset_D` = d,
- `Genes ratio` = gr,
- `OR[log2(a*d)-log2(b*c)]` = or,
- p = pvalue.all,
- adjP = p.adjust.all,
- genes = geneID.all)
-
-# save to a excel file
-write_xlsx(tf, "~/Documents/ownCloud/DexStim_RNAseq_Mouse/manuscript/Figures/Figure II_PFC/GOterms_DEgenesAllRegions.xlsx")
diff --git a/07_PlotsManuscript/13_FigureSII_D.R b/07_PlotsManuscript/13_FigureSII_D.R
deleted file mode 100644
index 8bc2fca..0000000
--- a/07_PlotsManuscript/13_FigureSII_D.R
+++ /dev/null
@@ -1,199 +0,0 @@
-##################################################
-## Project: DexStim Mouse Brain
-## Date: 30.11.2021
-## Author: Nathalie
-##################################################
-# GO enrichment for hub genes across all regions
-
-
-library(data.table)
-library(ggplot2)
-library(dplyr)
-library(tidyverse)
-library(org.Mm.eg.db)
-library(clusterProfiler)
-library(ggpubr)
-library(writexl)
-
-regions <-
- c("AMY", "PFC", "PVN", "CER", "vDG", "dDG", "vCA1", "dCA1")
-
-
-# 1. Read DE tables from all regions ----------
-
-list_reg_sig <- list()
-list_genes_sig <- list()
-
-for (reg in regions) {
- res <-
- fread(
- file = paste0(
- "~/Documents/ownCloud/DexStim_RNAseq_Mouse/tables/coExpression_kimono/03_AnalysisFuncoup/",
- "/04_",
- reg,
- "_funcoup_differential_nodebetweennessNorm_betacutoff0.01.csv"
- ),
- sep = ","
- )
- na_indices <- which(is.na(res$nodebetweenness_norm))
- res$padj[na_indices] <- 0
- res_sig <- res[res$nodebetweenness_norm >= 1.0, ]
- list_reg_sig[[reg]] <- res_sig
- list_genes_sig[[reg]] <- res_sig$ensembl_id
-}
-
-hub_shared <- Reduce(intersect, list_genes_sig)
-genes <- mapIds(org.Mm.eg.db, keys = hub_shared, keytype = "ENSEMBL", column="ENTREZID")
-
-# background are all genes in out dataset
-background <- read.table(file = paste0("~/Documents/ownCloud/DexStim_RNAseq_Mouse/tables/06_background_entrezID.txt"),
- header = FALSE)[,1]
-
-
-# 1.1 GO enrichment for genes DE in all regions ---------------------
-
-# GO enrichment
-min_genes <- round(length(genes)/10)
-ego <- enrichGO(gene = as.character(genes),
- universe = as.character(background),
- OrgDb = org.Mm.eg.db,
- ont = "BP",
- pAdjustMethod = "BH",
- minGSSize = min_genes, # min number of genes associated with GO term
- maxGSSize = 10000, # max number of genes associated with GO term
- readable = TRUE)@result
-head(ego, n = 20)
-
-
-# 1.4 Merge dataframes from all, up and downregulated genes --------------------
-
-# apparently not all entrez ids are valid --> not all taken into account in enrichment analysis
-ngenes_rel <- 7
-nback_rel <- 12089
-# add columns relevant for gene ratio and odds ratio
-data_go <- ego %>%
- mutate(n_genes = as.numeric(str_extract(ego$BgRatio, "^\\d+"))) %>%
- mutate(gr = Count/n_genes*100) %>%
- mutate(b = ngenes_rel - Count,
- c = n_genes - Count,
- d = nback_rel - Count,
- or = log2(Count*d)-log2(b*c))
-
-
-# 1.5 Barplot -------------------------------
-# gene ratio
-bp.1 <-
- ggplot(data = data_go[1:25,], aes(
- x = factor(Description, levels = rev(data_go$Description[1:25])),
- y = gr
- )) +
- geom_bar(stat = "identity", position = "stack",
- fill = "#0F5057") +
- ylab("% Gene Ratio") +
- xlab("") +
- theme_light() +
- theme(
- axis.title.y = element_text(size = 14),
- axis.text.x = element_text(size = 12),
- axis.title.x = element_text(size =14),
- axis.text.y = element_text(size = 12),
- legend.position = "top",
- legend.text = element_text(size = 10)
- ) +
- coord_flip()
-
-# odds ratio
-bp.2 <-
- ggplot(data = data_go[1:25,], aes(
- x = factor(Description, levels = rev(data_go$Description[1:25])),
- y = or
- )) +
- geom_bar(stat = "identity", position = "stack",
- fill = "#FAA916") +
- ylab("Odds Ratio") +
- theme_light() +
- theme(
- axis.title.y = element_text(size = 14),
- axis.text.x = element_text(size = 12),
- axis.title.x = element_text(size =14),
- axis.text.y = element_text(size = 12),
- legend.position = "top",
- legend.text = element_text(size = 10)
- ) +
- coord_flip()
-
-# p-value
-bp.3 <-
- ggplot(data = data_go[1:25,], aes(
- x = factor(Description, levels = rev(data_go$Description[1:25])),
- y = -log10(p.adjust)
- )) +
- geom_bar(stat = "identity", position = "stack",
- fill = "#D45E60") +
- geom_hline(yintercept = -log10(0.05),linetype="dashed", color = "red") +
