From 98cde1c2bcc33366f71d7f63f907601a6923dd96 Mon Sep 17 00:00:00 2001 From: Tom Kuipers Date: Mon, 10 May 2021 13:47:06 +0200 Subject: [PATCH 01/31] Version update --- DESCRIPTION | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/DESCRIPTION b/DESCRIPTION index 34249d9..06d8f95 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,7 +1,7 @@ Package: dgeAnalysis Type: Package Title: dgeAnalysis -Version: 1.4.3 +Version: 1.4.4 Author@R: person("Tom", "Kuipers", email = "t.b.kuipers@outlook.com", role = c("aut", "cre")) Description: From 02854d273e4d1fb7ed7d24f76c482bd5064fbfab Mon Sep 17 00:00:00 2001 From: Tom Kuipers Date: Mon, 10 May 2021 13:47:49 +0200 Subject: [PATCH 02/31] Fix false warning, where contrast are not the same --- CHANGELOG.md | 4 ++++ inst/src/tabs/run_analysis/svr_run.R | 2 +- 2 files changed, 5 insertions(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index a3d3fe4..8cb2015 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -4,6 +4,10 @@ Changelog +version 1.4.4 +--------------------------- ++ Fix false warning: Contrast are the same error + version 1.4.3 --------------------------- + Add prior count to CPM calculation diff --git a/inst/src/tabs/run_analysis/svr_run.R b/inst/src/tabs/run_analysis/svr_run.R index 359fb8f..3d86702 100644 --- a/inst/src/tabs/run_analysis/svr_run.R +++ b/inst/src/tabs/run_analysis/svr_run.R @@ -57,7 +57,7 @@ preMarkdownChecks <- reactive ({ return("Wrong analysis mode!") } else if (is.null(input$matrix_val1) | is.null(input$matrix_val2)) { return("One of the contrast is empty!") - } else if (paste0(input$matrix_val1) == paste0(input$matrix_val2)) { + } else if (all(sort(input$matrix_val1) == sort(input$matrix_val2))) { return("Contrasts cant be the same!") } else if (!is.null(input$setGeneName)) { if (input$setGeneName == "symbol" & !("geneName" %in% colnames(data_annotation()))) { From 17700b9ce60cc7723e1b105ef7cc70ca4f77f29e Mon Sep 17 00:00:00 2001 From: Tom Kuipers Date: Thu, 10 Jun 2021 14:20:30 +0200 Subject: [PATCH 03/31] update namespace --- NAMESPACE | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/NAMESPACE b/NAMESPACE index ba74dce..b9de5e6 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -28,9 +28,9 @@ export(informationBox) export(list2df) export(list2graph) export(ma_plot) -export(multidimensionalScaling2dPlot) -export(multidimensionalScaling3dPlot) +export(multidimensionalScalingPlot) export(pValuePlot) +export(pcaPlot) export(plot_module_trait_relation_heat) export(plot_modules) export(plot_power) @@ -41,8 +41,6 @@ export(plotly_dendro_heat) export(plotly_dendrogram) export(readCountsFromTable) export(relevelSamples) -export(samplePca2dPlot) -export(samplePca3dPlot) export(stackDge) export(startApp) export(topDgeHeatmapPlot) From 6b3b8b94425f3d662bf8ef63ed48bb1aee0a095b Mon Sep 17 00:00:00 2001 From: Tom Kuipers Date: Thu, 10 Jun 2021 14:21:51 +0200 Subject: [PATCH 04/31] Remove 3D plots and move normalized mds Change PCA tab to dimension reduction --- R/de_plots.R | 114 ++---------------- inst/src/server.R | 8 +- .../svr_pca.R => dimension/svr_dimension.R} | 97 +++++---------- .../ui_pca.R => dimension/ui_dimension.R} | 25 ++-- inst/src/tabs/norm_data/svr_norm.R | 72 ----------- inst/src/tabs/norm_data/ui_norm.R | 42 ------- inst/src/tabs/raw_data/svr_raw.R | 47 ++------ inst/src/tabs/raw_data/ui_raw.R | 30 +---- inst/src/ui.R | 4 +- 9 files changed, 72 insertions(+), 367 deletions(-) rename inst/src/tabs/{pca/svr_pca.R => dimension/svr_dimension.R} (54%) rename inst/src/tabs/{pca/ui_pca.R => dimension/ui_dimension.R} (72%) diff --git a/R/de_plots.R b/R/de_plots.R index d1883fe..00e3b99 100644 --- a/R/de_plots.R +++ b/R/de_plots.R @@ -390,7 +390,7 @@ voomPlot <- function(dge, sourceId) { #' #' @export -multidimensionalScaling2dPlot <- function(dge, color, sourceId) { +multidimensionalScalingPlot <- function(dge, color, sourceId) { logFC <- plotMDS(dge$counts, ndim = ncol(dge) - 1) for_plots <- data.frame(logFC$cmdscale.out) for_plots$group <- dge$samples[, color] @@ -411,7 +411,7 @@ multidimensionalScaling2dPlot <- function(dge, color, sourceId) { source = sourceId ) %>% plotly::layout( - title = paste("MDS Plot 2D"), + title = paste("MDS Plot"), xaxis = list(title = 'MDS1'), yaxis = list(title = 'MDS2'), clickmode = "event+select", @@ -426,50 +426,6 @@ multidimensionalScaling2dPlot <- function(dge, color, sourceId) { p } - -#' Calculates required values with 'plotMDS' method. -#' The plot is created with plotly with values retrieved from the mds object. -#' Plot is colored based on the selected column. -#' -#' @param dge DGE list object, containing samples and counts -#' @param color String, Column on wich colors should be based -#' -#' @return p, (Plotly object) plot -#' -#' @export - -multidimensionalScaling3dPlot <- function(dge, color) { - logFC <- plotMDS(dge$counts, ndim = ncol(dge) - 1) - for_plots <- data.frame(logFC$cmdscale.out) - for_plots$group <- dge$samples[, color] - - p <- plot_ly( - data = for_plots, - x = ~ X1, - y = ~ X2, - z = ~ X3, - color = ~ for_plots$group, - text = rownames(for_plots), - hoverinfo = 'text' - ) %>% - add_markers(marker = list(size = 5, opacity = 0.75)) %>% - plotly::layout( - title = paste("MDS Plot 3D"), - scene = list( - xaxis = list(title = 'MDS1'), - yaxis = list(title = 'MDS2'), - zaxis = list(title = 'MDS3') - ) - ) %>% - config(toImageButtonOptions = list( - format = "png", - filename = "mds3d", - width = 1500, - height = 1000 - )) - p -} - ## -------------------------------------------------------------------------- ## ----- PCA PLOTS ----- @@ -530,7 +486,8 @@ variancePcaPlot <- function(dge) { #' #' @export -samplePca2dPlot <- function(dge, color, getPC1, getPC2) { +pcaPlot <- function(dge, color, getPC1, getPC2) { + save(dge, color, getPC1, getPC2, file="test.RData") tdge <- t(dge$counts) tdge[!is.finite(tdge)] <- 0 pca <- prcomp(tdge, center = TRUE) @@ -553,10 +510,10 @@ samplePca2dPlot <- function(dge, color, getPC1, getPC2) { line = list(color = '#999999', width = 1)), key = ~ rownames(pca), - source = "pca_pca2d" + source = "pca" ) %>% plotly::layout( - title = 'PCA 2D', + title = 'PCA', xaxis = list(title = paste0( getPC1, " (", round(percent[getPC1, ] * 100, 2), "%)" )), @@ -568,64 +525,7 @@ samplePca2dPlot <- function(dge, color, getPC1, getPC2) { ) %>% config(toImageButtonOptions = list( format = "png", - filename = "pca2d", - width = 1500, - height = 1000 - )) - p -} - - -#' Columns and rows from DGE list are turned. -#' PCA is calculated with prcomp. -#' PC percentages are calculated. -#' Scatter plot is created based on selected PCs. -#' -#' @param dge DGE list object, containing samples and counts -#' @param color String, Column on wich colors should be based -#' @param getPC1 String, Selected PC to be plotted on x-axis -#' @param getPC2 String, Selected PC to be plotted on y-axis -#' @param getPC3 String, Selected PC to be plotted on z-axis -#' -#' @return p, (Plotly object) plot -#' -#' @export - -samplePca3dPlot <- function(dge, color, getPC1, getPC2, getPC3) { - tdge <- t(dge$counts) - tdge[!is.finite(tdge)] <- 0 - pca <- prcomp(tdge, center = TRUE) - percent <- data.frame(summary(pca)$importance[2, ]) - colnames(percent) <- "percent" - - pca <- data.frame(scale(tdge, center = T, scale = F) %*% pca$rotation) - pca$group <- dge$samples[, color] - - p <- plot_ly( - data = pca, - x = pca[[getPC1]], - y = pca[[getPC2]], - z = pca[[getPC3]], - color = ~ pca$group, - text = rownames(pca), - hoverinfo = 'text' - ) %>% - add_markers(marker = list(size = 5, opacity = 0.75)) %>% - plotly::layout(title = 'PCA 3D', - scene = list( - xaxis = list(title = paste0( - getPC1, " (", round(percent[getPC1, ] * 100, 2), "%)" - )), - yaxis = list(title = paste0( - getPC2, " (", round(percent[getPC2, ] * 100, 2), "%)" - )), - zaxis = list(title = paste0( - getPC3, " (", round(percent[getPC3, ] * 100, 2), "%)" - )) - )) %>% - config(toImageButtonOptions = list( - format = "png", - filename = "pca3d", + filename = "pca", width = 1500, height = 1000 )) diff --git a/inst/src/server.R b/inst/src/server.R index 0759e54..4f95ba9 100644 --- a/inst/src/server.R +++ b/inst/src/server.R @@ -30,7 +30,7 @@ server <- function(input, output, session) { alignment = "Alignment", raw_data = "Raw Data", norm_data = "Normalization", - pca = "PCA", + dim = "Dimension reduction", heatmaps = "Heatmaps", analysis = "DE analysis", bias = "Bias", @@ -93,8 +93,8 @@ server <- function(input, output, session) { icon = icon("chart-line") ), menuItem( - "PCA", - tabName = "pca", + "Dimension reduction", + tabName = "dimension", icon = icon("chart-line") ), menuItem( @@ -148,7 +148,7 @@ server <- function(input, output, session) { source("tabs/alignment/svr_alignment.R", local = TRUE) source("tabs/raw_data/svr_raw.R", local = TRUE) source("tabs/norm_data/svr_norm.R", local = TRUE) - source("tabs/pca/svr_pca.R", local = TRUE) + source("tabs/dimension/svr_dimension.R", local = TRUE) source("tabs/heatmaps/svr_heatmaps.R", local = TRUE) source("tabs/analysis/svr_analysis.R", local = TRUE) source("tabs/bias/svr_bias.R", local = TRUE) diff --git a/inst/src/tabs/pca/svr_pca.R b/inst/src/tabs/dimension/svr_dimension.R similarity index 54% rename from inst/src/tabs/pca/svr_pca.R rename to inst/src/tabs/dimension/svr_dimension.R index 6b1f7bf..1b4bff9 100644 --- a/inst/src/tabs/pca/svr_pca.R +++ b/inst/src/tabs/dimension/svr_dimension.R @@ -9,24 +9,24 @@ output[["variance_pca"]] <- renderPlotly({ }) }) -## samples PCA 2D -output[["samples_pca_2d"]] <- renderPlotly({ +## PCA +output[["pca"]] <- renderPlotly({ tryCatch({ checkReload() - samplePca2dPlot(inUse_normDge, - input$group_pca2d, - input$set_pca2d_pc1, - input$set_pca2d_pc2) + pcaPlot(inUse_normDge, + input$group_pca, + input$set_pca_pc1, + input$set_pca_pc2) }, error = function(err) { return(NULL) }) }) -## Set color of PCA 2d -output[["group_pca2d"]] <- renderUI({ +## Set color of PCA +output[["group_pca"]] <- renderUI({ tryCatch({ selectInput( - inputId = "group_pca2d", + inputId = "group_pca", label = "Color by:", choices = colnames(data_samples()) ) @@ -35,31 +35,18 @@ output[["group_pca2d"]] <- renderUI({ }) }) -## Set color of PCA 2d -output[["group_pca2d"]] <- renderUI({ - tryCatch({ - selectInput( - inputId = "group_pca2d", - label = "Color by:", - choices = colnames(data_samples()) - ) - }, error = function(err) { - return(NULL) - }) -}) - -## Set PCs for PCA 2d -output[["setpc_pca2d"]] <- renderUI({ +## Set PCs for PCA +output[["setpc_pca"]] <- renderUI({ tryCatch({ all_pc <- sprintf("PC%s", seq(1:ncol(inUse_normDge$counts))) tagList( selectInput( - inputId = "set_pca2d_pc1", + inputId = "set_pca_pc1", label = "Select X-axis PC:", choices = all_pc ), selectInput( - inputId = "set_pca2d_pc2", + inputId = "set_pca_pc2", label = "Select Y-axis PC:", choices = all_pc, selected = "PC2" @@ -70,11 +57,11 @@ output[["setpc_pca2d"]] <- renderUI({ }) }) -## Selected data points samples_pca_2d +## Selected data points samples_pca output[["selected_pca"]] <- DT::renderDataTable({ tryCatch({ checkReload() - s <- event_data(event = "plotly_selected", source = "pca_pca2d") + s <- event_data(event = "plotly_selected", source = "pca") if (is.null(s)) { s <- "" } @@ -86,27 +73,21 @@ output[["selected_pca"]] <- DT::renderDataTable({ }) }) -## samples PCA 3D -output[["samples_pca_3d"]] <- renderPlotly({ +## Normalized mds +output[["norm_un_cluster"]] <- renderPlotly({ tryCatch({ checkReload() - samplePca3dPlot( - inUse_normDge, - input$group_pca3d, - input$set_pca3d_pc1, - input$set_pca3d_pc2, - input$set_pca3d_pc3 - ) + multidimensionalScalingPlot(inUse_normDge, input$group_norm_mds, "norm_mds") }, error = function(err) { return(NULL) }) }) -## Set color of PCA 3d -output[["group_pca3d"]] <- renderUI({ +## Set color of mds +output[["group_norm_mds"]] <- renderUI({ tryCatch({ selectInput( - inputId = "group_pca3d", + inputId = "group_norm_mds", label = "Color by:", choices = colnames(data_samples()) ) @@ -115,29 +96,12 @@ output[["group_pca3d"]] <- renderUI({ }) }) -## Set PCs for PCA 3d -output[["setpc_pca3d"]] <- renderUI({ +## Selected data points norm_un_cluster +output[["selected_norm_mds"]] <- DT::renderDataTable({ tryCatch({ - all_pc <- sprintf("PC%s", seq(1:ncol(inUse_normDge$counts))) - tagList( - selectInput( - inputId = "set_pca3d_pc1", - label = "Select X-axis PC:", - choices = all_pc - ), - selectInput( - inputId = "set_pca3d_pc2", - label = "Select Y-axis PC:", - choices = all_pc, - selected = "PC2" - ), - selectInput( - inputId = "set_pca3d_pc3", - label = "Select Z-axis PC:", - choices = all_pc, - selected = "PC3" - ) - ) + checkReload() + s <- event_data(event = "plotly_selected", source = "norm_mds") + DT::datatable(data_samples()[unlist(s$key), , drop = FALSE], options = list(pageLength = 15, scrollX = TRUE)) }, error = function(err) { return(NULL) }) @@ -166,16 +130,17 @@ output[["variance_pca_info"]] <- renderUI({ informationBox(infoText) }) -output[["samples_pca_2d_info"]] <- renderUI({ +output[["pca_info"]] <- renderUI({ infoText <- "The 2D PCA plot shows the samples based on the two components. A PCA plot shows important information from a multivariate data table and shows the result as new variables (Principal Components)." informationBox(infoText) }) -output[["samples_pca_3d_info"]] <- renderUI({ +output[["norm_un_cluster_info"]] <- renderUI({ infoText <- - "The 3D PCA plot shows the samples based on the three components. A PCA plot shows important - information from a multivariate data table and shows the result as new variables (Principal Components)." + "This MDS plot (multidimensional scaling plot) can be viewed as a 2D plot with + calculations of two dimensions. With the MDS plot distances between samples is + shown, based on similarities and differences." informationBox(infoText) }) diff --git a/inst/src/tabs/pca/ui_pca.R b/inst/src/tabs/dimension/ui_dimension.R similarity index 72% rename from inst/src/tabs/pca/ui_pca.R rename to inst/src/tabs/dimension/ui_dimension.R index 6511f25..65b6f06 100644 --- a/inst/src/tabs/pca/ui_pca.R +++ b/inst/src/tabs/dimension/ui_dimension.R @@ -1,6 +1,6 @@ -tab_pca <- tabItem( - tabName = "pca", +tab_dimension <- tabItem( + tabName = "dimension", align = "center", br(), @@ -24,21 +24,21 @@ tab_pca <- tabItem( ), tabPanel( - title = "Sample PCA 2D", + title = "PCA", HTML('
'), sidebarLayout( position = "right", sidebarPanel( width = 3, - uiOutput("group_pca2d"), - uiOutput("setpc_pca2d"), + uiOutput("group_pca"), + uiOutput("setpc_pca"), br(), - uiOutput("samples_pca_2d_info"), + uiOutput("pca_info"), span(icon("copyright"), "LUMC - SASC", style = "color: #e3e3e3;") ), mainPanel( width = 9, - plotlyOutput("samples_pca_2d", height = "600px") %>% withSpinner() + plotlyOutput("pca", height = "600px") %>% withSpinner() ) ), HTML('
'), @@ -47,23 +47,24 @@ tab_pca <- tabItem( ), tabPanel( - title = "Sample PCA 3D", + title = "MDS", HTML('
'), sidebarLayout( position = "right", sidebarPanel( width = 3, - uiOutput("group_pca3d"), - uiOutput("setpc_pca3d"), + uiOutput("group_norm_mds"), br(), - uiOutput("samples_pca_3d_info"), + uiOutput("norm_un_cluster_info"), span(icon("copyright"), "LUMC - SASC", style = "color: #e3e3e3;") ), mainPanel( width = 9, - plotlyOutput("samples_pca_3d", height = "600px") %>% withSpinner() + plotlyOutput("norm_un_cluster", height = "600px") %>% withSpinner() ) ), + HTML('
'), + DT::dataTableOutput("selected_norm_mds") %>% withSpinner(), HTML('
') ) ) diff --git a/inst/src/tabs/norm_data/svr_norm.R b/inst/src/tabs/norm_data/svr_norm.R index a45c2fd..a6d6287 100644 --- a/inst/src/tabs/norm_data/svr_norm.R +++ b/inst/src/tabs/norm_data/svr_norm.R @@ -66,62 +66,6 @@ output[["selected_norm_voom"]] <- DT::renderDataTable({ }) }) -## Normalized Multidimensional scaling 2D -output[["norm_un_cluster_2d"]] <- renderPlotly({ - tryCatch({ - checkReload() - multidimensionalScaling2dPlot(inUse_normDge, input$group_norm_mds2d, "norm_mds2d") - }, error = function(err) { - return(NULL) - }) -}) - -## Set color of mds 2d -output[["group_norm_mds2d"]] <- renderUI({ - tryCatch({ - selectInput( - inputId = "group_norm_mds2d", - label = "Color by:", - choices = colnames(data_samples()) - ) - }, error = function(err) { - return(NULL) - }) -}) - -## Selected data points norm_un_cluster_2d -output[["selected_norm_mds2d"]] <- DT::renderDataTable({ - tryCatch({ - checkReload() - s <- event_data(event = "plotly_selected", source = "norm_mds2d") - DT::datatable(data_samples()[unlist(s$key), , drop = FALSE], options = list(pageLength = 15, scrollX = TRUE)) - }, error = function(err) { - return(NULL) - }) -}) - -## Normalized Multidimensional scaling 3D -output[["norm_un_cluster_3d"]] <- renderPlotly({ - tryCatch({ - checkReload() - multidimensionalScaling3dPlot(inUse_normDge, input$group_norm_mds3d) - }, error = function(err) { - return(NULL) - }) -}) - -## Set color of mds 3d -output[["group_norm_mds3d"]] <- renderUI({ - tryCatch({ - selectInput( - inputId = "group_norm_mds3d", - label = "Color by:", - choices = colnames(data_samples()) - ) - }, error = function(err) { - return(NULL) - }) -}) ## INFORMATION BOXES @@ -150,19 +94,3 @@ output[["norm_voom_plot_info"]] <- renderUI({ filtering of low expressed genes will be visible in this plot." informationBox(infoText) }) - -output[["norm_un_cluster_2d_info"]] <- renderUI({ - infoText <- - "This MDS plot (multidimensional scaling plot) can be viewed as a 2D plot with - calculations of two dimensions. With the MDS plot distances between samples is - shown, based on similarities and differences." - informationBox(infoText) -}) - -output[["norm_un_cluster_3d_info"]] <- renderUI({ - infoText <- - "This MDS plot (multidimensional scaling plot) can be viewed as a 3D plot with - calculations of three dimensions. With the MDS plot distances between samples is - shown, based on similarities and differences." - informationBox(infoText) -}) diff --git a/inst/src/tabs/norm_data/ui_norm.R b/inst/src/tabs/norm_data/ui_norm.R index 008a8d5..89cd5e6 100644 --- a/inst/src/tabs/norm_data/ui_norm.R +++ b/inst/src/tabs/norm_data/ui_norm.R @@ -71,48 +71,6 @@ tab_norm_data <- tabItem( HTML('
'), DT::dataTableOutput("selected_norm_voom") %>% withSpinner(), HTML('
') - ), - - tabPanel( - title = "Multidimensional scaling 2D", - HTML('
'), - sidebarLayout( - position = "right", - sidebarPanel( - width = 3, - uiOutput("group_norm_mds2d"), - br(), - uiOutput("norm_un_cluster_2d_info"), - span(icon("copyright"), "LUMC - SASC", style = "color: #e3e3e3;") - ), - mainPanel( - width = 9, - plotlyOutput("norm_un_cluster_2d", height = "600px") %>% withSpinner() - ) - ), - HTML('
'), - DT::dataTableOutput("selected_norm_mds2d") %>% withSpinner(), - HTML('
') - ), - - tabPanel( - title = "Multidimensional scaling 3D", - HTML('
'), - sidebarLayout( - position = "right", - sidebarPanel( - width = 3, - uiOutput("group_norm_mds3d"), - br(), - uiOutput("norm_un_cluster_3d_info"), - span(icon("copyright"), "LUMC - SASC", style = "color: #e3e3e3;") - ), - mainPanel( - width = 9, - plotlyOutput("norm_un_cluster_3d", height = "600px") %>% withSpinner() - ) - ), - HTML('
') ) ) ) diff --git a/inst/src/tabs/raw_data/svr_raw.R b/inst/src/tabs/raw_data/svr_raw.R index 288609f..eb52235 100644 --- a/inst/src/tabs/raw_data/svr_raw.R +++ b/inst/src/tabs/raw_data/svr_raw.R @@ -73,21 +73,21 @@ output[["selected_raw_voom"]] <- DT::renderDataTable({ }) }) -## Multidimensional scaling 2D -output[["un_cluster_2d"]] <- renderPlotly({ +## Multidimensional scaling +output[["un_cluster"]] <- renderPlotly({ tryCatch({ checkReload() - multidimensionalScaling2dPlot(get_raw_dge(), input$group_raw_mds2d, "raw_mds2d") + multidimensionalScalingPlot(get_raw_dge(), input$group_raw_mds, "raw_mds") }, error = function(err) { return(NULL) }) }) -## Set color of mds 2d -output[["group_raw_mds2d"]] <- renderUI({ +## Set color of mds +output[["group_raw_mds"]] <- renderUI({ tryCatch({ selectInput( - inputId = "group_raw_mds2d", + inputId = "group_raw_mds", label = "Color by:", choices = colnames(data_samples()) ) @@ -96,11 +96,11 @@ output[["group_raw_mds2d"]] <- renderUI({ }) }) -## Selected data points un_cluster_2d -output[["selected_raw_mds2d"]] <- DT::renderDataTable({ +## Selected data points un_cluster +output[["selected_raw_mds"]] <- DT::renderDataTable({ tryCatch({ checkReload() - s <- event_data(event = "plotly_selected", source = "raw_mds2d") + s <- event_data(event = "plotly_selected", source = "raw_mds") if (is.null(s)) { s <- "" } @@ -112,25 +112,6 @@ output[["selected_raw_mds2d"]] <- DT::renderDataTable({ }) }) -## Multidimensional scaling 3D -output[["un_cluster_3d"]] <- renderPlotly({ - tryCatch({ - checkReload() - multidimensionalScaling3dPlot(get_raw_dge(), input$group_raw_mds3d) - }, error = function(err) { - return(NULL) - }) -}) - -## Set color of mds 2d -output[["group_raw_mds3d"]] <- renderUI({ - tryCatch({ - selectInput("group_raw_mds3d", "Color by:", - colnames(data_samples())) - }, error = function(err) { - return(NULL) - }) -}) ## INFORMATION BOXES @@ -160,18 +141,10 @@ output[["raw_voom_plot_info"]] <- renderUI({ informationBox(infoText) }) -output[["un_cluster_2d_info"]] <- renderUI({ +output[["un_cluster_info"]] <- renderUI({ infoText <- "This MDS plot (multidimensional scaling plot) can be viewed as a 2D plot with calculations of two dimensions. With the MDS plot distances between samples is shown, based on similarities and differences." informationBox(infoText) }) - -output[["un_cluster_3d_info"]] <- renderUI({ - infoText <- - "This MDS plot (multidimensional scaling plot) can be viewed as a 3D plot with - calculations of three dimensions. With the MDS plot distances between samples is - shown, based on similarities and differences." - informationBox(infoText) -}) diff --git a/inst/src/tabs/raw_data/ui_raw.R b/inst/src/tabs/raw_data/ui_raw.R index 1f7980e..2b71e6e 100644 --- a/inst/src/tabs/raw_data/ui_raw.R +++ b/inst/src/tabs/raw_data/ui_raw.R @@ -72,44 +72,24 @@ tab_raw_data <- tabItem( ), tabPanel( - title = "Multidimensional scaling 2D", + title = "MDS", HTML('
'), sidebarLayout( position = "right", sidebarPanel( width = 3, - uiOutput("group_raw_mds2d"), + uiOutput("group_raw_mds"), br(), - uiOutput("un_cluster_2d_info"), + uiOutput("un_cluster_info"), span(icon("copyright"), "LUMC - SASC", style = "color: #e3e3e3;") ), mainPanel( width = 9, - plotlyOutput("un_cluster_2d", height = "600px") %>% withSpinner() + plotlyOutput("un_cluster", height = "600px") %>% withSpinner() ) ), HTML('
'), - DT::dataTableOutput("selected_raw_mds2d") %>% withSpinner(), - HTML('
') - ), - - tabPanel( - title = "Multidimensional scaling 3D", - HTML('
'), - sidebarLayout( - position = "right", - sidebarPanel( - width = 3, - uiOutput("group_raw_mds3d"), - br(), - uiOutput("un_cluster_3d_info"), - span(icon("copyright"), "LUMC - SASC", style = "color: #e3e3e3;") - ), - mainPanel( - width = 9, - plotlyOutput("un_cluster_3d", height = "600px") %>% withSpinner() - ) - ), + DT::dataTableOutput("selected_raw_mds") %>% withSpinner(), HTML('
') ) ) diff --git a/inst/src/ui.R b/inst/src/ui.R index 1e2c0e7..9362994 100644 --- a/inst/src/ui.R +++ b/inst/src/ui.R @@ -5,7 +5,7 @@ source("tabs/run_analysis/ui_run.R", local = TRUE) source("tabs/alignment/ui_alignment.R", local = TRUE) source("tabs/raw_data/ui_raw.R", local = TRUE) source("tabs/norm_data/ui_norm.R", local = TRUE) -source("tabs/pca/ui_pca.R", local = TRUE) +source("tabs/dimension/ui_dimension.R", local = TRUE) source("tabs/heatmaps/ui_heatmaps.R", local = TRUE) source("tabs/analysis/ui_analysis.R", local = TRUE) source("tabs/bias/ui_bias.R", local = TRUE) @@ -42,7 +42,7 @@ ui <- dashboardPage( tab_alignment, tab_raw_data, tab_norm_data, - tab_pca, + tab_dimension, tab_heatmaps, tab_analysis, tab_bias, From ff586e16c125f43f557104254bacd0f58d71e87e Mon Sep 17 00:00:00 2001 From: Tom Kuipers Date: Thu, 10 Jun 2021 14:22:06 +0200 Subject: [PATCH 05/31] man update --- man/multidimensionalScaling3dPlot.Rd | 23 -------------- ...Plot.Rd => multidimensionalScalingPlot.Rd} | 6 ++-- man/{samplePca2dPlot.Rd => pcaPlot.Rd} | 6 ++-- man/samplePca3dPlot.Rd | 31 ------------------- 4 files changed, 6 insertions(+), 60 deletions(-) delete mode 100644 man/multidimensionalScaling3dPlot.Rd rename man/{multidimensionalScaling2dPlot.Rd => multidimensionalScalingPlot.Rd} (84%) rename man/{samplePca2dPlot.Rd => pcaPlot.Rd} (88%) delete mode 100644 man/samplePca3dPlot.Rd diff --git a/man/multidimensionalScaling3dPlot.Rd b/man/multidimensionalScaling3dPlot.Rd deleted file mode 100644 index 17a0f92..0000000 --- a/man/multidimensionalScaling3dPlot.Rd +++ /dev/null @@ -1,23 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/de_plots.R -\name{multidimensionalScaling3dPlot} -\alias{multidimensionalScaling3dPlot} -\title{Calculates required values with 'plotMDS' method. -The plot is created with plotly with values retrieved from the mds object. -Plot is colored based on the selected column.} -\usage{ -multidimensionalScaling3dPlot(dge, color) -} -\arguments{ -\item{dge}{DGE list object, containing samples and counts} - -\item{color}{String, Column on wich colors should be based} -} -\value{ -p, (Plotly object) plot -} -\description{ -Calculates required values with 'plotMDS' method. -The plot is created with plotly with values retrieved from the mds object. -Plot is colored based on the selected column. -} diff --git a/man/multidimensionalScaling2dPlot.Rd b/man/multidimensionalScalingPlot.Rd similarity index 84% rename from man/multidimensionalScaling2dPlot.Rd rename to man/multidimensionalScalingPlot.Rd index f024f5b..238af8f 100644 --- a/man/multidimensionalScaling2dPlot.Rd +++ b/man/multidimensionalScalingPlot.Rd @@ -1,12 +1,12 @@ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/de_plots.R -\name{multidimensionalScaling2dPlot} -\alias{multidimensionalScaling2dPlot} +\name{multidimensionalScalingPlot} +\alias{multidimensionalScalingPlot} \title{Calculates required values with 'plotMDS' method. The plot is created with plotly with values retrieved from the mds object. Plot is colored based on the selected column.} \usage{ -multidimensionalScaling2dPlot(dge, color, sourceId) +multidimensionalScalingPlot(dge, color, sourceId) } \arguments{ \item{dge}{DGE list object, containing samples and counts} diff --git a/man/samplePca2dPlot.Rd b/man/pcaPlot.Rd similarity index 88% rename from man/samplePca2dPlot.Rd rename to man/pcaPlot.Rd index f91641d..7a2f093 100644 --- a/man/samplePca2dPlot.Rd +++ b/man/pcaPlot.Rd @@ -1,13 +1,13 @@ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/de_plots.R -\name{samplePca2dPlot} -\alias{samplePca2dPlot} +\name{pcaPlot} +\alias{pcaPlot} \title{Columns and rows from DGE list are turned. PCA is calculated with prcomp. PC percentages are calculated. Scatter plot is created based on selected PCs.} \usage{ -samplePca2dPlot(dge, color, getPC1, getPC2) +pcaPlot(dge, color, getPC1, getPC2) } \arguments{ \item{dge}{DGE list object, containing samples and counts} diff --git a/man/samplePca3dPlot.Rd b/man/samplePca3dPlot.Rd deleted file mode 100644 index 3ab2a20..0000000 --- a/man/samplePca3dPlot.Rd +++ /dev/null @@ -1,31 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/de_plots.R -\name{samplePca3dPlot} -\alias{samplePca3dPlot} -\title{Columns and rows from DGE list are turned. -PCA is calculated with prcomp. -PC percentages are calculated. -Scatter plot is created based on selected PCs.} -\usage{ -samplePca3dPlot(dge, color, getPC1, getPC2, getPC3) -} -\arguments{ -\item{dge}{DGE list object, containing samples and counts} - -\item{color}{String, Column on wich colors should be based} - -\item{getPC1}{String, Selected PC to be plotted on x-axis} - -\item{getPC2}{String, Selected PC to be plotted on y-axis} - -\item{getPC3}{String, Selected PC to be plotted on z-axis} -} -\value{ -p, (Plotly object) plot -} -\description{ -Columns and rows from DGE list are turned. -PCA is calculated with prcomp. -PC percentages are calculated. -Scatter plot is created based on selected PCs. -} From f418b36307d986033b0b5e27ecaf1b8512682deb Mon Sep 17 00:00:00 2001 From: Tom Kuipers Date: Thu, 10 Jun 2021 14:24:00 +0200 Subject: [PATCH 06/31] update changelog --- CHANGELOG.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index 8cb2015..3a4d395 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -6,6 +6,9 @@ Newest changes should be on top. --> version 1.4.4 --------------------------- ++ Move normalized MDS to Dimension reduction ++ Change PCA tab to Dimension reduction ++ Remove MDS 3D and PCA 3D + Fix false warning: Contrast are the same error version 1.4.3 From 73f9dd6ad216a3c76977c76b104e2c79c998623c Mon Sep 17 00:00:00 2001 From: Tom Kuipers Date: Thu, 10 Jun 2021 14:24:36 +0200 Subject: [PATCH 07/31] version number update --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index d71c5eb..4cc55bf 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # dgeAnalysis -This R package contains the R-Shiny application v1.4.3 developed to perform differential gene expression analysis. +This R package contains the R-Shiny application v1.4.4 developed to perform differential gene expression analysis. * dgeAnalysis manual: https://github.com/LUMC/dgeAnalysis/blob/master/MANUAL.pdf ## Installing From 4a6c3ffd4ba48ec8d7be639a9086befaa16527a5 Mon Sep 17 00:00:00 2001 From: Tom Kuipers Date: Thu, 10 Jun 2021 15:23:16 +0200 Subject: [PATCH 08/31] change pca function name --- inst/src/markdown/analysisDESeq2.Rmd | 2 +- inst/src/markdown/analysisEdgeR.Rmd | 2 +- inst/src/markdown/analysisLimma.Rmd | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/inst/src/markdown/analysisDESeq2.Rmd b/inst/src/markdown/analysisDESeq2.Rmd index 386ea1e..4bca9d7 100644 --- a/inst/src/markdown/analysisDESeq2.Rmd +++ b/inst/src/markdown/analysisDESeq2.Rmd @@ -165,7 +165,7 @@ normDge <- DGEList(counts = data.frame(counts(getSize, normalized = TRUE)), samp normDge$counts <- cpm(normDge, log = TRUE, prior.count = 1) countDistributionLinePlot(normDge) -samplePca2dPlot(normDge, design_base, "PC1", "PC2") +pcaPlot(normDge, design_base, "PC1", "PC2") ``` diff --git a/inst/src/markdown/analysisEdgeR.Rmd b/inst/src/markdown/analysisEdgeR.Rmd index 6010bb4..71f3f60 100644 --- a/inst/src/markdown/analysisEdgeR.Rmd +++ b/inst/src/markdown/analysisEdgeR.Rmd @@ -159,7 +159,7 @@ normDge <- calcNormFactors(highExprDge, method = "TMM") tempDge <- normDge tempDge$counts <- cpm(normDge, log = TRUE, prior.count = 1) countDistributionLinePlot(tempDge) -samplePca2dPlot(tempDge, design_base, "PC1", "PC2") +pcaPlot(tempDge, design_base, "PC1", "PC2") ``` diff --git a/inst/src/markdown/analysisLimma.Rmd b/inst/src/markdown/analysisLimma.Rmd index 1c69adf..05284e2 100644 --- a/inst/src/markdown/analysisLimma.Rmd +++ b/inst/src/markdown/analysisLimma.Rmd @@ -159,7 +159,7 @@ normDge <- calcNormFactors(highExprDge, method = "TMM") tempDge <- normDge tempDge$counts <- cpm(normDge, log = TRUE, prior.count = 1) countDistributionLinePlot(tempDge) -samplePca2dPlot(tempDge, design_base, "PC1", "PC2") +pcaPlot(tempDge, design_base, "PC1", "PC2") ``` From 08bf7deffc6347329b83ca2948d16425c95b0076 Mon Sep 17 00:00:00 2001 From: Tom Kuipers Date: Thu, 10 Jun 2021 15:23:44 +0200 Subject: [PATCH 09/31] add tsne plot --- CHANGELOG.md | 1 + NAMESPACE | 1 + R/de_plots.R | 69 +++++++++++++++++++++++-- inst/src/lib/libraries.R | 1 + inst/src/tabs/dimension/svr_dimension.R | 42 +++++++++++++++ inst/src/tabs/dimension/ui_dimension.R | 24 +++++++++ 6 files changed, 135 insertions(+), 3 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 3a4d395..4da93a2 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -6,6 +6,7 @@ Newest changes should be on top. --> version 1.4.4 --------------------------- ++ Add tSNE plot to Dimension reduction + Move normalized MDS to Dimension reduction + Change PCA tab to Dimension reduction + Remove MDS 3D and PCA 3D diff --git a/NAMESPACE b/NAMESPACE index b9de5e6..d654fa7 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -44,6 +44,7 @@ export(relevelSamples) export(stackDge) export(startApp) export(topDgeHeatmapPlot) +export(tsnePlot) export(variableHeatmapPlot) export(variancePcaPlot) export(volcanoPlot) diff --git a/R/de_plots.R b/R/de_plots.R index 00e3b99..c22c5b2 100644 --- a/R/de_plots.R +++ b/R/de_plots.R @@ -428,7 +428,7 @@ multidimensionalScalingPlot <- function(dge, color, sourceId) { ## -------------------------------------------------------------------------- -## ----- PCA PLOTS ----- +## ----- DIMENSION REDUCTION PLOTS ----- #' Columns and rows from DGE list are turned. @@ -478,7 +478,7 @@ variancePcaPlot <- function(dge) { #' Scatter plot is created based on selected PCs. #' #' @param dge DGE list object, containing samples and counts -#' @param color String, Column on wich colors should be based +#' @param color String, Column on which colors should be based #' @param getPC1 String, Selected PC to be plotted on x-axis #' @param getPC2 String, Selected PC to be plotted on y-axis #' @@ -487,7 +487,6 @@ variancePcaPlot <- function(dge) { #' @export pcaPlot <- function(dge, color, getPC1, getPC2) { - save(dge, color, getPC1, getPC2, file="test.RData") tdge <- t(dge$counts) tdge[!is.finite(tdge)] <- 0 pca <- prcomp(tdge, center = TRUE) @@ -532,6 +531,70 @@ pcaPlot <- function(dge, color, getPC1, getPC2) { p } + +#' Perplexity is set (max 30 & min 1), this depends on the number of samples +#' tsne model is calculated. +#' Scatter plot is created based on selected PCs. +#' +#' @param dge DGE list object, containing samples and counts +#' @param color String, Column on which colors should be based +#' +#' @return p, (Plotly object) plot +#' +#' @export + +tsnePlot <- function(dge, color) { + set.seed(1234) + + perplexity <- 30 + while (perplexity > 0) { + try({ + tsne_model <- Rtsne( + t(dge$counts), + perplexity = perplexity, + check_duplicates = FALSE, + normalize = FALSE + ) + }, silent = TRUE + ) + perplexity <- perplexity - 1 + } + + tsne_data <- as.data.frame(tsne_model$Y) + rownames(tsne_data) <- colnames(dge$counts) + tsne_data$color <- dge$samples[[color]] + + p <- plot_ly( + data = tsne_data, + x = ~V1, + y = ~V2, + type = "scattergl", + mode = "markers", + source = "tsne", + key = ~ rownames(tsne_data), + marker = list(size=15, + line = list(color = '#999999', + width = 1)), + color = ~color, + text = rownames(tsne_data), + hoverinfo = 'text') %>% + plotly::layout( + title = "t-SNE", + xaxis = list(title = "tSNE 1"), + yaxis = list(title = "tSNE 2"), + clickmode = "event+select", + dragmode = "select") %>% + config( + toImageButtonOptions = list( + format = "png", + filename = "t-SNE", + width = 1500, + height = 1000 + ) + ) + p +} + ## -------------------------------------------------------------------------- ## ----- HEATMAPS PLOTS ----- diff --git a/inst/src/lib/libraries.R b/inst/src/lib/libraries.R index 8a31599..30983a9 100644 --- a/inst/src/lib/libraries.R +++ b/inst/src/lib/libraries.R @@ -17,6 +17,7 @@ if (!require("tidyr")) install.packages("tidyr") if (!require("scales")) install.packages("scales") if (!require("broom")) install.packages("broom") if (!require("plotly")) install.packages("plotly") +if (!require("Rtsne")) install.packages("Rtsne") if (!require("rmarkdown")) install.packages("rmarkdown") ## Pathway analysis diff --git a/inst/src/tabs/dimension/svr_dimension.R b/inst/src/tabs/dimension/svr_dimension.R index 1b4bff9..270d39f 100644 --- a/inst/src/tabs/dimension/svr_dimension.R +++ b/inst/src/tabs/dimension/svr_dimension.R @@ -73,6 +73,40 @@ output[["selected_pca"]] <- DT::renderDataTable({ }) }) +## tSNE +output[["dim_tsne"]] <- renderPlotly({ + tryCatch({ + checkReload() + tsnePlot(inUse_normDge, input$group_dim_tsne) + }, error = function(err) { + return(NULL) + }) +}) + +## Set color of tSNE +output[["group_dim_tsne"]] <- renderUI({ + tryCatch({ + selectInput( + inputId = "group_dim_tsne", + label = "Color by:", + choices = colnames(data_samples()) + ) + }, error = function(err) { + return(NULL) + }) +}) + +## Selected data points tSNE +output[["selected_dim_tsne"]] <- DT::renderDataTable({ + tryCatch({ + checkReload() + s <- event_data(event = "plotly_selected", source = "tsne") + DT::datatable(data_samples()[unlist(s$key), , drop = FALSE], options = list(pageLength = 15, scrollX = TRUE)) + }, error = function(err) { + return(NULL) + }) +}) + ## Normalized mds output[["norm_un_cluster"]] <- renderPlotly({ tryCatch({ @@ -144,3 +178,11 @@ output[["norm_un_cluster_info"]] <- renderUI({ shown, based on similarities and differences." informationBox(infoText) }) + +output[["dim_tsne_info"]] <- renderUI({ + infoText <- + "t-distributed stochastic neighbor embedding (t-SNE) is a statistical method + for visualizing high-dimensional data by giving each datapoint a location in a + multi-dimensional map." + informationBox(infoText) +}) diff --git a/inst/src/tabs/dimension/ui_dimension.R b/inst/src/tabs/dimension/ui_dimension.R index 65b6f06..918b184 100644 --- a/inst/src/tabs/dimension/ui_dimension.R +++ b/inst/src/tabs/dimension/ui_dimension.R @@ -46,6 +46,29 @@ tab_dimension <- tabItem( HTML('
') ), + + tabPanel( + title = "t-SNE", + HTML('
'), + sidebarLayout( + position = "right", + sidebarPanel( + width = 3, + uiOutput("group_dim_tsne"), + br(), + uiOutput("dim_tsne_info"), + span(icon("copyright"), "LUMC - SASC", style = "color: #e3e3e3;") + ), + mainPanel( + width = 9, + plotlyOutput("dim_tsne", height = "600px") %>% withSpinner() + ) + ), + HTML('
'), + DT::dataTableOutput("selected_dim_tsne") %>% withSpinner(), + HTML('
') + ), + tabPanel( title = "MDS", HTML('