- ylab("-log10(FDR)") +
- theme_light() +
- theme(
- axis.title.y = element_text(size = 14),
- axis.text.x = element_text(size = 12),
- axis.title.x = element_text(size =14),
- axis.text.y = element_text(size = 12),
- legend.position = "top",
- legend.text = element_text(size = 10)
- ) +
- coord_flip()
-
-
-# combined plot
-comb <- ggarrange(bp.1,
- bp.2 +
- theme(axis.text.y = element_blank(),
- axis.ticks.y = element_blank(),
- axis.title.y = element_blank() ),
- bp.3 +
- theme(axis.text.y = element_blank(),
- axis.ticks.y = element_blank(),
- axis.title.y = element_blank() ),
- nrow = 1,
- widths = c(2.5,1,1))
-
-# Save plot
-ggexport(
- comb,
- filename = "13_FigureSII_D.pdf",
- height = 10,
- width = 13
-)
-
-
-# bring table to a format similar as other tables
-tf <- data_go[1:25,]
-
-# add Category column
-tf$Category <- "GO_bp"
-# add GeneSet column
-tf$GeneSet <- tf$Description %>%
- stringr::str_replace_all(" ", "_") %>%
- stringr::str_to_upper()
-tf$GeneSet <- paste0("GO_", tf$GeneSet)
-# add -log10 transformed FDR pvalue
-tf$`[-log10 FDR]` <- -log10(tf$p.adjust)
-
-# subset to columns that should be printed
-tf <- tf %>%
- dplyr::select(Category, GeneSet, n_genes, Count, b, c, d, gr, or, pvalue,
- p.adjust, `[-log10 FDR]`, geneID) %>%
- dplyr::rename(N_genes = n_genes,
- N_overlap_A = Count,
- `Input that does not overlap_B` = b,
- `GO term genes - overlap_C` = c,
- `Not GO term genes and not in my geneset_D` = d,
- `Genes ratio` = gr,
- `OR[log2(a*d)-log2(b*c)]` = or,
- p = pvalue,
- adjP = p.adjust,
- genes = geneID)
-
-# save to a excel file
-write_xlsx(tf, "~/Documents/ownCloud/DexStim_RNAseq_Mouse/manuscript/Figures/Figure II_PFC/GOterms_hubsAllRegions.xlsx")
-
diff --git a/07_PlotsManuscript/14_FigureSIV_gwas.R b/07_PlotsManuscript/14_FigureSIV_gwas.R
deleted file mode 100644
index f7fa6a2..0000000
--- a/07_PlotsManuscript/14_FigureSIV_gwas.R
+++ /dev/null
@@ -1,75 +0,0 @@
-##################################################
-## Project: DexStim Mouse Brain
-## Date: 30.11.2021
-## Author: Nathalie
-##################################################
-# GWAS enrichment plot for manuscript
-# Tcf4 neighbours?
-
-library(readxl)
-library(dplyr)
-library(ggplot2)
-library(ggpubr)
-
-# 1. Pathway enrichment for Abcd1 and neighbours
-
-data <- read_xlsx(path = "~/Documents/ownCloud/DexStim_RNAseq_Mouse/manuscript/Figures/Figure IV_GWAS/TCF4/FUMA_diffTcf4PFCnetwork_gene2func68788/FUMA_diffPFCTcf4.xlsx",
- sheet = "GWAS")
-
-data <- arrange(data, desc(`[-log10]`))
-data$`my adj p` <- as.numeric(data$`my adj p`)
-data <- data[data$`my adj p` <= 0.05,]
-data <- data[!is.na(data$`my adj p`),]
-
-# GeneSet as factor
-data$GeneSet <- factor(data$GeneSet, levels = data$GeneSet)
-
-
-# bar plot gene ratio
-gr <- ggplot(data, aes(x = GeneSet, y = `Genes ratio`) ) +
- geom_bar(position = position_dodge2(reverse=TRUE), stat="identity", fill = "#0F5057") +
- scale_fill_manual() +
- theme_light() +
- coord_flip() +
- scale_x_discrete(limits = rev) +
- theme(text = element_text(size= 14)) +
- ylab("% Gene Ratio") +
- labs(x = NULL)
-
-# bar plot odds ratio
-or <- ggplot(data, aes(x = GeneSet, y = `OR[log2(a*d)-log2(b*c)]`) ) +
- geom_bar(position = position_dodge2(reverse=TRUE), stat="identity", fill = "#FAA916") +
- scale_fill_manual() +
- theme_light() +
- coord_flip() +
- scale_x_discrete(limits = rev) +
- theme(text = element_text(size= 14)) +
- ylab("Odds Ratio") +
- labs(x = NULL)
-
-# barplot fdr p-value
-fdr <- ggplot(data, aes(x = GeneSet, y = `[-log10]`) ) +
- geom_bar(position = position_dodge2(reverse=TRUE), stat="identity", fill = "#D45E60") +
- geom_hline(yintercept = -log10(0.05),linetype="dashed", color = "red") +
- theme_light() +
- coord_flip() +
- scale_x_discrete(limits = rev) +
- theme(text = element_text(size= 14)) +
- ylab("-log10(FDR)") +
- labs(x = NULL)
-
-# combined barplot
-comb <- ggarrange(gr,
- or +
- theme(axis.text.y = element_blank(),
- axis.ticks.y = element_blank(),
- axis.title.y = element_blank() ),
- fdr +
- theme(axis.text.y = element_blank(),
- axis.ticks.y = element_blank(),
- axis.title.y = element_blank() ),
- nrow = 1,
- widths = c(1.8,1,1))
-
-ggexport(comb, filename = "14_FigureSIV_gwas.pdf", width = 14, height = 8)
-