'), @@ -69,3 +92,4 @@ tab_dimension <- tabItem( ) ) ) + From 4149552025fb93b5213f3239ca01795618f5333d Mon Sep 17 00:00:00 2001 From: Tom Kuipers Date: Thu, 10 Jun 2021 15:24:05 +0200 Subject: [PATCH 10/31] man update --- man/pcaPlot.Rd | 2 +- man/tsnePlot.Rd | 23 +++++++++++++++++++++++ 2 files changed, 24 insertions(+), 1 deletion(-) create mode 100644 man/tsnePlot.Rd diff --git a/man/pcaPlot.Rd b/man/pcaPlot.Rd index 7a2f093..78dcd59 100644 --- a/man/pcaPlot.Rd +++ b/man/pcaPlot.Rd @@ -12,7 +12,7 @@ pcaPlot(dge, color, getPC1, getPC2) \arguments{ \item{dge}{DGE list object, containing samples and counts} -\item{color}{String, Column on wich colors should be based} +\item{color}{String, Column on which colors should be based} \item{getPC1}{String, Selected PC to be plotted on x-axis} diff --git a/man/tsnePlot.Rd b/man/tsnePlot.Rd new file mode 100644 index 0000000..4e64e2c --- /dev/null +++ b/man/tsnePlot.Rd @@ -0,0 +1,23 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/de_plots.R +\name{tsnePlot} +\alias{tsnePlot} +\title{Perplexity is set (max 30 & min 1), this depends on the number of samples +tsne model is calculated. +Scatter plot is created based on selected PCs.} +\usage{ +tsnePlot(dge, color) +} +\arguments{ +\item{dge}{DGE list object, containing samples and counts} + +\item{color}{String, Column on which colors should be based} +} +\value{ +p, (Plotly object) plot +} +\description{ +Perplexity is set (max 30 & min 1), this depends on the number of samples +tsne model is calculated. +Scatter plot is created based on selected PCs. +} From 68014d449c50a6a740ee6df84f700baad3de45f9 Mon Sep 17 00:00:00 2001 From: Tom Kuipers Date: Thu, 10 Jun 2021 15:43:43 +0200 Subject: [PATCH 11/31] update infobox --- CHANGELOG.md | 1 + inst/src/tabs/alignment/svr_alignment.R | 17 ++++++------ inst/src/tabs/analysis/svr_analysis.R | 36 ++++++++++++------------- inst/src/tabs/bias/svr_bias.R | 18 ++++++------- inst/src/tabs/dimension/svr_dimension.R | 14 +++++----- inst/src/tabs/gprofiler/svr_gprofiler.R | 32 +++++++++++----------- inst/src/tabs/heatmaps/svr_heatmaps.R | 6 ++--- inst/src/tabs/norm_data/svr_norm.R | 18 ++++++------- inst/src/tabs/raw_data/svr_raw.R | 10 +++---- 9 files changed, 76 insertions(+), 76 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 4da93a2..147e888 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -6,6 +6,7 @@ Newest changes should be on top. --> version 1.4.4 --------------------------- ++ Small infobox update + Add tSNE plot to Dimension reduction + Move normalized MDS to Dimension reduction + Change PCA tab to Dimension reduction diff --git a/inst/src/tabs/alignment/svr_alignment.R b/inst/src/tabs/alignment/svr_alignment.R index f4be44a..57673f8 100644 --- a/inst/src/tabs/alignment/svr_alignment.R +++ b/inst/src/tabs/alignment/svr_alignment.R @@ -67,20 +67,19 @@ output[["complex"]] <- renderPlotly({ output[["align_sum_info"]] <- renderUI({ infoText <- - "In the summary plots, a general overview can be seen of the count data. These plots show a - bar plot with the alignment results and a complexity plot. The alignment plot is a stacked bar - plot, where every 'row' stands for a sample and the stacked bars per row are the different - tags. These tags indicate the distribution of the read counts, given by a read counter (e.g. + "The summary plots provide a general overview of the counting data. These plots show a + bar chart showing the alignment results and a complexity plot. The alignment plot is a stacked bar + plot, where each 'row' represents a sample and the stacked bars per row are different + tags. These tags indicate the distribution of read counts given by a read counter (eg. HTSeq)." informationBox(infoText) }) output[["complex_info"]] <- renderUI({ infoText <- - "The complexity plots show how many reads cover a certain amount of genes. On the X-axis, - there is a ranking from zero to one thousand. This ranking stands for the number of genes. - On the Y-axis the proportion of the reads relative to the total amount of counts is shown. - Normally the expectation is a growing curve. Big deviations between samples of the number - of reads for a gene, will be visible." + "The complexity graphs show how many reads cover a given number of genes. On the X-axis, + there is a ranking from zero to a thousand. This arrangement represents the number of genes. + The Y-axis shows the proportion of reads in relation to the total number of gene counts. + Large variances between samples of the number or reads for a gene, will be visible." informationBox(infoText) }) diff --git a/inst/src/tabs/analysis/svr_analysis.R b/inst/src/tabs/analysis/svr_analysis.R index 1568b31..c7c8203 100644 --- a/inst/src/tabs/analysis/svr_analysis.R +++ b/inst/src/tabs/analysis/svr_analysis.R @@ -135,49 +135,49 @@ output[["p_val_plot"]] <- renderPlotly({ output[["de_ratio_info"]] <- renderUI({ infoText <- - "The DE ratio plot gives a quick overview of the amount of differentially expressed genes. - (down, not and up-regulated) The exact amount of genes are shown inside the plot. The dispersion - of the Y-axis is based on the percentages. This is calculated based on the total amount of genes + "The DE ratio plot provides a quick overview of the amount of differentially expressed genes. + (down, unregulated and up regulated) The exact number of genes is shown in the plot. the spread + of the Y-axis is based on the percentages. This is calculated based on the total number of genes after filtering." informationBox(infoText) }) output[["ma_plot_info"]] <- renderUI({ infoText <- - "The MA plot (Mean average), plots all genes after filtering. This plot creates a figure with - log-intensity ratios (M-values) and log-intensity averages (A-values). The MA plot can give a - view of all genes and their amount of expression in a comparison of two groups. On the - X-axis the Log2CPM values (A) are plotted against the Log2FC values on the Y-axis (M). In - this plot, the genes are colored based on the DE results (down, not and up-regulated). Also, + "The MA plot, plots all genes after filtering. This plot creates a figure with + log intensity ratios (M values) and log intensity means (A values). The MA plot can give a + overview of all genes and their amount of expression in a comparison of two groups. on the + X-axis plots the Log2CPM values (A) against the Log2FC values on the Y-axis (M). In + in this plot the genes are stained based on the DE results (down, unregulated and upregulated). so, the trend of expression is shown. This trend shows the average expression flow." informationBox(infoText) }) output[["volcano_plot_info"]] <- renderUI({ infoText <- - "The volcano plot also shows the most expressed genes and can give a good view of the + "The volcano plot also shows the most expressed genes and can give a good idea of the expression results. The X-axis contains the Log2FC (magnitude of change) and the Y-axis - shows -Log10 p-value (statistical significance). A great benefit of the volcano plot is that it - makes genes with a high fold-change, who are statistically significant, well visible. If genes - are down-regulated, they will be visible towards the left of the plot and up-regulated genes - more towards the right. The most statistically significant genes are placed towards the top." + shows -Log10 p-value (statistical significance). A major advantage of the volcano plot is that it + makes genes with a high fold-change (which are statistically significant) clearly visible. As genes + are down-regulated, they will be visible on the left side of the plot and up-regulated genes + more to the right. The most statistically significant genes are placed at the top." informationBox(infoText) }) output[["barcode_plot_info"]] <- renderUI({ infoText <- "The barcode plot shows the most expressed genes. The X-axis shows the Log2CPM - for every sample per gene. The Y-axis shows most expressed genes (from top to - bottom). Every bar stands for a sample. With this plot differences between samples for a + for each sample per gene. The Y-axis shows the most expressed genes (from top to + bottom). Each bar represents a sample. With this plot, differences between samples for a specific gene is visible." informationBox(infoText) }) output[["p_val_plot_info"]] <- renderUI({ infoText <- - "The p-value plot is used as a quality control plot. When all bars, for example, are all the - same height, then the analysis can be described as “noisy”. The ideal pattern would be that - bars around a p-value of zero are high. Next, the bars should get lower in a nice (steep) + "The p-value plot is used as a quality control plot. For example, if all bars are all + same height, the analysis can be described as “noisy”. The ideal pattern would be that + bars around a p-value of zero are high. Then the bars have to be in a nice (steep) curve and should be (almost) zero at the p-value of one." informationBox(infoText) }) diff --git a/inst/src/tabs/bias/svr_bias.R b/inst/src/tabs/bias/svr_bias.R index 2e1bf9c..1f9822d 100644 --- a/inst/src/tabs/bias/svr_bias.R +++ b/inst/src/tabs/bias/svr_bias.R @@ -101,28 +101,28 @@ output[["geneStrand_bias"]] <- renderPlotly({ output[["gc_bias_info"]] <- renderUI({ infoText <- - "The GC bias plots are generated based on the average log fold change on the Y-axis. The bias plots + "The GC bias plots are generated based on the mean change of the logFC on the Y-axis. The bias plots can only be used if an annotation is used to perform the analysis. If annotation values are - present, a column can be selected with the use of a drop-down button. The columns should - contain the text: 'GC', otherwise they will not be recognized as a column suitable + is present, a column can be selected using a drop-down button. The columns must + contain the text: 'GC', otherwise they will not be recognized as a suitable column for GC bias calculations." informationBox(infoText) }) output[["len_bias_info"]] <- renderUI({ infoText <- - "The feature-length bias plots are generated based on the average log fold change on the Y-axis. The bias plots + "The feature-length bias plots are generated from the mean change of the logFC on the Y-axis. The bias plots can only be used if an annotation is used to perform the analysis. If annotation values are - present, a column can be selected with the use of a drop-down button. The columns should - contain the text: 'length', otherwise they will not be recognized as a column suitable + is present, a column can be selected using a drop-down button. The columns must + contain the text: 'length', otherwise they will not be recognized as a suitable column for length bias calculations." informationBox(infoText) }) output[["geneStrand_info"]] <- renderUI({ infoText <- - "The gene strand bias plots are generated based on strand info (+ or -) and the number of - genes (DE or not). It shows the distribution of genes between strands. The gene strand plot can only - be generated when using an annotation file containing strand information about genes." + "The gene strand bias plots are generated based on strand info (+ or -) and number + genes (DE or not). It shows the distribution of genes between strands. The Gene Strand Plot can only + be generated using the gene strand information annotation file." informationBox(infoText) }) diff --git a/inst/src/tabs/dimension/svr_dimension.R b/inst/src/tabs/dimension/svr_dimension.R index 270d39f..1b28635 100644 --- a/inst/src/tabs/dimension/svr_dimension.R +++ b/inst/src/tabs/dimension/svr_dimension.R @@ -158,8 +158,8 @@ pc_gene_table <- reactive({ output[["variance_pca_info"]] <- renderUI({ infoText <- - "This plot shows the variances of the PCA (Principal Components Analysis) components. - A scree plot shows the 'eigenvalues' from the PCA and can be used to decide how many components + "This chart shows the variances of the PCA (Principal Components Analysis) components. + A scree plot shows the 'eigenvalues' from the PCA and can be used to determine how many components can be kept for the PCA analysis." informationBox(infoText) }) @@ -167,22 +167,22 @@ output[["variance_pca_info"]] <- renderUI({ output[["pca_info"]] <- renderUI({ infoText <- "The 2D PCA plot shows the samples based on the two components. A PCA plot shows important - information from a multivariate data table and shows the result as new variables (Principal Components)." + information from a multivariate data table and displays the result as new variables (main components)." informationBox(infoText) }) output[["norm_un_cluster_info"]] <- renderUI({ infoText <- "This MDS plot (multidimensional scaling plot) can be viewed as a 2D plot with - calculations of two dimensions. With the MDS plot distances between samples is - shown, based on similarities and differences." + calculations of two dimensions. With the MDS plotting distances between samples, samples + shown, based on similarities and differences." informationBox(infoText) }) output[["dim_tsne_info"]] <- renderUI({ infoText <- "t-distributed stochastic neighbor embedding (t-SNE) is a statistical method - for visualizing high-dimensional data by giving each datapoint a location in a - multi-dimensional map." + for visualizing high-dimensional data by giving each datapoint a location in a + multi-dimensional map." informationBox(infoText) }) diff --git a/inst/src/tabs/gprofiler/svr_gprofiler.R b/inst/src/tabs/gprofiler/svr_gprofiler.R index 3bded2a..7fb20df 100644 --- a/inst/src/tabs/gprofiler/svr_gprofiler.R +++ b/inst/src/tabs/gprofiler/svr_gprofiler.R @@ -212,42 +212,42 @@ output[["heat_table"]] <- DT::renderDataTable({ output[["gProfiler2_plot_info"]] <- renderUI({ infoText <- "The gProfiler 2 manhattan plot shows the terms colored by source (database). - Every term is represented by a circle. The size of a circle represents the term - size (number of genes). The y-axis show the adjusted p-value in a (negative) + Each term is represented by a circle. The size of a circle represents the term + size (number of genes). The y-axis shows the adjusted p-value in a (negative) log10 scale." informationBox(infoText) }) output[["enrich_barplot_info"]] <- renderUI({ infoText <- - "The bar plot shows a sorted list of the most enriched terms. The bar plot is sorted based on - p-value. The colors of the bars are also generated based on p-value. On the X-axis, - the amount of genes linked to the pathway is shown." + "The bar graph shows a sorted list of the most enriched terms. The bar chart is sorted based on + p-value. The colors of the bars are also generated based on p-value. on the X-axis, + the amount of genes linked to the pathway is displayed." informationBox(infoText) }) output[["enrich_DEbarplot_info"]] <- renderUI({ infoText <- - "The bar plot shows a sorted list of the most enriched terms. The bar plot is sorted based on - p-value. The plot shows the number of DE genes (up or down) regulated in a enrichment term." + "The bar graph shows a sorted list of the most enriched terms. The bar chart is sorted based on + p-value. The plot shows the number of DE genes (up or down) regulated in an enrichment term." informationBox(infoText) }) output[["enrich_cnet_info"]] <- renderUI({ infoText <- - "The concept network can show which genes are involved in the most significant terms. The - most enriched terms together with all corresponding genes are collected and shown in a - network plot. Some pathways may contain some matching genes. These genes will also be - connected. The color given to genes is based on the log fold change determined after the - expression analysis. In the end, this plot shows the connection of genes between the most - significant pathways." + "The concept network can show which genes are involved in the most significant terms. The + most enriched terms along with all associated genes are collected and displayed in a + network plot. Some pathways may contain some matching genes. These genes will also + connected. The color given to genes is based on the log-fold change determined after the + expression analysis. Ultimately, this plot shows the connection of genes between the most + important road." informationBox(infoText) }) output[["enrich_heat_info"]] <- renderUI({ infoText <- - "The heatmap visualizes pathways and the corresponding genes. The genes are sorted based on - Log2FC. The pathways are sorted on number of gene mathes between other pathways, listing pathways with - the most gene matches on the left. With this plot genes present in pathways can be compared on sight." + "The heatmap visualizes pathways and the associated genes. The genes are sorted based on: + Log2FC. The paths are sorted by the number of gene maths among other paths, with paths listed with + most gene matches on the left. This allows genes present in pathways to be compared on sight." informationBox(infoText) }) diff --git a/inst/src/tabs/heatmaps/svr_heatmaps.R b/inst/src/tabs/heatmaps/svr_heatmaps.R index 80cd397..8565035 100644 --- a/inst/src/tabs/heatmaps/svr_heatmaps.R +++ b/inst/src/tabs/heatmaps/svr_heatmaps.R @@ -90,16 +90,16 @@ output[["heatmap_dge_ngenes"]] <- renderUI({ output[["var_heat_info"]] <- renderUI({ infoText <- "This heatmap is based on the most variable genes. This is calculated - by getting the average Log2CPM difference of a gene between all samples. The + by getting the average Log2CPM difference of a gene between all samples. the genes are then sorted based on the calculated difference. The genes with the - biggest difference between Log2CPM values between all samples are listed as + greatest difference between Log2CPM values between all samples are reported as 'The most variable' and are shown in the heatmap." informationBox(infoText) }) output[["dge_heat_info"]] <- renderUI({ infoText <- - "This heatmap is based on the most differentially expressed genes. This + "This heatmap is based on the most differentially expressed genes. this is defined by sorting all genes based on the adjusted p-value. The genes with the lowest adjusted p-values are considered 'most expressed'." informationBox(infoText) diff --git a/inst/src/tabs/norm_data/svr_norm.R b/inst/src/tabs/norm_data/svr_norm.R index a6d6287..32e945a 100644 --- a/inst/src/tabs/norm_data/svr_norm.R +++ b/inst/src/tabs/norm_data/svr_norm.R @@ -72,25 +72,25 @@ output[["selected_norm_voom"]] <- DT::renderDataTable({ output[["norm_dist_line_info"]] <- renderUI({ infoText <- "The line plot shows the density of the log2CPM values per sample. The density shows the - distribution of count values per sample and is used to determine big differences between - samples." + distribution of counts per sample and is used to detect large differences between + samples." informationBox(infoText) }) output[["norm_dist_boxplot_info"]] <- renderUI({ infoText <- - "The box plot has a similar purpose, but the data can be viewed in a different - format. The distribution can be seen between the Log2CPM at the corresponding - samples." + "The box plot serves a similar purpose to the line plot, but the data can be viewed in a different way + format. The distribution can be seen between the Log2CPM at the corresponding + samples." informationBox(infoText) }) output[["norm_voom_plot_info"]] <- renderUI({ infoText <- "The voom plot provides a check on the filtering, which is performed at the beginning of the - analysis. The method used to calculate this is 'voom'. Voom is an acronym for - mean-variance modeling at the observational level. This means that the mean-variance in - the data is calculated and gives each observation a certain weight. Problems during the - filtering of low expressed genes will be visible in this plot." + analysis. The method to calculate this is 'voom'. Voom is an acronym for + mean variance modeling at the observational level. This means that the mean variance in + the data is calculated and gives each observation a certain weight. Problems during the + filtering of low expressed genes will be visible in this plot." informationBox(infoText) }) diff --git a/inst/src/tabs/raw_data/svr_raw.R b/inst/src/tabs/raw_data/svr_raw.R index eb52235..ad3c09a 100644 --- a/inst/src/tabs/raw_data/svr_raw.R +++ b/inst/src/tabs/raw_data/svr_raw.R @@ -118,14 +118,14 @@ output[["selected_raw_mds"]] <- DT::renderDataTable({ output[["dist_line_info"]] <- renderUI({ infoText <- "The line plot shows the density of the log2CPM values per sample. The density shows the - distribution of count values per sample and is used to determine big differences between + distribution of counts per sample and is used to detect large differences between samples." informationBox(infoText) }) output[["dist_boxplot_info"]] <- renderUI({ infoText <- - "The box plot has a similar purpose, but the data can be viewed in a different + "The box plot serves a similar purpose as the line plot, but the data can be viewed in a different way format. The distribution can be seen between the Log2CPM at the corresponding samples." informationBox(infoText) @@ -134,8 +134,8 @@ output[["dist_boxplot_info"]] <- renderUI({ output[["raw_voom_plot_info"]] <- renderUI({ infoText <- "The voom plot provides a check on the filtering, which is performed at the beginning of the - analysis. The method used to calculate this is 'voom'. Voom is an acronym for - mean-variance modeling at the observational level. This means that the mean-variance in + analysis. The method to calculate this is 'voom'. Voom is an acronym for + mean variance modeling at the observational level. This means that the mean variance in the data is calculated and gives each observation a certain weight. Problems during the filtering of low expressed genes will be visible in this plot." informationBox(infoText) @@ -144,7 +144,7 @@ output[["raw_voom_plot_info"]] <- renderUI({ output[["un_cluster_info"]] <- renderUI({ infoText <- "This MDS plot (multidimensional scaling plot) can be viewed as a 2D plot with - calculations of two dimensions. With the MDS plot distances between samples is + calculations of two dimensions. With the MDS plotting distances between samples, samples shown, based on similarities and differences." informationBox(infoText) }) From 21b7c19d121673afee61f8cd71d79554888f9f79 Mon Sep 17 00:00:00 2001 From: Tom Kuipers Date: Thu, 10 Jun 2021 15:43:43 +0200 Subject: [PATCH 12/31] update infobox --- CHANGELOG.md | 1 + inst/src/tabs/about/ui_about.R | 2 +- inst/src/tabs/alignment/svr_alignment.R | 17 ++++++------ inst/src/tabs/analysis/svr_analysis.R | 36 ++++++++++++------------- inst/src/tabs/bias/svr_bias.R | 18 ++++++------- inst/src/tabs/dimension/svr_dimension.R | 14 +++++----- inst/src/tabs/gprofiler/svr_gprofiler.R | 32 +++++++++++----------- inst/src/tabs/heatmaps/svr_heatmaps.R | 6 ++--- inst/src/tabs/norm_data/svr_norm.R | 18 ++++++------- inst/src/tabs/raw_data/svr_raw.R | 10 +++---- 10 files changed, 77 insertions(+), 77 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 4da93a2..147e888 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -6,6 +6,7 @@ Newest changes should be on top. --> version 1.4.4 --------------------------- ++ Small infobox update + Add tSNE plot to Dimension reduction + Move normalized MDS to Dimension reduction + Change PCA tab to Dimension reduction diff --git a/inst/src/tabs/about/ui_about.R b/inst/src/tabs/about/ui_about.R index a4ab3ec..ff1d196 100644 --- a/inst/src/tabs/about/ui_about.R +++ b/inst/src/tabs/about/ui_about.R @@ -6,7 +6,7 @@ tab_about <- tabItem( HTML('
'), h2("dgeAnalysis"), - h4("R shiny tool to perform differential gene expression"), + h4("R shiny tool to perform & visualize differential gene expression"), HTML('
'), br(), br(), diff --git a/inst/src/tabs/alignment/svr_alignment.R b/inst/src/tabs/alignment/svr_alignment.R index f4be44a..57673f8 100644 --- a/inst/src/tabs/alignment/svr_alignment.R +++ b/inst/src/tabs/alignment/svr_alignment.R @@ -67,20 +67,19 @@ output[["complex"]] <- renderPlotly({ output[["align_sum_info"]] <- renderUI({ infoText <- - "In the summary plots, a general overview can be seen of the count data. These plots show a - bar plot with the alignment results and a complexity plot. The alignment plot is a stacked bar - plot, where every 'row' stands for a sample and the stacked bars per row are the different - tags. These tags indicate the distribution of the read counts, given by a read counter (e.g. + "The summary plots provide a general overview of the counting data. These plots show a + bar chart showing the alignment results and a complexity plot. The alignment plot is a stacked bar + plot, where each 'row' represents a sample and the stacked bars per row are different + tags. These tags indicate the distribution of read counts given by a read counter (eg. HTSeq)." informationBox(infoText) }) output[["complex_info"]] <- renderUI({ infoText <- - "The complexity plots show how many reads cover a certain amount of genes. On the X-axis, - there is a ranking from zero to one thousand. This ranking stands for the number of genes. - On the Y-axis the proportion of the reads relative to the total amount of counts is shown. - Normally the expectation is a growing curve. Big deviations between samples of the number - of reads for a gene, will be visible." + "The complexity graphs show how many reads cover a given number of genes. On the X-axis, + there is a ranking from zero to a thousand. This arrangement represents the number of genes. + The Y-axis shows the proportion of reads in relation to the total number of gene counts. + Large variances between samples of the number or reads for a gene, will be visible." informationBox(infoText) }) diff --git a/inst/src/tabs/analysis/svr_analysis.R b/inst/src/tabs/analysis/svr_analysis.R index 1568b31..c7c8203 100644 --- a/inst/src/tabs/analysis/svr_analysis.R +++ b/inst/src/tabs/analysis/svr_analysis.R @@ -135,49 +135,49 @@ output[["p_val_plot"]] <- renderPlotly({ output[["de_ratio_info"]] <- renderUI({ infoText <- - "The DE ratio plot gives a quick overview of the amount of differentially expressed genes. - (down, not and up-regulated) The exact amount of genes are shown inside the plot. The dispersion - of the Y-axis is based on the percentages. This is calculated based on the total amount of genes + "The DE ratio plot provides a quick overview of the amount of differentially expressed genes. + (down, unregulated and up regulated) The exact number of genes is shown in the plot. the spread + of the Y-axis is based on the percentages. This is calculated based on the total number of genes after filtering." informationBox(infoText) }) output[["ma_plot_info"]] <- renderUI({ infoText <- - "The MA plot (Mean average), plots all genes after filtering. This plot creates a figure with - log-intensity ratios (M-values) and log-intensity averages (A-values). The MA plot can give a - view of all genes and their amount of expression in a comparison of two groups. On the - X-axis the Log2CPM values (A) are plotted against the Log2FC values on the Y-axis (M). In - this plot, the genes are colored based on the DE results (down, not and up-regulated). Also, + "The MA plot, plots all genes after filtering. This plot creates a figure with + log intensity ratios (M values) and log intensity means (A values). The MA plot can give a + overview of all genes and their amount of expression in a comparison of two groups. on the + X-axis plots the Log2CPM values (A) against the Log2FC values on the Y-axis (M). In + in this plot the genes are stained based on the DE results (down, unregulated and upregulated). so, the trend of expression is shown. This trend shows the average expression flow." informationBox(infoText) }) output[["volcano_plot_info"]] <- renderUI({ infoText <- - "The volcano plot also shows the most expressed genes and can give a good view of the + "The volcano plot also shows the most expressed genes and can give a good idea of the expression results. The X-axis contains the Log2FC (magnitude of change) and the Y-axis - shows -Log10 p-value (statistical significance). A great benefit of the volcano plot is that it - makes genes with a high fold-change, who are statistically significant, well visible. If genes - are down-regulated, they will be visible towards the left of the plot and up-regulated genes - more towards the right. The most statistically significant genes are placed towards the top." + shows -Log10 p-value (statistical significance). A major advantage of the volcano plot is that it + makes genes with a high fold-change (which are statistically significant) clearly visible. As genes + are down-regulated, they will be visible on the left side of the plot and up-regulated genes + more to the right. The most statistically significant genes are placed at the top." informationBox(infoText) }) output[["barcode_plot_info"]] <- renderUI({ infoText <- "The barcode plot shows the most expressed genes. The X-axis shows the Log2CPM - for every sample per gene. The Y-axis shows most expressed genes (from top to - bottom). Every bar stands for a sample. With this plot differences between samples for a + for each sample per gene. The Y-axis shows the most expressed genes (from top to + bottom). Each bar represents a sample. With this plot, differences between samples for a specific gene is visible." informationBox(infoText) }) output[["p_val_plot_info"]] <- renderUI({ infoText <- - "The p-value plot is used as a quality control plot. When all bars, for example, are all the - same height, then the analysis can be described as “noisy”. The ideal pattern would be that - bars around a p-value of zero are high. Next, the bars should get lower in a nice (steep) + "The p-value plot is used as a quality control plot. For example, if all bars are all + same height, the analysis can be described as “noisy”. The ideal pattern would be that + bars around a p-value of zero are high. Then the bars have to be in a nice (steep) curve and should be (almost) zero at the p-value of one." informationBox(infoText) }) diff --git a/inst/src/tabs/bias/svr_bias.R b/inst/src/tabs/bias/svr_bias.R index 2e1bf9c..1f9822d 100644 --- a/inst/src/tabs/bias/svr_bias.R +++ b/inst/src/tabs/bias/svr_bias.R @@ -101,28 +101,28 @@ output[["geneStrand_bias"]] <- renderPlotly({ output[["gc_bias_info"]] <- renderUI({ infoText <- - "The GC bias plots are generated based on the average log fold change on the Y-axis. The bias plots + "The GC bias plots are generated based on the mean change of the logFC on the Y-axis. The bias plots can only be used if an annotation is used to perform the analysis. If annotation values are - present, a column can be selected with the use of a drop-down button. The columns should - contain the text: 'GC', otherwise they will not be recognized as a column suitable + is present, a column can be selected using a drop-down button. The columns must + contain the text: 'GC', otherwise they will not be recognized as a suitable column for GC bias calculations." informationBox(infoText) }) output[["len_bias_info"]] <- renderUI({ infoText <- - "The feature-length bias plots are generated based on the average log fold change on the Y-axis. The bias plots + "The feature-length bias plots are generated from the mean change of the logFC on the Y-axis. The bias plots can only be used if an annotation is used to perform the analysis. If annotation values are - present, a column can be selected with the use of a drop-down button. The columns should - contain the text: 'length', otherwise they will not be recognized as a column suitable + is present, a column can be selected using a drop-down button. The columns must + contain the text: 'length', otherwise they will not be recognized as a suitable column for length bias calculations." informationBox(infoText) }) output[["geneStrand_info"]] <- renderUI({ infoText <- - "The gene strand bias plots are generated based on strand info (+ or -) and the number of - genes (DE or not). It shows the distribution of genes between strands. The gene strand plot can only - be generated when using an annotation file containing strand information about genes." + "The gene strand bias plots are generated based on strand info (+ or -) and number + genes (DE or not). It shows the distribution of genes between strands. The Gene Strand Plot can only + be generated using the gene strand information annotation file." informationBox(infoText) }) diff --git a/inst/src/tabs/dimension/svr_dimension.R b/inst/src/tabs/dimension/svr_dimension.R index 270d39f..1b28635 100644 --- a/inst/src/tabs/dimension/svr_dimension.R +++ b/inst/src/tabs/dimension/svr_dimension.R @@ -158,8 +158,8 @@ pc_gene_table <- reactive({ output[["variance_pca_info"]] <- renderUI({ infoText <- - "This plot shows the variances of the PCA (Principal Components Analysis) components. - A scree plot shows the 'eigenvalues' from the PCA and can be used to decide how many components + "This chart shows the variances of the PCA (Principal Components Analysis) components. + A scree plot shows the 'eigenvalues' from the PCA and can be used to determine how many components can be kept for the PCA analysis." informationBox(infoText) }) @@ -167,22 +167,22 @@ output[["variance_pca_info"]] <- renderUI({ output[["pca_info"]] <- renderUI({ infoText <- "The 2D PCA plot shows the samples based on the two components. A PCA plot shows important - information from a multivariate data table and shows the result as new variables (Principal Components)." + information from a multivariate data table and displays the result as new variables (main components)." informationBox(infoText) }) output[["norm_un_cluster_info"]] <- renderUI({ infoText <- "This MDS plot (multidimensional scaling plot) can be viewed as a 2D plot with - calculations of two dimensions. With the MDS plot distances between samples is - shown, based on similarities and differences." + calculations of two dimensions. With the MDS plotting distances between samples, samples + shown, based on similarities and differences." informationBox(infoText) }) output[["dim_tsne_info"]] <- renderUI({ infoText <- "t-distributed stochastic neighbor embedding (t-SNE) is a statistical method - for visualizing high-dimensional data by giving each datapoint a location in a - multi-dimensional map." + for visualizing high-dimensional data by giving each datapoint a location in a + multi-dimensional map." informationBox(infoText) }) diff --git a/inst/src/tabs/gprofiler/svr_gprofiler.R b/inst/src/tabs/gprofiler/svr_gprofiler.R index 3bded2a..7fb20df 100644 --- a/inst/src/tabs/gprofiler/svr_gprofiler.R +++ b/inst/src/tabs/gprofiler/svr_gprofiler.R @@ -212,42 +212,42 @@ output[["heat_table"]] <- DT::renderDataTable({ output[["gProfiler2_plot_info"]] <- renderUI({ infoText <- "The gProfiler 2 manhattan plot shows the terms colored by source (database). - Every term is represented by a circle. The size of a circle represents the term - size (number of genes). The y-axis show the adjusted p-value in a (negative) + Each term is represented by a circle. The size of a circle represents the term + size (number of genes). The y-axis shows the adjusted p-value in a (negative) log10 scale." informationBox(infoText) }) output[["enrich_barplot_info"]] <- renderUI({ infoText <- - "The bar plot shows a sorted list of the most enriched terms. The bar plot is sorted based on - p-value. The colors of the bars are also generated based on p-value. On the X-axis, - the amount of genes linked to the pathway is shown." + "The bar graph shows a sorted list of the most enriched terms. The bar chart is sorted based on + p-value. The colors of the bars are also generated based on p-value. on the X-axis, + the amount of genes linked to the pathway is displayed." informationBox(infoText) }) output[["enrich_DEbarplot_info"]] <- renderUI({ infoText <- - "The bar plot shows a sorted list of the most enriched terms. The bar plot is sorted based on - p-value. The plot shows the number of DE genes (up or down) regulated in a enrichment term." + "The bar graph shows a sorted list of the most enriched terms. The bar chart is sorted based on + p-value. The plot shows the number of DE genes (up or down) regulated in an enrichment term." informationBox(infoText) }) output[["enrich_cnet_info"]] <- renderUI({ infoText <- - "The concept network can show which genes are involved in the most significant terms. The - most enriched terms together with all corresponding genes are collected and shown in a - network plot. Some pathways may contain some matching genes. These genes will also be - connected. The color given to genes is based on the log fold change determined after the - expression analysis. In the end, this plot shows the connection of genes between the most - significant pathways." + "The concept network can show which genes are involved in the most significant terms. The + most enriched terms along with all associated genes are collected and displayed in a + network plot. Some pathways may contain some matching genes. These genes will also + connected. The color given to genes is based on the log-fold change determined after the + expression analysis. Ultimately, this plot shows the connection of genes between the most + important road." informationBox(infoText) }) output[["enrich_heat_info"]] <- renderUI({ infoText <- - "The heatmap visualizes pathways and the corresponding genes. The genes are sorted based on - Log2FC. The pathways are sorted on number of gene mathes between other pathways, listing pathways with - the most gene matches on the left. With this plot genes present in pathways can be compared on sight." + "The heatmap visualizes pathways and the associated genes. The genes are sorted based on: + Log2FC. The paths are sorted by the number of gene maths among other paths, with paths listed with + most gene matches on the left. This allows genes present in pathways to be compared on sight." informationBox(infoText) }) diff --git a/inst/src/tabs/heatmaps/svr_heatmaps.R b/inst/src/tabs/heatmaps/svr_heatmaps.R index 80cd397..8565035 100644 --- a/inst/src/tabs/heatmaps/svr_heatmaps.R +++ b/inst/src/tabs/heatmaps/svr_heatmaps.R @@ -90,16 +90,16 @@ output[["heatmap_dge_ngenes"]] <- renderUI({ output[["var_heat_info"]] <- renderUI({ infoText <- "This heatmap is based on the most variable genes. This is calculated - by getting the average Log2CPM difference of a gene between all samples. The + by getting the average Log2CPM difference of a gene between all samples. the genes are then sorted based on the calculated difference. The genes with the - biggest difference between Log2CPM values between all samples are listed as + greatest difference between Log2CPM values between all samples are reported as 'The most variable' and are shown in the heatmap." informationBox(infoText) }) output[["dge_heat_info"]] <- renderUI({ infoText <- - "This heatmap is based on the most differentially expressed genes. This + "This heatmap is based on the most differentially expressed genes. this is defined by sorting all genes based on the adjusted p-value. The genes with the lowest adjusted p-values are considered 'most expressed'." informationBox(infoText) diff --git a/inst/src/tabs/norm_data/svr_norm.R b/inst/src/tabs/norm_data/svr_norm.R index a6d6287..32e945a 100644 --- a/inst/src/tabs/norm_data/svr_norm.R +++ b/inst/src/tabs/norm_data/svr_norm.R @@ -72,25 +72,25 @@ output[["selected_norm_voom"]] <- DT::renderDataTable({ output[["norm_dist_line_info"]] <- renderUI({ infoText <- "The line plot shows the density of the log2CPM values per sample. The density shows the - distribution of count values per sample and is used to determine big differences between - samples." + distribution of counts per sample and is used to detect large differences between + samples." informationBox(infoText) }) output[["norm_dist_boxplot_info"]] <- renderUI({ infoText <- - "The box plot has a similar purpose, but the data can be viewed in a different - format. The distribution can be seen between the Log2CPM at the corresponding - samples." + "The box plot serves a similar purpose to the line plot, but the data can be viewed in a different way + format. The distribution can be seen between the Log2CPM at the corresponding + samples." informationBox(infoText) }) output[["norm_voom_plot_info"]] <- renderUI({ infoText <- "The voom plot provides a check on the filtering, which is performed at the beginning of the - analysis. The method used to calculate this is 'voom'. Voom is an acronym for - mean-variance modeling at the observational level. This means that the mean-variance in - the data is calculated and gives each observation a certain weight. Problems during the - filtering of low expressed genes will be visible in this plot." + analysis. The method to calculate this is 'voom'. Voom is an acronym for + mean variance modeling at the observational level. This means that the mean variance in + the data is calculated and gives each observation a certain weight. Problems during the + filtering of low expressed genes will be visible in this plot." informationBox(infoText) }) diff --git a/inst/src/tabs/raw_data/svr_raw.R b/inst/src/tabs/raw_data/svr_raw.R index eb52235..ad3c09a 100644 --- a/inst/src/tabs/raw_data/svr_raw.R +++ b/inst/src/tabs/raw_data/svr_raw.R @@ -118,14 +118,14 @@ output[["selected_raw_mds"]] <- DT::renderDataTable({ output[["dist_line_info"]] <- renderUI({ infoText <- "The line plot shows the density of the log2CPM values per sample. The density shows the - distribution of count values per sample and is used to determine big differences between + distribution of counts per sample and is used to detect large differences between samples." informationBox(infoText) }) output[["dist_boxplot_info"]] <- renderUI({ infoText <- - "The box plot has a similar purpose, but the data can be viewed in a different + "The box plot serves a similar purpose as the line plot, but the data can be viewed in a different way format. The distribution can be seen between the Log2CPM at the corresponding samples." informationBox(infoText) @@ -134,8 +134,8 @@ output[["dist_boxplot_info"]] <- renderUI({ output[["raw_voom_plot_info"]] <- renderUI({ infoText <- "The voom plot provides a check on the filtering, which is performed at the beginning of the - analysis. The method used to calculate this is 'voom'. Voom is an acronym for - mean-variance modeling at the observational level. This means that the mean-variance in + analysis. The method to calculate this is 'voom'. Voom is an acronym for + mean variance modeling at the observational level. This means that the mean variance in the data is calculated and gives each observation a certain weight. Problems during the filtering of low expressed genes will be visible in this plot." informationBox(infoText) @@ -144,7 +144,7 @@ output[["raw_voom_plot_info"]] <- renderUI({ output[["un_cluster_info"]] <- renderUI({ infoText <- "This MDS plot (multidimensional scaling plot) can be viewed as a 2D plot with - calculations of two dimensions. With the MDS plot distances between samples is + calculations of two dimensions. With the MDS plotting distances between samples, samples shown, based on similarities and differences." informationBox(infoText) }) From a8d638f8337933b7c138cb01c8226723ec69e423 Mon Sep 17 00:00:00 2001 From: Tom Kuipers Date: Thu, 10 Jun 2021 15:58:59 +0200 Subject: [PATCH 13/31] change file name and remove quotes --- inst/src/tabs/export/svr_export.R | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/inst/src/tabs/export/svr_export.R b/inst/src/tabs/export/svr_export.R index 1a8d85e..7672151 100644 --- a/inst/src/tabs/export/svr_export.R +++ b/inst/src/tabs/export/svr_export.R @@ -46,7 +46,7 @@ datasetInput <- reactive({ ## create filename and save data as CSV output$downloadCSV <- downloadHandler( filename = function() { - paste0("shiny_analysis_", gsub(" ", "_", tolower(input$dataset_select)), ".csv") + paste0("dgeAnalysis_", gsub(" ", "_", tolower(input$dataset_select)), ".csv") }, content = function(file) { write.table( @@ -54,7 +54,8 @@ output$downloadCSV <- downloadHandler( file, row.names = TRUE, col.names = NA, - sep = "," + sep = ",", + quote = FALSE ) } ) @@ -62,7 +63,7 @@ output$downloadCSV <- downloadHandler( ## create filename and save data as TSV output$downloadTSV <- downloadHandler( filename = function() { - paste0("shiny_analysis_", gsub(" ", "_", tolower(input$dataset_select)), ".tsv") + paste0("dgeAnalysis_", gsub(" ", "_", tolower(input$dataset_select)), ".tsv") }, content = function(file) { write.table( @@ -70,7 +71,8 @@ output$downloadTSV <- downloadHandler( file, row.names = TRUE, col.names = NA, - sep = "\t" + sep = "\t", + quote = FALSE ) } ) From 1e2f7a3f3ecfb2847b9c7b9b31877321e5438541 Mon Sep 17 00:00:00 2001 From: Tom Kuipers Date: Thu, 10 Jun 2021 15:58:59 +0200 Subject: [PATCH 14/31] change file name and remove quotes --- CHANGELOG.md | 1 + inst/src/tabs/export/svr_export.R | 10 ++++++---- 2 files changed, 7 insertions(+), 4 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 147e888..f02fceb 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -6,6 +6,7 @@ Newest changes should be on top. --> version 1.4.4 --------------------------- ++ Change export file name and remove quotes from exported dataframe + Small infobox update + Add tSNE plot to Dimension reduction + Move normalized MDS to Dimension reduction diff --git a/inst/src/tabs/export/svr_export.R b/inst/src/tabs/export/svr_export.R index 1a8d85e..7672151 100644 --- a/inst/src/tabs/export/svr_export.R +++ b/inst/src/tabs/export/svr_export.R @@ -46,7 +46,7 @@ datasetInput <- reactive({ ## create filename and save data as CSV output$downloadCSV <- downloadHandler( filename = function() { - paste0("shiny_analysis_", gsub(" ", "_", tolower(input$dataset_select)), ".csv") + paste0("dgeAnalysis_", gsub(" ", "_", tolower(input$dataset_select)), ".csv") }, content = function(file) { write.table( @@ -54,7 +54,8 @@ output$downloadCSV <- downloadHandler( file, row.names = TRUE, col.names = NA, - sep = "," + sep = ",", + quote = FALSE ) } ) @@ -62,7 +63,7 @@ output$downloadCSV <- downloadHandler( ## create filename and save data as TSV output$downloadTSV <- downloadHandler( filename = function() { - paste0("shiny_analysis_", gsub(" ", "_", tolower(input$dataset_select)), ".tsv") + paste0("dgeAnalysis_", gsub(" ", "_", tolower(input$dataset_select)), ".tsv") }, content = function(file) { write.table( @@ -70,7 +71,8 @@ output$downloadTSV <- downloadHandler( file, row.names = TRUE, col.names = NA, - sep = "\t" + sep = "\t", + quote = FALSE ) } ) From 469d0a4740439e2b6c26ce9f7451dcd2963773b3 Mon Sep 17 00:00:00 2001 From: Tom Kuipers Date: Thu, 10 Jun 2021 17:56:19 +0200 Subject: [PATCH 15/31] Change coloring of countdist and complexity --- R/de_plots.R | 214 ++++++++++++------------ inst/src/tabs/alignment/svr_alignment.R | 35 ++-- inst/src/tabs/norm_data/svr_norm.R | 16 +- inst/src/tabs/norm_data/ui_norm.R | 1 + inst/src/tabs/raw_data/svr_raw.R | 16 +- inst/src/tabs/raw_data/ui_raw.R | 1 + 6 files changed, 155 insertions(+), 128 deletions(-) diff --git a/R/de_plots.R b/R/de_plots.R index c22c5b2..af09a9a 100644 --- a/R/de_plots.R +++ b/R/de_plots.R @@ -118,7 +118,7 @@ alignmentSummaryPlot <- function(se, sort_value = "None", perc = T) { #' A dot line plot is created based on the number of reads at rank. #' #' @param se SummerizedExperiment object, containing samples and counts -#' @param group_color String, Sort samples based on a group +#' @param color String, Sort samples based on a group #' @param perc Boolean, Data in percentages #' @param rank Integer, The number of genes/rank (min=10) #' @@ -126,117 +126,90 @@ alignmentSummaryPlot <- function(se, sort_value = "None", perc = T) { #' #' @export -complexityPlot <- function(se, group_color = "None", perc = T, rank = 1000) { - compData <- complexityData(se, rank) - - p <- plot_ly(type = 'scattergl', - mode = "lines+markers") %>% - plotly::layout( - title = "Gene complexity %", - xaxis = list(title = 'Rank', type = "log"), - yaxis = if (perc) { - list(tickformat = "%", title = 'Cumulative fraction of total reads till rank') - } - else { - list(title = 'Cumulative reads till rank') - }, - legend = list(tracegroupgap = 0) - ) %>% - config( - toImageButtonOptions = list( - format = "png", - filename = "countdistline", - width = 1500, - height = 1000 - ) +complexityPlot <- function(se, color = "None", perc = T, rank = 1000) { + if (color == "None") {color <- NA} + + compData <- complexityData(se, rank) + compData <- merge( + x = compData, + y = as.data.frame(colData(se)), + by.x = "sample", + by.y = "row.names", + all.x = TRUE + ) + + p <- plot_ly(type = 'scattergl', + mode = "lines+markers") %>% + plotly::layout( + title = "Gene complexity %", + xaxis = list(title = 'Rank', type = "log"), + yaxis = if (perc) { + list(tickformat = "%", title = 'Cumulative fraction of total reads till rank') + } + else { + list(title = 'Cumulative reads till rank') + }, + legend = list(tracegroupgap = 0) + ) %>% + config( + toImageButtonOptions = list( + format = "png", + filename = "countdistline", + width = 1500, + height = 1000 ) - - legend_duplic <- c() - for (var in colnames(se)) { - temp <- compData[compData$sample == var,] - if (group_color != "None") { - p <- add_trace( - p, - x = temp$rank, - y = if (perc) { - temp$fraction - } else { - temp$value - }, - showlegend = if (!se[[group_color]][colnames(se) == var] %in% legend_duplic) { - TRUE - } else{ - FALSE - }, - legendgroup = se[[group_color]][colnames(se) == var], - color = rep(se[[group_color]][colnames(se) == var], rank), - text = if (perc) { - paste( - temp$sample, - "\n", - temp$rank, - "Genes\n", - round(temp$fraction * 100, 2), - '% Reads\n' - ) - } else { - paste( - temp$sample, - "\n", - temp$rank, - "Genes\n", - formatC( - temp$value, - format = "f", - big.mark = ".", - digits = 0 - ), - 'Reads\n' - ) - }, - hoverinfo = 'text' + ) + + header_col <- c() + group_color <- NA + for (var in colnames(se)) { + temp <- compData[compData$sample == var, ] + if (!is.na(color)) { + group_color <- temp[[color]][1] + } + p <- add_trace( + p, + x = temp$rank, + y = if (perc) { + temp$fraction + } else { + temp$value + }, + showlegend = if (!is.na(color) & group_color %in% header_col) {FALSE} else{TRUE}, + legendgroup = if (is.na(color)) {var} else {group_color}, + color = if (is.na(color)) {var} else {group_color}, + text = if (perc) { + paste( + temp$sample, + "\n", + temp$rank, + "Genes\n", + round(temp$fraction * 100, 2), + '% Reads\n' ) - legend_duplic <- c(legend_duplic, se[[group_color]][colnames(se) == var]) } else { - p <- add_trace( - p, - x = temp$rank, - y = if (perc) { - temp$fraction - } else { - temp$value - }, - color = var, - text = if (perc) { - paste( - temp$sample, - "\n", - temp$rank, - "Genes\n", - round(temp$fraction * 100, 2), - '% Reads\n' - ) - } else { - paste( - temp$sample, - "\n", - temp$rank, - "Genes\n", - formatC( - temp$value, - format = "f", - big.mark = ".", - digits = 0 - ), - 'Reads\n' - ) - }, - hoverinfo = 'text' + paste( + temp$sample, + "\n", + temp$rank, + "Genes\n", + formatC( + temp$value, + format = "f", + big.mark = ".", + digits = 0 + ), + 'Reads\n' ) - } + }, + hoverinfo = 'text' + ) + if (!is.na(color)) { + header_col <- c(header_col, as.character(group_color)) } - p } + p +} ## -------------------------------------------------------------------------- @@ -248,13 +221,23 @@ complexityPlot <- function(se, group_color = "None", perc = T, rank = 1000) { #' A dot line plot is created based on the number of reads at rank. #' #' @param dge DGE list object, containing samples and counts +#' @param color String, Sort samples based on a group #' #' @return p, (Plotly object) plot #' #' @export -countDistributionLinePlot <- function(dge) { +countDistributionLinePlot <- function(dge, color = "None") { + if (color == "None") {color <- NA} + stackCounts <- data.frame(stackDge(dge)) + stackCounts <- merge( + x = stackCounts, + y = dge$samples, + by.x = "sample", + by.y = "row.names", + all.x = TRUE + ) p <- plot_ly(type = 'scattergl', mode = 'lines', @@ -262,7 +245,8 @@ countDistributionLinePlot <- function(dge) { plotly::layout( title = "Gene count distribution", xaxis = list(title = 'Log2CPM'), - yaxis = list(title = 'Density') + yaxis = list(title = 'Density'), + legend = list(tracegroupgap = 0) ) %>% config( toImageButtonOptions = list( @@ -272,19 +256,31 @@ countDistributionLinePlot <- function(dge) { height = 1000 ) ) + + header_col <- c() + group_color <- NA for (var in unique(stackCounts$sample)) { temp <- stackCounts[stackCounts$sample == var,] density <- density(temp$logCPM) + if (!is.na(color)) { + group_color <- temp[[color]][1] + } p <- add_trace( p, x = density$x, y = density$y, - name = var, + name = if (is.na(color)) {var} else {group_color}, text = var, + legendgroup = if (is.na(color)) {NULL} else {group_color}, + showlegend = if (!is.na(color) & group_color %in% header_col) {FALSE} else{TRUE}, + color = if (is.na(color)) {NULL} else {group_color}, hoverinfo = 'text', fill = 'tozeroy', alpha = 0.05 ) + if (!is.na(color)) { + header_col <- c(header_col, as.character(group_color)) + } } p } diff --git a/inst/src/tabs/alignment/svr_alignment.R b/inst/src/tabs/alignment/svr_alignment.R index 57673f8..d98c929 100644 --- a/inst/src/tabs/alignment/svr_alignment.R +++ b/inst/src/tabs/alignment/svr_alignment.R @@ -24,9 +24,9 @@ output[["align_sum"]] <- renderPlotly({ tryCatch({ checkReload() if (input$setSummary == "actual") { - perc = F + perc = FALSE } else { - perc = T + perc = TRUE } alignmentSummaryPlot(get_se(), input$group_sum, perc) }, error = function(err) { @@ -34,30 +34,31 @@ output[["align_sum"]] <- renderPlotly({ }) }) -## Select a group to color complexity plot -output[["group_color"]] <- renderUI({ +## Create complexity plot +output[["complex"]] <- renderPlotly({ tryCatch({ checkReload() - selectInput( - inputId = "group_color", - label = "Group by:", - choices = c("None" = "None", colnames(data_samples())) - ) + if (input$setComplexity == "actual") { + perc = FALSE + } else { + perc = TRUE + } + complexityPlot(get_se(), input$group_color, perc, input$comp_rank) }, error = function(err) { + print(err) return(NULL) }) }) -## Create complexity plot -output[["complex"]] <- renderPlotly({ +## Select a group to color complexity plot +output[["group_color"]] <- renderUI({ tryCatch({ checkReload() - if (input$setComplexity == "actual") { - perc = F - } else { - perc = T - } - complexityPlot(get_se(), input$group_color, perc, input$comp_rank) + selectInput( + inputId = "group_color", + label = "Group by:", + choices = c("None" = "None", colnames(data_samples())) + ) }, error = function(err) { return(NULL) }) diff --git a/inst/src/tabs/norm_data/svr_norm.R b/inst/src/tabs/norm_data/svr_norm.R index 32e945a..b84d370 100644 --- a/inst/src/tabs/norm_data/svr_norm.R +++ b/inst/src/tabs/norm_data/svr_norm.R @@ -16,7 +16,21 @@ output[["normalized_counts"]] <- DT::renderDataTable({ output[["norm_dist_line"]] <- renderPlotly({ tryCatch({ checkReload() - countDistributionLinePlot(inUse_normDge) + countDistributionLinePlot(inUse_normDge, input$norm_line_color) + }, error = function(err) { + return(NULL) + }) +}) + +## Select a group to color line plot +output[["norm_line_color"]] <- renderUI({ + tryCatch({ + checkReload() + selectInput( + inputId = "norm_line_color", + label = "Group by:", + choices = c("None" = "None", colnames(data_samples())) + ) }, error = function(err) { return(NULL) }) diff --git a/inst/src/tabs/norm_data/ui_norm.R b/inst/src/tabs/norm_data/ui_norm.R index 89cd5e6..4590292 100644 --- a/inst/src/tabs/norm_data/ui_norm.R +++ b/inst/src/tabs/norm_data/ui_norm.R @@ -22,6 +22,7 @@ tab_norm_data <- tabItem( position = "right", sidebarPanel( width = 3, + uiOutput("norm_line_color"), uiOutput("norm_dist_line_info"), span(icon("copyright"), "LUMC - SASC", style = "color: #e3e3e3;") ), diff --git a/inst/src/tabs/raw_data/svr_raw.R b/inst/src/tabs/raw_data/svr_raw.R index ad3c09a..df26023 100644 --- a/inst/src/tabs/raw_data/svr_raw.R +++ b/inst/src/tabs/raw_data/svr_raw.R @@ -3,7 +3,21 @@ output[["dist_line"]] <- renderPlotly({ tryCatch({ checkReload() - countDistributionLinePlot(get_raw_dge()) + countDistributionLinePlot(get_raw_dge(), input$raw_line_color) + }, error = function(err) { + return(NULL) + }) +}) + +## Select a group to color line plot +output[["raw_line_color"]] <- renderUI({ + tryCatch({ + checkReload() + selectInput( + inputId = "raw_line_color", + label = "Group by:", + choices = c("None" = "None", colnames(data_samples())) + ) }, error = function(err) { return(NULL) }) diff --git a/inst/src/tabs/raw_data/ui_raw.R b/inst/src/tabs/raw_data/ui_raw.R index 2b71e6e..9f231ed 100644 --- a/inst/src/tabs/raw_data/ui_raw.R +++ b/inst/src/tabs/raw_data/ui_raw.R @@ -12,6 +12,7 @@ tab_raw_data <- tabItem( position = "right", sidebarPanel( width = 3, + uiOutput("raw_line_color"), uiOutput("dist_line_info"), span(icon("copyright"), "LUMC - SASC", style = "color: #e3e3e3;") ), From 0582b0ed3d783edd4461dafad8743eaa0d69d5e3 Mon Sep 17 00:00:00 2001 From: Tom Kuipers Date: Thu, 10 Jun 2021 17:56:30 +0200 Subject: [PATCH 16/31] update man --- man/complexityPlot.Rd | 4 ++-- man/countDistributionLinePlot.Rd | 4 +++- 2 files changed, 5 insertions(+), 3 deletions(-) diff --git a/man/complexityPlot.Rd b/man/complexityPlot.Rd index 7be329a..577dda3 100644 --- a/man/complexityPlot.Rd +++ b/man/complexityPlot.Rd @@ -6,12 +6,12 @@ If percentages == TRUE percentages are calculated. A dot line plot is created based on the number of reads at rank.} \usage{ -complexityPlot(se, group_color = "None", perc = T, rank = 1000) +complexityPlot(se, color = "None", perc = T, rank = 1000) } \arguments{ \item{se}{SummerizedExperiment object, containing samples and counts} -\item{group_color}{String, Sort samples based on a group} +\item{color}{String, Sort samples based on a group} \item{perc}{Boolean, Data in percentages} diff --git a/man/countDistributionLinePlot.Rd b/man/countDistributionLinePlot.Rd index bd2f660..b63797d 100644 --- a/man/countDistributionLinePlot.Rd +++ b/man/countDistributionLinePlot.Rd @@ -6,10 +6,12 @@ A line plot is created based on Log2CPM and the density of the counts. A dot line plot is created based on the number of reads at rank.} \usage{ -countDistributionLinePlot(dge) +countDistributionLinePlot(dge, color = "None") } \arguments{ \item{dge}{DGE list object, containing samples and counts} + +\item{color}{String, Sort samples based on a group} } \value{ p, (Plotly object) plot From c3cfd4f2d4adc567fa57b9ba16d9ac02609d7d8f Mon Sep 17 00:00:00 2001 From: Tom Kuipers Date: Fri, 11 Jun 2021 11:21:18 +0200 Subject: [PATCH 17/31] Remove file --- R/wcgna.R | 437 ------------------------------------------------------ 1 file changed, 437 deletions(-) delete mode 100644 R/wcgna.R diff --git a/R/wcgna.R b/R/wcgna.R deleted file mode 100644 index 5e843ff..0000000 --- a/R/wcgna.R +++ /dev/null @@ -1,437 +0,0 @@ - -## ----- TREE PLOTS ----- - - -#' Get heigth information from tree object. -#' Get labels/names from tree ends. -#' Calculate start and end positions of tree branches -#' Save line coordinates in dataframe -#' -#' @param tree Hclust object, tree object -#' -#' @return new, (Dataframe) dataframe with all dendrogram line coordinates -#' -#' @export - -get_dendrogram_data <- function(tree, - labels = NULL, - horiz = FALSE, - reverseDirection = FALSE, - hang = 0.1, - xlab = "", - ylab = "", - axes = TRUE, - cex.labels = 1, - ..., - adjustRange = FALSE) { - hang.gr = hang - if (hang < 0) - hang.gr = 0.1 - n = length(tree$order) - heights = tree$height - range = range(heights) - hang.scaled = hang.gr * (max(heights) - min(heights)) - range[1] = range[1] - hang.scaled - - indexLim = c(0.5, n + 0.5) - if (adjustRange) - { - ctr = mean(indexLim) - indexLim = ctr + (indexLim - ctr) / 1.08 - } - - nMerge = n - 1 - if (is.null(labels)) - labels = tree$labels - if (is.null(labels)) - labels = rep("", n) - if (is.na(labels[1])) - labels = rep("", n) - if (is.logical(labels) && labels[1] == "FALSE") - labels = rep("", n) - - singleton.x = rep(NA, n) - singleton.x[tree$order] = c(1:n) - cluster.x = rep(NA, n) - - new <- data.frame(matrix( - ncol = 5, - nrow = 0, - dimnames = list(NULL, c("label", "x", "y", "xend", "yend")) - )) - for (m in 1:nMerge) { - o1 = tree$merge[m, 1] - o2 = tree$merge[m, 2] - h = heights[m] - hh = if (hang > 0) - h - hang.scaled - else - range[1] - h1 = if (o1 < 0) - hh - else - heights[o1] - h2 = if (o2 < 0) - hh - else - heights[o2] - - x1 = if (o1 < 0) - singleton.x[-o1] - else - cluster.x[o1] - x2 = if (o2 < 0) - singleton.x[-o2] - else - cluster.x[o2] - - cluster.x[m] = mean(c(x1, x2)) - - label1 <- labels[-o1] - label2 <- labels[-o2] - if (length(label1) > 1) { - label1 <- "" - } - if (length(label2) > 1) { - label2 <- "" - } - - if (!is.na(x1)) { - new[nrow(new) + 1, ] <- c(label1, x1, h1, x1, h) - if (!is.na(x2)) { - new[nrow(new) + 1, ] <- c("", x1, h, x2, h) - new[nrow(new) + 1, ] <- c(label2, x2, h2, x2, h) - } - } - } - new[2:5] <- sapply(new[2:5], as.double) - new <- new[order(new$x), ] - return(new) -} - - -#' Get dendrogram data dendro_data(). -#' Plot a dendrogram. -#' -#' @param d Hclust object, tree object -#' @param color String, color samples -#' @param color_list Vector, Color genes -#' -#' @return p, (Plotly object) plot -#' -#' @export - -plotly_dendrogram <- function(d, color, color_list) { - dendro_data <- get_dendrogram_data(d) - - p <- plot_ly( - data = dendro_data, - x = ~ x, - y = ~ y, - color = I("black"), - hoverinfo = "none" - ) %>% - add_segments(xend = ~ xend, - yend = ~ yend, - showlegend = FALSE) %>% - add_markers( - data = dendro_data[dendro_data$label != "", ], - x = ~ x, - y = ~ y, - color = color, - marker = list(size = 10, - color = color_list), - text = ~ label, - hoverinfo = 'text' - ) %>% - plotly::layout( - dragmode = "zoom", - title = "Dendrogram with normalized log2CPM values", - xaxis = list( - title = "", - showticklabels = FALSE, - zeroline = FALSE - ), - yaxis = list(title = "Height") - ) %>% - config(toImageButtonOptions = list( - format = "png", - filename = "dendro", - width = 1500, - height = 1000 - )) - p -} - -## -------------------------------------------------------------------------- - -## ----- HEATMAP PLOTS ----- - - -#' Create heatmap with trait data. -#' Show sample, trait value (column) and the exact trait value -#' -#' @param trait Dataframe, dataframe with all trait values -#' -#' @return p, (Plotly object) plot -#' -#' @export - -plot_trait_heat <- function(trait) { - heatmap_list <- list() - for (column in colnames(trait)) { - p <- plot_ly( - x = rownames(trait), - y = column, - z = t(trait[[column]]), - type = "heatmap", - showscale = FALSE, - hoverinfo = 'text', - text = matrix( - paste( - "Sample:", - rownames(trait), - "
Column:", - rep(c(column), nrow(trait)), - "
Value:", - t(trait[[column]]) - ), - ncol = nrow(trait) - ) - ) - heatmap_list[[column]] <- p - } - subplot( - heatmap_list, - nrows = ncol(trait), - shareX = TRUE, - margin = 0.00025 - ) %>% - config( - toImageButtonOptions = list( - format = "png", - filename = "trait_heat", - width = 1500, - height = 1000 - ) - ) -} - - -#' Create heatmap from gene clusters. -#' Show genes vs genes colored by adjacency -#' -#' @param data_TOM WGCNA object, Topology Overlap Matrix -#' -#' @return p, (Plotly object) plot -#' -#' @export - -plotly_dendro_heat <- function(data_TOM) { - p <- plot_ly( - x = rownames(data_TOM), - y = colnames(data_TOM), - z = data_TOM, - colorbar = list(title = "Adjacency", len = 1), - type = "heatmap" - ) %>% - plotly::layout( - title = "Network heatmap", - xaxis = list(title = ''), - yaxis = list(title = '') - ) - p -} - - -#' Create heatmap with trait data compared to module relations. -#' Show module name, correlation between trait and module, and the trait p-value -#' -#' @param moduleTraitCor Dataframe, dataframe with all trait correlation values -#' @param moduleTraitPvalue Dataframe, dataframe with all trait p-values -#' -#' @return p, (Plotly object) plot -#' -#' @export - -plot_module_trait_relation_heat <- function(moduleTraitCor, moduleTraitPvalue) { - p <- plot_ly( - x = colnames(moduleTraitCor), - y = rownames(moduleTraitCor), - z = moduleTraitCor, - colorbar = list(title = "Correlation", len = 1), - type = "heatmap", - hoverinfo = 'text', - text = matrix( - paste( - #"Trait:", colnames(moduleTraitCor), - "
Module:", - rownames(moduleTraitCor), - "
Correlation:", - signif(moduleTraitCor, 2), - "
P-value:", - signif(moduleTraitPvalue, 1) - ), - nrow = nrow(moduleTraitCor), - ncol = ncol(moduleTraitCor) - ) - ) %>% - plotly::layout( - title = "Module & Trait relation", - xaxis = list(title = ''), - yaxis = list(title = '') - ) %>% - config( - toImageButtonOptions = list( - format = "png", - filename = "trait_relation", - width = 1500, - height = 1000 - ) - ) - p - } - -## -------------------------------------------------------------------------- - -## ----- POWER PLOTS ----- - - -#' Create dot plot with power values (scale independence). -#' A R^2 cutoff line indicates the min. power levels. -#' -#' @param soft Dataframe, soft threshold values per power -#' @param cutoff Integer, R^2 cutoff value -#' -#' @return p, (Plotly object) plot -#' -#' @export - -plot_power <- function(soft, cutoff) { - soft_combined <- data.frame( - pwr = soft$fitIndices[, 1], - value = -sign(soft$fitIndices[, 3]) * soft$fitIndices[, 2] - ) - - p <- plot_ly( - data = soft_combined, - x = ~ pwr, - y = ~ value, - type = "scattergl", - mode = "markers", - text = ~ paste("Power:", pwr), - hoverinfo = 'text' - ) %>% - plotly::layout( - title = "Scale independence", - xaxis = list(title = "Soft Threshold (power)"), - yaxis = list(title = "Scale Free Topology Model Fit, signed R^2"), - shapes = list( - list( - type = "line", - line = list(color = "red"), - x0 = 0, - x1 = 1, - xref = "paper", - y0 = cutoff, - y1 = cutoff - ) - ) - ) %>% - config(toImageButtonOptions = list( - format = "png", - filename = "power1", - width = 1500, - height = 1000 - )) - p -} - - -#' Create dot plot with soft power values (mean connectivity). -#' -#' @param soft Dataframe, soft threshold values per power -#' -#' @return p, (Plotly object) plot -#' -#' @export - -plot_soft <- function(soft) { - soft_combined <- data.frame(pwr = soft$fitIndices[, 1], - value = soft$fitIndices[, 5]) - - p <- plot_ly( - data = soft_combined, - x = ~ pwr, - y = ~ value, - type = "scattergl", - mode = "markers", - text = ~ paste("Power: ", pwr), - hoverinfo = 'text' - ) %>% - plotly::layout( - title = "Mean connectivity", - xaxis = list(title = "Soft Threshold (power)"), - yaxis = list(title = "Mean Connectivity") - ) %>% - config(toImageButtonOptions = list( - format = "png", - filename = "power2", - width = 1500, - height = 1000 - )) - p -} - - -## -------------------------------------------------------------------------- - -## ----- MODULES BARPLOT ----- - -#' Barplot with module sizes. -#' Module sizes are defined in percentages and colors are added -#' Stacked barplot is returned -#' -#' @param color Vector, all module colors of all used genes -#' -#' @return p, (Plotly object) plot -#' -#' @export - -plot_modules <- function(color) { - colorTable <- data.frame(table(color)) - colorTable <- colorTable[order(match(colorTable$color, unique(color))), ] - colorTable$perc <- colorTable$Freq / sum(colorTable$Freq) - colorTable$color <- factor(colorTable$color, levels = colorTable$color) - - p <- plot_ly( - data = colorTable, - x = ~ perc, - y = NULL, - type = "bar", - color = ~ color, - marker = list(color = colorTable$color), - text = ~ paste(Freq, "Genes"), - hoverinfo = "text" - ) %>% - plotly::layout( - barmode = "stack", - showlegend = FALSE, - xaxis = list( - title = "", - zeroline = FALSE, - showline = FALSE, - showticklabels = FALSE, - showgrid = FALSE - ), - yaxis = list( - zeroline = FALSE, - showline = FALSE, - showticklabels = FALSE, - showgrid = FALSE - ) - ) - p -} - -## -------------------------------------------------------------------------- From a27d911787ce769d34c7db7f637137137df17545 Mon Sep 17 00:00:00 2001 From: Tom Kuipers Date: Fri, 11 Jun 2021 11:21:42 +0200 Subject: [PATCH 18/31] Add dendrogram --- R/de.R | 112 ++++++++++++++++++++++++ R/de_plots.R | 57 +++++++++++- inst/src/tabs/dimension/svr_dimension.R | 31 +++++++ inst/src/tabs/dimension/ui_dimension.R | 20 +++++ 4 files changed, 218 insertions(+), 2 deletions(-) diff --git a/R/de.R b/R/de.R index 9350397..1b05546 100644 --- a/R/de.R +++ b/R/de.R @@ -167,3 +167,115 @@ gamConfidenceFit <- function(deTab, biasColumn) { } ## -------------------------------------------------------------------------- + +## ----- TREE PLOTS ----- + + +#' Get heigth information from tree object. +#' Get labels/names from tree ends. +#' Calculate start and end positions of tree branches +#' Save line coordinates in dataframe +#' +#' @param tree Hclust object, tree object +#' +#' @return new, (Dataframe) dataframe with all dendrogram line coordinates +#' +#' @export + +get_dendrogram_data <- function(tree, + labels = NULL, + horiz = FALSE, + reverseDirection = FALSE, + hang = 0.1, + xlab = "", + ylab = "", + axes = TRUE, + cex.labels = 1, + ..., + adjustRange = FALSE) { + hang.gr = hang + if (hang < 0) + hang.gr = 0.1 + n = length(tree$order) + heights = tree$height + range = range(heights) + hang.scaled = hang.gr * (max(heights) - min(heights)) + range[1] = range[1] - hang.scaled + + indexLim = c(0.5, n + 0.5) + if (adjustRange) + { + ctr = mean(indexLim) + indexLim = ctr + (indexLim - ctr) / 1.08 + } + + nMerge = n - 1 + if (is.null(labels)) + labels = tree$labels + if (is.null(labels)) + labels = rep("", n) + if (is.na(labels[1])) + labels = rep("", n) + if (is.logical(labels) && labels[1] == "FALSE") + labels = rep("", n) + + singleton.x = rep(NA, n) + singleton.x[tree$order] = c(1:n) + cluster.x = rep(NA, n) + + new <- data.frame(matrix( + ncol = 5, + nrow = 0, + dimnames = list(NULL, c("label", "x", "y", "xend", "yend")) + )) + for (m in 1:nMerge) { + o1 = tree$merge[m, 1] + o2 = tree$merge[m, 2] + h = heights[m] + hh = if (hang > 0) + h - hang.scaled + else + range[1] + h1 = if (o1 < 0) + hh + else + heights[o1] + h2 = if (o2 < 0) + hh + else + heights[o2] + + x1 = if (o1 < 0) + singleton.x[-o1] + else + cluster.x[o1] + x2 = if (o2 < 0) + singleton.x[-o2] + else + cluster.x[o2] + + cluster.x[m] = mean(c(x1, x2)) + + label1 <- labels[-o1] + label2 <- labels[-o2] + if (length(label1) > 1) { + label1 <- "" + } + if (length(label2) > 1) { + label2 <- "" + } + + if (!is.na(x1)) { + new[nrow(new) + 1, ] <- c(label1, x1, h1, x1, h) + if (!is.na(x2)) { + new[nrow(new) + 1, ] <- c("", x1, h, x2, h) + new[nrow(new) + 1, ] <- c(label2, x2, h2, x2, h) + } + } + } + new[2:5] <- sapply(new[2:5], as.double) + new <- new[order(new$x), ] + return(new) +} + +## -------------------------------------------------------------------------- diff --git a/R/de_plots.R b/R/de_plots.R index af09a9a..13bfb79 100644 --- a/R/de_plots.R +++ b/R/de_plots.R @@ -106,8 +106,8 @@ alignmentSummaryPlot <- function(se, sort_value = "None", perc = T) { plotly::subplot( plot_list, nrows = length(unique(lse$order)), - shareY = T, - shareX = T, + shareY = TRUE, + shareX = TRUE, margin = 0.005 ) } @@ -591,6 +591,59 @@ tsnePlot <- function(dge, color) { p } +#' Get dendrogram data dendro_data(). +#' Plot a dendrogram. +#' +#' @param d Hclust object, tree object +#' @param color String, color samples +#' @param color_list Vector, Color genes +#' +#' @return p, (Plotly object) plot +#' +#' @export + +plotly_dendrogram <- function(d, color, color_list) { + dendro_data <- get_dendrogram_data(d) + + p <- plot_ly( + data = dendro_data, + x = ~ x, + y = ~ y, + color = I("black"), + hoverinfo = "none" + ) %>% + add_segments(xend = ~ xend, + yend = ~ yend, + showlegend = FALSE) %>% + add_markers( + data = dendro_data[dendro_data$label != "", ], + x = ~ x, + y = ~ y, + color = color, + marker = list(size = 10, + color = color_list), + text = ~ label, + hoverinfo = 'text' + ) %>% + plotly::layout( + dragmode = "zoom", + title = "Dendrogram", + xaxis = list( + title = "", + showticklabels = FALSE, + zeroline = FALSE + ), + yaxis = list(title = "Height") + ) %>% + config(toImageButtonOptions = list( + format = "png", + filename = "dendro", + width = 1500, + height = 1000 + )) + p +} + ## -------------------------------------------------------------------------- ## ----- HEATMAPS PLOTS ----- diff --git a/inst/src/tabs/dimension/svr_dimension.R b/inst/src/tabs/dimension/svr_dimension.R index 1b28635..9f577de 100644 --- a/inst/src/tabs/dimension/svr_dimension.R +++ b/inst/src/tabs/dimension/svr_dimension.R @@ -141,6 +141,31 @@ output[["selected_norm_mds"]] <- DT::renderDataTable({ }) }) +## Sample dendrogram +output[["dim_dendro"]] <- renderPlotly({ + tryCatch({ + checkReload() + + sampleTree <- hclust(dist(t(inUse_normDge$counts)), method = "average") + plotly_dendrogram(sampleTree, inUse_normDge$samples[[input$color_dendro]], NA) + }, error = function(err) { + return(NULL) + }) +}) + +## Set color of sample dendrogram +output[["color_dendro"]] <- renderUI({ + tryCatch({ + selectInput( + inputId = "color_dendro", + label = "Color by:", + choices = colnames(data_samples()) + ) + }, error = function(err) { + return(NULL) + }) +}) + ## PC values per gene (table) pc_gene_table <- reactive({ tryCatch({ @@ -186,3 +211,9 @@ output[["dim_tsne_info"]] <- renderUI({ multi-dimensional map." informationBox(infoText) }) + +output[["dendro_info"]] <- renderUI({ + infoText <- "Hierarchical clustering of samples using normalized data. Log2CPM + values are used to create the dendrogram. " + informationBox(infoText) +}) diff --git a/inst/src/tabs/dimension/ui_dimension.R b/inst/src/tabs/dimension/ui_dimension.R index 918b184..d4348e3 100644 --- a/inst/src/tabs/dimension/ui_dimension.R +++ b/inst/src/tabs/dimension/ui_dimension.R @@ -89,6 +89,26 @@ tab_dimension <- tabItem( HTML('
'), DT::dataTableOutput("selected_norm_mds") %>% withSpinner(), HTML('
') + ), + + tabPanel( + title = "Dendrogram", + HTML('
'), + sidebarLayout( + position = "right", + sidebarPanel( + width = 3, + uiOutput("color_dendro"), + br(), + uiOutput("dendro_info"), + span(icon("copyright"), "LUMC - SASC", style = "color: #e3e3e3;") + ), + mainPanel( + width = 9, + plotlyOutput("dim_dendro", height = "600px") %>% withSpinner() + ) + ), + HTML('
') ) ) ) From b1d9f212903977380f238d96bb3a8b7d0dde4488 Mon Sep 17 00:00:00 2001 From: Tom Kuipers Date: Fri, 11 Jun 2021 11:22:03 +0200 Subject: [PATCH 19/31] tab name update --- inst/src/tabs/norm_data/ui_norm.R | 2 +- inst/src/tabs/raw_data/ui_raw.R | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/inst/src/tabs/norm_data/ui_norm.R b/inst/src/tabs/norm_data/ui_norm.R index 4590292..c56c89c 100644 --- a/inst/src/tabs/norm_data/ui_norm.R +++ b/inst/src/tabs/norm_data/ui_norm.R @@ -16,7 +16,7 @@ tab_norm_data <- tabItem( ), tabPanel( - title = "Line distribution", + title = "Count distribution", HTML('
'), sidebarLayout( position = "right", diff --git a/inst/src/tabs/raw_data/ui_raw.R b/inst/src/tabs/raw_data/ui_raw.R index 9f231ed..b74fb4c 100644 --- a/inst/src/tabs/raw_data/ui_raw.R +++ b/inst/src/tabs/raw_data/ui_raw.R @@ -6,7 +6,7 @@ tab_raw_data <- tabItem( tabsetPanel( tabPanel( - title = "Line distribution", + title = "Count distribution", HTML('
'), sidebarLayout( position = "right", From 3200b75101b9406d39831267a9230eecb9fbc208 Mon Sep 17 00:00:00 2001 From: Tom Kuipers Date: Fri, 11 Jun 2021 12:07:49 +0200 Subject: [PATCH 20/31] Add new cpm filter --- inst/src/markdown/analysisDESeq2.Rmd | 5 ++++- inst/src/markdown/analysisEdgeR.Rmd | 5 ++++- inst/src/markdown/analysisLimma.Rmd | 5 ++++- inst/src/tabs/run_analysis/svr_run.R | 11 ++++++----- inst/src/tabs/run_analysis/ui_run.R | 14 ++++++++++++-- 5 files changed, 30 insertions(+), 10 deletions(-) diff --git a/inst/src/markdown/analysisDESeq2.Rmd b/inst/src/markdown/analysisDESeq2.Rmd index 4bca9d7..c790101 100644 --- a/inst/src/markdown/analysisDESeq2.Rmd +++ b/inst/src/markdown/analysisDESeq2.Rmd @@ -19,6 +19,7 @@ params: data_annotation: "" setGeneName: "" cpm_value: "" + cpm_perc: "" excluded_samples: "" design_base: "" design_value: "" @@ -50,6 +51,7 @@ data_counts <- params$data_counts data_annotation <- params$data_annotation setGeneName <- params$setGeneName cpm_value <- params$cpm_value +cpm_perc <- params$cpm_perc excluded_samples <- params$excluded_samples design_base <- params$design_base design_value <- params$design_value @@ -74,6 +76,7 @@ data_counts <- data_counts[,!colnames(data_counts) %in% excluded_samples] # SHOW VALUES OF VARIABLES cpm_value +cpm_perc excluded_samples alpha design_base @@ -139,7 +142,7 @@ analysis <- analysis[rowSums(abs(assay(analysis))) > 1,] # FILTER IF NESSECARY counts <- cpm(counts(analysis), log = TRUE, prior.count = 1) selectedFeatures <- rownames(analysis)[apply(counts, 1, function(v) - sum(v >= cpm_value)) >= 1 / 4 * ncol(counts)] + sum(v >= cpm_value)) >= (cpm_perc / 100) * ncol(counts)] analysis <- analysis[selectedFeatures, ] ``` diff --git a/inst/src/markdown/analysisEdgeR.Rmd b/inst/src/markdown/analysisEdgeR.Rmd index 71f3f60..58f9c1d 100644 --- a/inst/src/markdown/analysisEdgeR.Rmd +++ b/inst/src/markdown/analysisEdgeR.Rmd @@ -19,6 +19,7 @@ params: data_annotation: "" setGeneName: "" cpm_value: "" + cpm_perc: "" excluded_samples: "" design_base: "" design_value: "" @@ -50,6 +51,7 @@ data_counts <- params$data_counts data_annotation <- params$data_annotation setGeneName <- params$setGeneName cpm_value <- params$cpm_value +cpm_perc <- params$cpm_perc excluded_samples <- params$excluded_samples design_base <- params$design_base design_value <- params$design_value @@ -74,6 +76,7 @@ data_counts <- data_counts[, !colnames(data_counts) %in% excluded_samples] # SHOW VALUES OF VARIABLES cpm_value +cpm_perc excluded_samples alpha design_base @@ -136,7 +139,7 @@ The raw data is filtered based on the input values provided. edger <- calcNormFactors(dge, method = "TMM") counts <- cpm(edger, log = TRUE, prior.count = 1) selectedFeatures <- rownames(edger)[apply(counts, 1, function(v) - sum(v >= cpm_value)) >= 1 / 4 * ncol(counts)] + sum(v >= cpm_value)) >= (cpm_perc / 100) * ncol(counts)] ``` diff --git a/inst/src/markdown/analysisLimma.Rmd b/inst/src/markdown/analysisLimma.Rmd index 05284e2..02c4225 100644 --- a/inst/src/markdown/analysisLimma.Rmd +++ b/inst/src/markdown/analysisLimma.Rmd @@ -19,6 +19,7 @@ params: data_annotation: "" setGeneName: "" cpm_value: "" + cpm_perc: "" excluded_samples: "" design_base: "" design_value: "" @@ -50,6 +51,7 @@ data_counts <- params$data_counts data_annotation <- params$data_annotation setGeneName <- params$setGeneName cpm_value <- params$cpm_value +cpm_perc <- params$cpm_perc excluded_samples <- params$excluded_samples design_base <- params$design_base design_value <- params$design_value @@ -74,6 +76,7 @@ data_counts <- data_counts[, !colnames(data_counts) %in% excluded_samples] # SHOW VALUES OF VARIABLES cpm_value +cpm_perc excluded_samples alpha design_base @@ -136,7 +139,7 @@ The raw data is filtered based on the input values provided. limmaV <- calcNormFactors(dge, method = "TMM") counts <- cpm(limmaV, log = TRUE, prior.count = 1) selectedFeatures <- rownames(limmaV)[apply(counts, 1, function(v) - sum(v >= cpm_value)) >= 1 / 4 * ncol(counts)] + sum(v >= cpm_value)) >= (cpm_perc / 100) * ncol(counts)] ``` diff --git a/inst/src/tabs/run_analysis/svr_run.R b/inst/src/tabs/run_analysis/svr_run.R index 3d86702..608de1c 100644 --- a/inst/src/tabs/run_analysis/svr_run.R +++ b/inst/src/tabs/run_analysis/svr_run.R @@ -1,6 +1,7 @@ ## Start an analysis observeEvent(input$run_button, { + print(input$cpm_perc) if (!is.null(preMarkdownChecks())) { showNotification(ui = preMarkdownChecks(), duration = 5, @@ -24,6 +25,7 @@ observeEvent(input$run_button, { excluded_samples = input$exclude_samples, setGeneName = input$setGeneName, cpm_value = input$cpm_value, + cpm_perc = input$cpm_perc, design_base = input$design_base, design_value = input$design_value, matrix_v1 = input$matrix_val1, @@ -215,16 +217,15 @@ output[["show_matrix"]] <- renderUI({ total_matrix2 <- paste(total_matrix2, collapse = " in ") if (total_matrix1 == "") { - total_matrix1 <- "No values selected" + total_matrix1 <- "NaN" } if (total_matrix2 == "") { - total_matrix2 <- "No values selected" + total_matrix2 <- "NaN" } - total_matrix <- - paste(total_matrix1, total_matrix2, sep = " VS ") + total_matrix <- paste("Genes are up/down regulated in", total_matrix1, "compared to", total_matrix2) }, error = function(err) { - return("No values selected VS No values selected") + return("No values selected") }) }) diff --git a/inst/src/tabs/run_analysis/ui_run.R b/inst/src/tabs/run_analysis/ui_run.R index d615ad5..0644dd3 100644 --- a/inst/src/tabs/run_analysis/ui_run.R +++ b/inst/src/tabs/run_analysis/ui_run.R @@ -20,7 +20,7 @@ tab_run_analysis <- tabItem( "EdgeR" = "analysisEdgeR", "DESeq2" = "analysisDESeq2" ) - ), + ), br(), sliderInput( inputId = "alpha_value", @@ -37,10 +37,20 @@ tab_run_analysis <- tabItem( min = 0, max = 10, step = 0.1 + ), + sliderInput( + inputId = "cpm_perc", + label = "Set how many samples should meet CPM cutoff:", + value = 25, + min = 0, + max = 100, + step = 1, + post = "%" ) ), column( width = 6, + br(), br(), br(), uiOutput("design_base"), radioButtons( inputId = "design_type", @@ -64,7 +74,7 @@ tab_run_analysis <- tabItem( ), column( width = 6, - h4("Find genes that respond to:"), + h4("Read this comparison as:"), div(br(), uiOutput("show_matrix"), br(), From e16da9d0d4eebcf9bd67d03d6bb14230c9a26b66 Mon Sep 17 00:00:00 2001 From: Tom Kuipers Date: Fri, 11 Jun 2021 13:39:40 +0200 Subject: [PATCH 21/31] add filenames + md5 --- inst/src/markdown/analysisDESeq2.Rmd | 3 +++ inst/src/markdown/analysisEdgeR.Rmd | 3 +++ inst/src/markdown/analysisLimma.Rmd | 3 +++ inst/src/tabs/run_analysis/svr_run.R | 21 ++++++++++++++++++++- 4 files changed, 29 insertions(+), 1 deletion(-) diff --git a/inst/src/markdown/analysisDESeq2.Rmd b/inst/src/markdown/analysisDESeq2.Rmd index c790101..aaaa81f 100644 --- a/inst/src/markdown/analysisDESeq2.Rmd +++ b/inst/src/markdown/analysisDESeq2.Rmd @@ -14,6 +14,7 @@ output: theme: paper always_allow_html: true params: + md5sum: "" data_samples: "" data_counts: "" data_annotation: "" @@ -46,6 +47,7 @@ if (!require("plotly")) install.packages("plotly") ```{r variables} # IMPORT VARIABLES +md5sum <- params$md5sum data_samples <- params$data_samples data_counts <- params$data_counts data_annotation <- params$data_annotation @@ -75,6 +77,7 @@ data_counts <- data_counts[,!colnames(data_counts) %in% excluded_samples] ```{r showVariables} # SHOW VALUES OF VARIABLES +md5sum cpm_value cpm_perc excluded_samples diff --git a/inst/src/markdown/analysisEdgeR.Rmd b/inst/src/markdown/analysisEdgeR.Rmd index 58f9c1d..61f8326 100644 --- a/inst/src/markdown/analysisEdgeR.Rmd +++ b/inst/src/markdown/analysisEdgeR.Rmd @@ -14,6 +14,7 @@ output: theme: paper always_allow_html: true params: + md5sum: "" data_samples: "" data_counts: "" data_annotation: "" @@ -46,6 +47,7 @@ if (!require("plotly")) install.packages("plotly") ```{r variables} # IMPORT VARIABLES +md5sum <- params$md5sum data_samples <- params$data_samples data_counts <- params$data_counts data_annotation <- params$data_annotation @@ -75,6 +77,7 @@ data_counts <- data_counts[, !colnames(data_counts) %in% excluded_samples] ```{r showVariables} # SHOW VALUES OF VARIABLES +md5sum cpm_value cpm_perc excluded_samples diff --git a/inst/src/markdown/analysisLimma.Rmd b/inst/src/markdown/analysisLimma.Rmd index 02c4225..80b4465 100644 --- a/inst/src/markdown/analysisLimma.Rmd +++ b/inst/src/markdown/analysisLimma.Rmd @@ -14,6 +14,7 @@ output: theme: paper always_allow_html: true params: + md5sum: "" data_samples: "" data_counts: "" data_annotation: "" @@ -46,6 +47,7 @@ if (!require("plotly")) install.packages("plotly") ```{r variables} # IMPORT VARIABLES +md5sum <- params$md5sum data_samples <- params$data_samples data_counts <- params$data_counts data_annotation <- params$data_annotation @@ -75,6 +77,7 @@ data_counts <- data_counts[, !colnames(data_counts) %in% excluded_samples] ```{r showVariables} # SHOW VALUES OF VARIABLES +md5sum cpm_value cpm_perc excluded_samples diff --git a/inst/src/tabs/run_analysis/svr_run.R b/inst/src/tabs/run_analysis/svr_run.R index 608de1c..f839db8 100644 --- a/inst/src/tabs/run_analysis/svr_run.R +++ b/inst/src/tabs/run_analysis/svr_run.R @@ -1,7 +1,6 @@ ## Start an analysis observeEvent(input$run_button, { - print(input$cpm_perc) if (!is.null(preMarkdownChecks())) { showNotification(ui = preMarkdownChecks(), duration = 5, @@ -19,6 +18,7 @@ observeEvent(input$run_button, { rmarkdown::render( input = paste0("markdown/", input$analysis_method, ".Rmd"), params = list( + md5sum = getMD5(), data_samples = data_samples(), data_counts = data_counts(), data_annotation = data_annotation(), @@ -65,10 +65,29 @@ preMarkdownChecks <- reactive ({ if (input$setGeneName == "symbol" & !("geneName" %in% colnames(data_annotation()))) { return("The annotation file is missing a column: 'geneName'!") } + } else if (is.null(input$file_counts)) { + return("No expression data uploaded!") } return(NULL) }) +## Get MD5 info from uploaded files +getMD5 <- reactive({ + md5 <- tryCatch({ + c( + paste0(input$file_samples$name, ": ", tools::md5sum(files = input$file_samples$datapath)), + paste0(input$file_counts$name, ": ", tools::md5sum(files = input$file_counts$datapath)), + paste0(input$file_annotation$name, ": ", tools::md5sum(files = input$file_annotation$datapath)) + ) + }, error = function(err) { + c( + paste0(input$file_samples$name, ": ", tools::md5sum(files = input$file_samples$datapath)), + paste0(input$file_counts$name, ": ", tools::md5sum(files = input$file_counts$datapath)) + ) + }) + as.matrix(md5) +}) + ## Render base column for design output[["design_base"]] <- renderUI({ tryCatch({ From 132a20eb8a8a6c7a1484c6af52b2f3c45f091555 Mon Sep 17 00:00:00 2001 From: Tom Kuipers Date: Fri, 11 Jun 2021 13:40:00 +0200 Subject: [PATCH 22/31] small code fixes --- inst/src/tabs/alignment/svr_alignment.R | 1 - inst/src/tabs/analysis/svr_analysis.R | 6 ------ inst/src/tabs/bias/svr_bias.R | 6 ------ inst/src/tabs/dimension/svr_dimension.R | 3 --- inst/src/tabs/raw_data/svr_raw.R | 3 --- inst/src/tabs/upload/svr_upload.R | 14 +++++++------- 6 files changed, 7 insertions(+), 26 deletions(-) diff --git a/inst/src/tabs/alignment/svr_alignment.R b/inst/src/tabs/alignment/svr_alignment.R index d98c929..68b689e 100644 --- a/inst/src/tabs/alignment/svr_alignment.R +++ b/inst/src/tabs/alignment/svr_alignment.R @@ -45,7 +45,6 @@ output[["complex"]] <- renderPlotly({ } complexityPlot(get_se(), input$group_color, perc, input$comp_rank) }, error = function(err) { - print(err) return(NULL) }) }) diff --git a/inst/src/tabs/analysis/svr_analysis.R b/inst/src/tabs/analysis/svr_analysis.R index c7c8203..43aeaae 100644 --- a/inst/src/tabs/analysis/svr_analysis.R +++ b/inst/src/tabs/analysis/svr_analysis.R @@ -39,9 +39,6 @@ output[["ma_plot"]] <- renderPlotly({ output[["selected_ma"]] <- DT::renderDataTable({ tryCatch({ s <- event_data(event = "plotly_selected", source = "analysis_ma") - if (is.null(s)) { - s <- "" - } DT::datatable(inUse_deTab[s$key,], options = list(pageLength = 15, scrollX = TRUE)) }, error = function(err) { return(DT::datatable(data.frame(c( @@ -66,9 +63,6 @@ output[["volcano_plot"]] <- renderPlotly({ output[["selected_volcano"]] <- DT::renderDataTable({ tryCatch({ s <- event_data(event = "plotly_selected", source = "analysis_volcano") - if (is.null(s)) { - s <- "" - } DT::datatable(inUse_deTab[s$key,], options = list(pageLength = 15, scrollX = TRUE)) }, error = function(err) { return(DT::datatable(data.frame(c( diff --git a/inst/src/tabs/bias/svr_bias.R b/inst/src/tabs/bias/svr_bias.R index 1f9822d..3f0eae3 100644 --- a/inst/src/tabs/bias/svr_bias.R +++ b/inst/src/tabs/bias/svr_bias.R @@ -13,9 +13,6 @@ output[["gc_bias"]] <- renderPlotly({ output[["selected_biasgc"]] <- DT::renderDataTable({ tryCatch({ s <- event_data(event = "plotly_selected", source = "biasGC") - if (is.null(s)) { - s <- "" - } DT::datatable(inUse_deTab[s$key, ], options = list(pageLength = 15, scrollX = TRUE)) }, error = function(err) { return(DT::datatable(data.frame(c( @@ -57,9 +54,6 @@ output[["len_bias"]] <- renderPlotly({ output[["selected_biaslength"]] <- DT::renderDataTable({ tryCatch({ s <- event_data(event = "plotly_selected", source = "biasLength") - if (is.null(s)) { - s <- "" - } DT::datatable(inUse_deTab[s$key, ], options = list(pageLength = 15, scrollX = TRUE)) }, error = function(err) { return(DT::datatable(data.frame(c( diff --git a/inst/src/tabs/dimension/svr_dimension.R b/inst/src/tabs/dimension/svr_dimension.R index 9f577de..d621fa8 100644 --- a/inst/src/tabs/dimension/svr_dimension.R +++ b/inst/src/tabs/dimension/svr_dimension.R @@ -62,9 +62,6 @@ output[["selected_pca"]] <- DT::renderDataTable({ tryCatch({ checkReload() s <- event_data(event = "plotly_selected", source = "pca") - if (is.null(s)) { - s <- "" - } DT::datatable(data_samples()[unlist(s$key), , drop = FALSE], options = list(pageLength = 15, scrollX = TRUE)) }, error = function(err) { return(DT::datatable(data.frame(c( diff --git a/inst/src/tabs/raw_data/svr_raw.R b/inst/src/tabs/raw_data/svr_raw.R index df26023..147881e 100644 --- a/inst/src/tabs/raw_data/svr_raw.R +++ b/inst/src/tabs/raw_data/svr_raw.R @@ -115,9 +115,6 @@ output[["selected_raw_mds"]] <- DT::renderDataTable({ tryCatch({ checkReload() s <- event_data(event = "plotly_selected", source = "raw_mds") - if (is.null(s)) { - s <- "" - } DT::datatable(data_samples()[unlist(s$key), , drop = FALSE], options = list(pageLength = 15, scrollX = TRUE)) }, error = function(err) { return(DT::datatable(data.frame(c( diff --git a/inst/src/tabs/upload/svr_upload.R b/inst/src/tabs/upload/svr_upload.R index a703a26..9eec5f0 100644 --- a/inst/src/tabs/upload/svr_upload.R +++ b/inst/src/tabs/upload/svr_upload.R @@ -51,7 +51,7 @@ observeEvent(input$app_mode, { ## Read sample data file data_samples <- reactive({ if (input$app_mode == "new") { - if (is.null(input$file_samples$datapath)) { + if (is.null(input$file_samples)) { return(NULL) } data_samples <- read.csv( @@ -71,7 +71,7 @@ data_samples <- reactive({ } } if (input$app_mode == "view") { - if (is.null(input$file_samples_view$datapath)) { + if (is.null(input$file_samples_view)) { return(NULL) } data_samples <- read.csv( @@ -96,7 +96,7 @@ data_samples <- reactive({ ## Read count data file data_counts <- reactive({ if (input$app_mode == "new") { - if (is.null(input$file_counts$datapath)) { + if (is.null(input$file_counts)) { return(NULL) } data_counts <- read.csv( @@ -116,7 +116,7 @@ data_counts <- reactive({ } } if (input$app_mode == "view") { - if (is.null(input$file_counts_view$datapath)) { + if (is.null(input$file_counts_view)) { return(NULL) } data_counts <- read.csv( @@ -140,7 +140,7 @@ data_counts <- reactive({ ## Read annotation data file data_annotation <- reactive({ - if (is.null(input$file_annotation$datapath)) { + if (is.null(input$file_annotation)) { return(NULL) } data_annotation <- read.csv( @@ -163,7 +163,7 @@ data_annotation <- reactive({ ## Read normalized data file data_norm <- reactive({ - if (is.null(input$file_norm_view$datapath)) { + if (is.null(input$file_norm_view)) { return(NULL) } data_norm <- read.csv( @@ -186,7 +186,7 @@ data_norm <- reactive({ ## Read detab data file data_detab <- reactive({ - if (is.null(input$file_detab_view$datapath)) { + if (is.null(input$file_detab_view)) { return(NULL) } data_detab <- read.csv( From 5d8058919708cde16dbc59fe394be253bafc542c Mon Sep 17 00:00:00 2001 From: Tom Kuipers Date: Mon, 14 Jun 2021 13:10:41 +0200 Subject: [PATCH 23/31] remove empty line --- inst/src/server.R | 1 - 1 file changed, 1 deletion(-) diff --git a/inst/src/server.R b/inst/src/server.R index 4f95ba9..5722734 100644 --- a/inst/src/server.R +++ b/inst/src/server.R @@ -156,5 +156,4 @@ server <- function(input, output, session) { source("tabs/gprofiler/svr_gprofiler.R", local = TRUE) source("tabs/export/svr_export.R", local = TRUE) source("tabs/about/svr_about.R", local = TRUE) - } From 1eed95fe556c4624996ecf972a3839238d9ad884 Mon Sep 17 00:00:00 2001 From: Tom Kuipers Date: Mon, 14 Jun 2021 13:40:56 +0200 Subject: [PATCH 24/31] clean up file upload --- inst/src/tabs/upload/svr_upload.R | 133 +++++++----------------------- 1 file changed, 28 insertions(+), 105 deletions(-) diff --git a/inst/src/tabs/upload/svr_upload.R b/inst/src/tabs/upload/svr_upload.R index 9eec5f0..4021659 100644 --- a/inst/src/tabs/upload/svr_upload.R +++ b/inst/src/tabs/upload/svr_upload.R @@ -48,47 +48,40 @@ observeEvent(input$app_mode, { ## ----- Read files ----- +## Read data files +readFiles <- function(input) { + df <- read.table( + file = input, + row.names = 1, + header = TRUE, + sep = "\t", + check.names = FALSE + ) + if (ncol(df) < 1) { + df <- read.table( + file = input, + row.names = 1, + header = TRUE, + sep = ",", + check.names = FALSE + ) + } + return(df) +} + ## Read sample data file data_samples <- reactive({ if (input$app_mode == "new") { if (is.null(input$file_samples)) { return(NULL) } - data_samples <- read.csv( - input$file_samples$datapath, - row.names = 1, - header = TRUE, - sep = "\t", - check.names = FALSE - ) - if (ncol(data_samples) < 1) { - data_samples <- read.csv( - input$file_samples$datapath, - row.names = 1, - header = TRUE, - check.names = FALSE - ) - } + data_samples <- readFiles(input = input$file_samples$datapath) } if (input$app_mode == "view") { if (is.null(input$file_samples_view)) { return(NULL) } - data_samples <- read.csv( - input$file_samples_view$datapath, - row.names = 1, - header = TRUE, - sep = "\t", - check.names = FALSE - ) - if (ncol(data_samples) < 1) { - data_samples <- read.csv( - input$file_samples_view$datapath, - row.names = 1, - header = TRUE, - check.names = FALSE - ) - } + data_samples <- readFiles(input = input$file_samples_view$datapath) } data_samples }) @@ -99,41 +92,13 @@ data_counts <- reactive({ if (is.null(input$file_counts)) { return(NULL) } - data_counts <- read.csv( - input$file_counts$datapath, - row.names = 1, - header = TRUE, - sep = "\t", - check.names = FALSE - ) - if (ncol(data_counts) < 1) { - data_counts <- read.csv( - input$file_counts$datapath, - row.names = 1, - header = TRUE, - check.names = FALSE - ) - } + data_counts <- readFiles(input = input$file_counts$datapath) } if (input$app_mode == "view") { if (is.null(input$file_counts_view)) { return(NULL) } - data_counts <- read.csv( - input$file_counts_view$datapath, - row.names = 1, - header = TRUE, - sep = "\t", - check.names = FALSE - ) - if (ncol(data_counts) < 1) { - data_counts <- read.csv( - input$file_counts_view$datapath, - row.names = 1, - header = TRUE, - check.names = FALSE - ) - } + data_counts <- readFiles(input = input$file_counts_view$datapath) } data_counts }) @@ -143,21 +108,7 @@ data_annotation <- reactive({ if (is.null(input$file_annotation)) { return(NULL) } - data_annotation <- read.csv( - input$file_annotation$datapath, - row.names = 1, - header = TRUE, - sep = "\t", - check.names = FALSE - ) - if (ncol(data_annotation) < 1) { - data_annotation <- read.csv( - input$file_annotation$datapath, - row.names = 1, - header = TRUE, - check.names = FALSE - ) - } + data_annotation <- readFiles(input = input$file_annotation$datapath) data_annotation }) @@ -166,21 +117,7 @@ data_norm <- reactive({ if (is.null(input$file_norm_view)) { return(NULL) } - data_norm <- read.csv( - input$file_norm_view$datapath, - row.names = 1, - header = TRUE, - sep = "\t", - check.names = FALSE - ) - if (ncol(data_norm) < 1) { - data_norm <- read.csv( - input$file_norm_view$datapath, - row.names = 1, - header = TRUE, - check.names = FALSE - ) - } + data_norm <- readFiles(input = input$file_norm_view$datapath) data_norm }) @@ -189,21 +126,7 @@ data_detab <- reactive({ if (is.null(input$file_detab_view)) { return(NULL) } - data_detab <- read.csv( - input$file_detab_view$datapath, - row.names = 1, - header = TRUE, - sep = "\t", - check.names = FALSE - ) - if (ncol(data_detab) < 1) { - data_detab <- read.csv( - input$file_detab_view$datapath, - row.names = 1, - header = TRUE, - check.names = FALSE - ) - } + data_detab <- readFiles(input = input$file_detab_view$datapath) data_detab }) From b1ade9e29fc84531c3b479d24b51a0b9b5e07eda Mon Sep 17 00:00:00 2001 From: Tom Kuipers Date: Mon, 14 Jun 2021 13:44:27 +0200 Subject: [PATCH 25/31] Style fixes --- R/markdown.R | 3 +-- inst/src/tabs/gprofiler/svr_gprofiler.R | 2 ++ inst/src/tabs/raw_data/svr_raw.R | 1 - inst/src/tabs/run_analysis/svr_run.R | 15 +++++++-------- 4 files changed, 10 insertions(+), 11 deletions(-) diff --git a/R/markdown.R b/R/markdown.R index 207f5ca..072dfcd 100644 --- a/R/markdown.R +++ b/R/markdown.R @@ -68,8 +68,7 @@ createDesign <- function(dge, dbase, dvalue, matrix_v1, matrix_v2) { #' #' @export -relevelSamples <- - function(dge, dbase, dvalue, matrix_v1, matrix_v2) { +relevelSamples <- function(dge, dbase, dvalue, matrix_v1, matrix_v2) { columns <- c(dbase, dvalue) for (value in columns) { newRef <- diff --git a/inst/src/tabs/gprofiler/svr_gprofiler.R b/inst/src/tabs/gprofiler/svr_gprofiler.R index 7fb20df..41879ba 100644 --- a/inst/src/tabs/gprofiler/svr_gprofiler.R +++ b/inst/src/tabs/gprofiler/svr_gprofiler.R @@ -209,6 +209,8 @@ output[["heat_table"]] <- DT::renderDataTable({ }) }) +## INFORMATION BOXES + output[["gProfiler2_plot_info"]] <- renderUI({ infoText <- "The gProfiler 2 manhattan plot shows the terms colored by source (database). diff --git a/inst/src/tabs/raw_data/svr_raw.R b/inst/src/tabs/raw_data/svr_raw.R index 147881e..5bdbf0e 100644 --- a/inst/src/tabs/raw_data/svr_raw.R +++ b/inst/src/tabs/raw_data/svr_raw.R @@ -123,7 +123,6 @@ output[["selected_raw_mds"]] <- DT::renderDataTable({ }) }) - ## INFORMATION BOXES output[["dist_line_info"]] <- renderUI({ diff --git a/inst/src/tabs/run_analysis/svr_run.R b/inst/src/tabs/run_analysis/svr_run.R index f839db8..cb7468b 100644 --- a/inst/src/tabs/run_analysis/svr_run.R +++ b/inst/src/tabs/run_analysis/svr_run.R @@ -186,14 +186,13 @@ output[["matrix_value2"]] <- renderUI({ ## Show the current design in use output[["show_design"]] <- renderUI({ tryCatch({ - design <- - createDesign( - data_samples(), - input$design_base, - input$design_value, - input$matrix_val1, - input$matrix_val2 - ) + design <- createDesign( + dge = data_samples(), + dbase = input$design_base, + dvalue = input$design_value, + matrix_v1 = input$matrix_val1, + matrix_v2 = input$matrix_val2 + ) design <- gsub("\\+", " + ", design) if (design == "~" | design == "~0 + ") { design <- "No values selected" From 140b901fa241173cad782cf6d7487ac7f7d8f4e5 Mon Sep 17 00:00:00 2001 From: Tom Kuipers Date: Mon, 14 Jun 2021 13:45:10 +0200 Subject: [PATCH 26/31] Update changelog --- CHANGELOG.md | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index f02fceb..525dca6 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -6,11 +6,14 @@ Newest changes should be on top. --> version 1.4.4 --------------------------- ++ Add file names and md5 to markdown ++ Add new CPM filter for number of samples (percentage) ++ Add dendrogram to dimension reduction + Change export file name and remove quotes from exported dataframe + Small infobox update -+ Add tSNE plot to Dimension reduction ++ Add tSNE plot to dimension reduction + Move normalized MDS to Dimension reduction -+ Change PCA tab to Dimension reduction ++ Change PCA tab to dimension reduction + Remove MDS 3D and PCA 3D + Fix false warning: Contrast are the same error From 19250fbe02eca2c2127cf426b284a0a7c85e6f19 Mon Sep 17 00:00:00 2001 From: Tom Kuipers Date: Mon, 14 Jun 2021 13:56:12 +0200 Subject: [PATCH 27/31] Style fix --- inst/src/tabs/dimension/svr_dimension.R | 20 ++++++++++---------- inst/src/tabs/dimension/ui_dimension.R | 23 +++++++++++------------ inst/src/tabs/run_analysis/ui_run.R | 2 +- 3 files changed, 22 insertions(+), 23 deletions(-) diff --git a/inst/src/tabs/dimension/svr_dimension.R b/inst/src/tabs/dimension/svr_dimension.R index d621fa8..990e1f2 100644 --- a/inst/src/tabs/dimension/svr_dimension.R +++ b/inst/src/tabs/dimension/svr_dimension.R @@ -1,14 +1,4 @@ -## Variance PCA -output[["variance_pca"]] <- renderPlotly({ - tryCatch({ - checkReload() - variancePcaPlot(inUse_normDge) - }, error = function(err) { - return(NULL) - }) -}) - ## PCA output[["pca"]] <- renderPlotly({ tryCatch({ @@ -70,6 +60,16 @@ output[["selected_pca"]] <- DT::renderDataTable({ }) }) +## Variance PCA +output[["variance_pca"]] <- renderPlotly({ + tryCatch({ + checkReload() + variancePcaPlot(inUse_normDge) + }, error = function(err) { + return(NULL) + }) +}) + ## tSNE output[["dim_tsne"]] <- renderPlotly({ tryCatch({ diff --git a/inst/src/tabs/dimension/ui_dimension.R b/inst/src/tabs/dimension/ui_dimension.R index d4348e3..bbf7468 100644 --- a/inst/src/tabs/dimension/ui_dimension.R +++ b/inst/src/tabs/dimension/ui_dimension.R @@ -6,47 +6,46 @@ tab_dimension <- tabItem( tabsetPanel( tabPanel( - title = "PCA variance", + title = "PCA", HTML('
'), sidebarLayout( position = "right", sidebarPanel( width = 3, - uiOutput("variance_pca_info"), + uiOutput("group_pca"), + uiOutput("setpc_pca"), + br(), + uiOutput("pca_info"), span(icon("copyright"), "LUMC - SASC", style = "color: #e3e3e3;") ), mainPanel( width = 9, - plotlyOutput("variance_pca", height = "600px") %>% withSpinner() + plotlyOutput("pca", height = "600px") %>% withSpinner() ) ), + HTML('
'), + DT::dataTableOutput("selected_pca") %>% withSpinner(), HTML('
') ), tabPanel( - title = "PCA", + title = "PCA variance", HTML('
'), sidebarLayout( position = "right", sidebarPanel( width = 3, - uiOutput("group_pca"), - uiOutput("setpc_pca"), - br(), - uiOutput("pca_info"), + uiOutput("variance_pca_info"), span(icon("copyright"), "LUMC - SASC", style = "color: #e3e3e3;") ), mainPanel( width = 9, - plotlyOutput("pca", height = "600px") %>% withSpinner() + plotlyOutput("variance_pca", height = "600px") %>% withSpinner() ) ), - HTML('
'), - DT::dataTableOutput("selected_pca") %>% withSpinner(), HTML('
') ), - tabPanel( title = "t-SNE", HTML('
'), diff --git a/inst/src/tabs/run_analysis/ui_run.R b/inst/src/tabs/run_analysis/ui_run.R index 0644dd3..0e865c7 100644 --- a/inst/src/tabs/run_analysis/ui_run.R +++ b/inst/src/tabs/run_analysis/ui_run.R @@ -40,7 +40,7 @@ tab_run_analysis <- tabItem( ), sliderInput( inputId = "cpm_perc", - label = "Set how many samples should meet CPM cutoff:", + label = "Minimum % of samples that meet Log2CPM cutoff:", value = 25, min = 0, max = 100, From 2c734f166dc0debd5ba221d79e6faf9b8e0544ee Mon Sep 17 00:00:00 2001 From: Tom Kuipers Date: Mon, 14 Jun 2021 15:22:58 +0200 Subject: [PATCH 28/31] Text and header update --- inst/src/server.R | 2 +- inst/src/tabs/home/svr_home.R | 23 ++++++++++++----------- 2 files changed, 13 insertions(+), 12 deletions(-) diff --git a/inst/src/server.R b/inst/src/server.R index 5722734..1882f36 100644 --- a/inst/src/server.R +++ b/inst/src/server.R @@ -30,7 +30,7 @@ server <- function(input, output, session) { alignment = "Alignment", raw_data = "Raw Data", norm_data = "Normalization", - dim = "Dimension reduction", + dimension = "Dimension reduction", heatmaps = "Heatmaps", analysis = "DE analysis", bias = "Bias", diff --git a/inst/src/tabs/home/svr_home.R b/inst/src/tabs/home/svr_home.R index ab28519..366c86f 100644 --- a/inst/src/tabs/home/svr_home.R +++ b/inst/src/tabs/home/svr_home.R @@ -6,17 +6,18 @@ output[["welcome"]] <- renderUI({ br(), span( "With the development of next-generation sequencing techniques, RNA-Seq is becoming a - more commonly applied method in various fields, e.g., cancer genomics, cell biology, etc. - RNA-Seq gives great insights into a biological species at a specific moment. To find out - which genes are playing an important function at a specific moment or under a specific - condition, differential gene expression analysis is used. With the use of this type of analysis, - research between different conditions can be conducted.", + more widely applied method in various fields, e.g. cancer genomics, cell biology, etc. + RNA-Seq gives great insights into a biological species at a specific time. Find out + which genes play an important function at a certain time or under a certain + condition, differential gene expression analysis is used. Using this type of analysis, + research between different conditions can be performed.", style = "text-align: justify; display: block; width: 90%" ), span( - "Using the application for differential gene analysis can be very helpful in many projects. Next - to checking data quality, it also provides a platform to make a quick analysis. This makes it - an easy to use platform capable of performing a variety of analysis possibilities.", + "Using the dgeAnalysis application for differential gene analysis can be very + helpful in many projects. In addition to checking data quality, it also provides a platform + to make a quick analysis. This makes it a user-friendly platform capable of performing + various analysis options.", style = "text-align: justify; display: block; width: 90%" ), br(), @@ -35,7 +36,7 @@ output[["get_started"]] <- renderUI({ span( "1. Go to the 'Data upload' page and select 'New analysis'.", tags$br(), - "2. Upload files (both CSV and TSV is accepted):", + "2. Upload files (both CSV and TSV are accepted):", tags$br(), HTML("  "), "- Samplesheet (metadata), Contains information about each sample.", @@ -48,7 +49,7 @@ output[["get_started"]] <- renderUI({ tags$br(), "3. Go to the 'Run analysis' page.", tags$br(), - "4. Adjust the settings for to the desired analysis.", + "4. Adjust the settings for the desired analysis.", tags$br(), "5. Run the analysis.", tags$br(), @@ -67,7 +68,7 @@ output[["get_started"]] <- renderUI({ span( "1. Go to the 'Data upload' page and select 'View analysis'.", tags$br(), - "2. Upload files (both CSV and TSV is accepted):", + "2. Upload files (both CSV and TSV are accepted):", tags$br(), HTML("  "), "- Samplesheet (metadata), Contains information about each sample.", From f653727bbee6e37adb86434cfadd88221967f6a1 Mon Sep 17 00:00:00 2001 From: Tom Kuipers Date: Mon, 14 Jun 2021 15:23:26 +0200 Subject: [PATCH 29/31] manual and programflow update --- MANUAL.pdf | Bin 638080 -> 1329363 bytes inst/src/www/programFlow.png | Bin 1949586 -> 1989076 bytes 2 files changed, 0 insertions(+), 0 deletions(-) diff --git a/MANUAL.pdf b/MANUAL.pdf index dcdd746d48348a4c89451269e5716b9ae9d0eab4..19abb3807786c8facdc17c6b8ff65f3840b70da0 100644 GIT binary patch literal 1329363 zcmeFXcT|&0yElxLt!`zr0Z|c9P(f)T0!meB(v>PD1nE8W(4!(n5D*YTM}&Z(N(q4g ziHbC78bXKAODG8~1VYLixBEHIclPss=ihIAYn^+oVR7f4xn{0@&2Q%FBXvy)Y029s zuMVsdW==|9x_Jp?=XO#_Ny^04-^1~eXn>!iFVOR;SHM$`>nEii`GOn*>>YhCi5k1R zK9yn(zT|mV=8_f&620q^e;&n zfP6ildi=YS?7usy-T&F?S64l?OE*tS>FNCS*Zh*Sq|C2J{-l(?-X&?)H{<)4{Cxu) z|L;`g{!P^+*xT`v)DuHHH%EJa*02CO|G$3G_I&Eh8g1_C;O}zjmh4Ry$%l@v&My9! 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00:00:00 2001 From: Tom Kuipers Date: Mon, 14 Jun 2021 15:44:36 +0200 Subject: [PATCH 30/31] update --- DESCRIPTION | 8 ++++---- NAMESPACE | 6 ------ man/get_dendrogram_data.Rd | 2 +- man/plot_module_trait_relation_heat.Rd | 21 --------------------- man/plot_modules.Rd | 21 --------------------- man/plot_power.Rd | 21 --------------------- man/plot_soft.Rd | 17 ----------------- man/plot_trait_heat.Rd | 19 ------------------- man/plotly_dendro_heat.Rd | 19 ------------------- man/plotly_dendrogram.Rd | 2 +- 10 files changed, 6 insertions(+), 130 deletions(-) delete mode 100644 man/plot_module_trait_relation_heat.Rd delete mode 100644 man/plot_modules.Rd delete mode 100644 man/plot_power.Rd delete mode 100644 man/plot_soft.Rd delete mode 100644 man/plot_trait_heat.Rd delete mode 100644 man/plotly_dendro_heat.Rd diff --git a/DESCRIPTION b/DESCRIPTION index 06d8f95..c212eb4 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -3,7 +3,7 @@ Type: Package Title: dgeAnalysis Version: 1.4.4 Author@R: - person("Tom", "Kuipers", email = "t.b.kuipers@outlook.com", role = c("aut", "cre")) + person("Tom", "Kuipers", email = "t.b.kuipers@lumc.nl", role = c("aut", "cre")) Description: R shiny tool to perform differential gene expression and view the results interactively. Depends: R (>= 3.6.1) @@ -27,12 +27,12 @@ Imports: scales (>= 1.1.1), broom (>= 0.7.1), plotly (>= 4.9.2), + Rtsne (>= 0.15), rmarkdown (>= 2.4), gprofiler2 (>= 0.2.0), - igraph (>= 1.2.5), - WGCNA (>= 1.69) + igraph (>= 1.2.5) biocViews: Annotation, Clustering, GeneSetEnrichment, GO, KEGG, MultipleComparison, Pathways, Reactome, Visualization, Sequencing, RNASeq, GeneExpression, Transcription, Normalization, DifferentialExpression, Bayesian, Regression, - PrincipalComponent, WGCNA + PrincipalComponent, gProfiler, DGE, dgeAnalysis diff --git a/NAMESPACE b/NAMESPACE index d654fa7..05b0280 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -31,13 +31,7 @@ export(ma_plot) export(multidimensionalScalingPlot) export(pValuePlot) export(pcaPlot) -export(plot_module_trait_relation_heat) -export(plot_modules) -export(plot_power) -export(plot_soft) -export(plot_trait_heat) export(plotlyGraph) -export(plotly_dendro_heat) export(plotly_dendrogram) export(readCountsFromTable) export(relevelSamples) diff --git a/man/get_dendrogram_data.Rd b/man/get_dendrogram_data.Rd index ef713f9..a48750d 100644 --- a/man/get_dendrogram_data.Rd +++ b/man/get_dendrogram_data.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/wcgna.R +% Please edit documentation in R/de.R \name{get_dendrogram_data} \alias{get_dendrogram_data} \title{Get heigth information from tree object. diff --git a/man/plot_module_trait_relation_heat.Rd b/man/plot_module_trait_relation_heat.Rd deleted file mode 100644 index 2e6360a..0000000 --- a/man/plot_module_trait_relation_heat.Rd +++ /dev/null @@ -1,21 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/wcgna.R -\name{plot_module_trait_relation_heat} -\alias{plot_module_trait_relation_heat} -\title{Create heatmap with trait data compared to module relations. -Show module name, correlation between trait and module, and the trait p-value} -\usage{ -plot_module_trait_relation_heat(moduleTraitCor, moduleTraitPvalue) -} -\arguments{ -\item{moduleTraitCor}{Dataframe, dataframe with all trait correlation values} - -\item{moduleTraitPvalue}{Dataframe, dataframe with all trait p-values} -} -\value{ -p, (Plotly object) plot -} -\description{ -Create heatmap with trait data compared to module relations. -Show module name, correlation between trait and module, and the trait p-value -} diff --git a/man/plot_modules.Rd b/man/plot_modules.Rd deleted file mode 100644 index 7ba7127..0000000 --- a/man/plot_modules.Rd +++ /dev/null @@ -1,21 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/wcgna.R -\name{plot_modules} -\alias{plot_modules} -\title{Barplot with module sizes. -Module sizes are defined in percentages and colors are added -Stacked barplot is returned} -\usage{ -plot_modules(color) -} -\arguments{ -\item{color}{Vector, all module colors of all used genes} -} -\value{ -p, (Plotly object) plot -} -\description{ -Barplot with module sizes. -Module sizes are defined in percentages and colors are added -Stacked barplot is returned -} diff --git a/man/plot_power.Rd b/man/plot_power.Rd deleted file mode 100644 index 85d4a58..0000000 --- a/man/plot_power.Rd +++ /dev/null @@ -1,21 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/wcgna.R -\name{plot_power} -\alias{plot_power} -\title{Create dot plot with power values (scale independence). -A R^2 cutoff line indicates the min. power levels.} -\usage{ -plot_power(soft, cutoff) -} -\arguments{ -\item{soft}{Dataframe, soft threshold values per power} - -\item{cutoff}{Integer, R^2 cutoff value} -} -\value{ -p, (Plotly object) plot -} -\description{ -Create dot plot with power values (scale independence). -A R^2 cutoff line indicates the min. power levels. -} diff --git a/man/plot_soft.Rd b/man/plot_soft.Rd deleted file mode 100644 index f9b78e4..0000000 --- a/man/plot_soft.Rd +++ /dev/null @@ -1,17 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/wcgna.R -\name{plot_soft} -\alias{plot_soft} -\title{Create dot plot with soft power values (mean connectivity).} -\usage{ -plot_soft(soft) -} -\arguments{ -\item{soft}{Dataframe, soft threshold values per power} -} -\value{ -p, (Plotly object) plot -} -\description{ -Create dot plot with soft power values (mean connectivity). -} diff --git a/man/plot_trait_heat.Rd b/man/plot_trait_heat.Rd deleted file mode 100644 index 9da924f..0000000 --- a/man/plot_trait_heat.Rd +++ /dev/null @@ -1,19 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/wcgna.R -\name{plot_trait_heat} -\alias{plot_trait_heat} -\title{Create heatmap with trait data. -Show sample, trait value (column) and the exact trait value} -\usage{ -plot_trait_heat(trait) -} -\arguments{ -\item{trait}{Dataframe, dataframe with all trait values} -} -\value{ -p, (Plotly object) plot -} -\description{ -Create heatmap with trait data. -Show sample, trait value (column) and the exact trait value -} diff --git a/man/plotly_dendro_heat.Rd b/man/plotly_dendro_heat.Rd deleted file mode 100644 index 67c3533..0000000 --- a/man/plotly_dendro_heat.Rd +++ /dev/null @@ -1,19 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/wcgna.R -\name{plotly_dendro_heat} -\alias{plotly_dendro_heat} -\title{Create heatmap from gene clusters. -Show genes vs genes colored by adjacency} -\usage{ -plotly_dendro_heat(data_TOM) -} -\arguments{ -\item{data_TOM}{WGCNA object, Topology Overlap Matrix} -} -\value{ -p, (Plotly object) plot -} -\description{ -Create heatmap from gene clusters. -Show genes vs genes colored by adjacency -} diff --git a/man/plotly_dendrogram.Rd b/man/plotly_dendrogram.Rd index 6e92faa..ee26983 100644 --- a/man/plotly_dendrogram.Rd +++ b/man/plotly_dendrogram.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/wcgna.R +% Please edit documentation in R/de_plots.R \name{plotly_dendrogram} \alias{plotly_dendrogram} \title{Get dendrogram data dendro_data(). From d377a95054564c1c43aa9e212c774780e3afd9b4 Mon Sep 17 00:00:00 2001 From: Tom Kuipers Date: Mon, 14 Jun 2021 15:46:52 +0200 Subject: [PATCH 31/31] Readme update --- README.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 4cc55bf..b5721fc 100644 --- a/README.md +++ b/README.md @@ -63,7 +63,7 @@ Pandoc:\ ## Fix install problems If there are problems installing the application, it might be worth to try installing specific packages manually: -``` +```R ## Shiny environment if (!require("shiny")) install.packages("shiny") if (!require("shinydashboard")) install.packages("shinydashboard") @@ -82,6 +82,7 @@ if (!require("tidyr")) install.packages("tidyr") if (!require("scales")) install.packages("scales") if (!require("broom")) install.packages("broom") if (!require("plotly")) install.packages("plotly") +if (!require("Rtsne")) install.packages("Rtsne") if (!require("rmarkdown")) install.packages("rmarkdown") ## Pathway analysis

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