Merge pull request #306 from lishensuo/develop22

add shiny visualize params & update NEWS.md

NEWS.md

@@ -6,13 +6,47 @@ https://github.com/openbiox/UCSCXenaShiny/commit/147f4612943df940d3bbd409e82a492
 
 ### Datasets
 
+- `load_data("tcga_PW")`: ssGSEA scores of HALLMARK, KEGG, IOBR terms for TCGA samples.
+- `load_data("tcga_PW_meta")`: metadata annotation for HALLMARK, KEGG, IOBR terms.
+- `load_data("pcawg_TIL")`: PCAWG TIL data.
+- `load_data("pcawg_PW")`: ssGSEA scores of HALLMARK, KEGG, IOBR terms for PCAWG samples.
+
 ### R Package Functions
 
-- `vis_dim_dist()` function and corresponding shiny module.
+- `.opt_pancan` : Default setting for alternative TPC datasets.
+- `mol_quick_analysis()`: Quick molecule analysis and report generation based on TCGA dataset.
+- `query_tcga_group()`: Group TPC samples by build-in or custom phenotype and support filtering or merging operations.
+- `vis_dim_dist()`: Visualize the distribution difference of TCGA samples after dimension reduction analysis.
+- `vis_identifier_dim_dist()`: Visualize the distribution difference of samples after Molecule Identifier dimension reduction analysis.
+- `vis_toil_Mut()`: Visualize molecular profile difference between mutation and wild status of queried gene.
+- `vis_toil_Mut_cancer()`: Visualize molecular profile difference between mutation and wild status of queried gene in Single Cancer Type
 
 ### Shiny application
 
-- abc
+- Homepage
+  - Added slicker gallery to display page summary;
+  - Added report generation for TCGA pan-cancer exploration.
+
+- General Dataset  Analysis
+
+  - Added one general dimension reduction analysis module.
+
+- Quick TPC Analysis
+
+  - Added one module for association analysis between molecule and pathway;
+  - Added one module for association analysis between molecule and mutation;
+  - Added one module for dimension reduction analysis.
+
+- Personalized Analysis
+
+  - Designed personalized TPC analysis pipelines for based on 3 methods and 3 modes.
+
+- Download
+
+  - Added two modules for exact subset of integrated TPC data and UCSCXena datasets.
+
+
+
 
 ## Enhancements
 
@@ -26,6 +60,8 @@ UCSCXenaShiny::get_pancan_methylation_value(
 )
 ```
 
+- Supported alternative molecular profiling datasets for quick and personalized TPC analysis.
+
 ## Bug Fixes
 
 - Merged data with unequal size in pan-cancer data query with a gene signature (#283), the fix also enhance the sample names match.

R/load_data.R

@@ -9,9 +9,12 @@
 #' **Builtin datasets**:
 #'   - `ccle_absolute`: CCLE ABSOLUTE result.
 #'   - `ccle_info`: CCLE information.
+#'   - `ccle_info_fine`: cleaned CCLE information for TPC analysis.
 #'   - `pcawg_info`: PCAWG information.
+#'   - `pcawg_info_fine`: cleaned PCAWG information for TPC analysis.
 #'   - `pcawg_purity`: PCAWG tumor purity, ploidy and WGD data.
 #'   - `tcga_clinical`: TCGA clinical data.
+#'   - `tcga_clinical_fine`: cleaned TCGA information for TPC analysis.
 #'   - `tcga_genome_instability`: TCGA genome instability data.
 #'   - `tcga_gtex`: TCGA and GTEX sample info.
 #'   - `tcga_purity`: TCGA tumor purity data.
@@ -31,10 +34,15 @@
 #'   - `tcga_pan_immune_signature`: TCGA pan-cancer immune signature.
 #'   - `tcga_stemness`: TCGA tumor stemness data.
 #'   - `tcga_TIL`: TCGA TIL data.
+#'   - `tcga_PW`: ssGSEA scores of HALLMARK, KEGG, IOBR terms for TCGA samples.
+#'   - `tcga_PW_meta`: metadata annotation for HALLMARK, KEGG, IOBR terms.
 #'   - `tcga_tmb`: TCGA TMB data.
 #'   - `tcga_armcalls`: TCGA arm alteration calls and Aneuploidy data.
 #'   - `tcga_dna_repair`: TCGA DNA repair data.
 #'   - `pancancer_conserved_immune_subtype`: Pan-cancer conserved immune subtypes.
+#'   - `pcawg_TIL`: PCAWG TIL data.
+#'   - `pcawg_PW`: ssGSEA scores of HALLMARK, KEGG, IOBR terms for PCAWG samples.
+
 #'   
 #' @return a dataset, typically a `data.frame`.
 #' @export

R/query_general.R

@@ -355,7 +355,7 @@ query_general_value = function(L1, L2, L3, database = c("toil","pcawg","ccle"),
 
 
 
-#' Title quick molecule analysis and report generation
+#' Quick molecule analysis and report generation
 #'
 #' @inheritParams query_pancan_value
 #' @param out_dir path to save analysis result and report, default is '.'

R/query_tcga_group.R

@@ -1,4 +1,4 @@
-#' Group TCGA samples by build-in or custom phenotype and support filtering or merging operations
+#' Group TPC samples by build-in or custom phenotype and support filtering or merging operations
 #'
 #' @param cancer select cancer cohort(s)
 #' @param custom upload custom phenotype data

R/vis_dim_dist.R

@@ -1,7 +1,7 @@
 #' Visualize the distribution difference of samples after dimension reduction analysis
 #'
 #' @param ids molecular identifiers (>=3)
-#' @param data_type molecular types, refer to query_pancan_value() fuuntion
+#' @param data_type molecular types, refer to query_pancan_value() function
 #' @param return.data whether to reture the raw meta/matrix data (list) instead of plot
 #' @param DR_method the dimension reduction method
 #' @param palette the color setting of RColorBrewer

inst/shinyapp/modules/ccle/modules-ccle-comp-o2o.R

@@ -84,11 +84,32 @@ ui.modules_ccle_comp_o2o = function(id) {
 					h4(strong("S3.1 Set analysis parameters")), 
 					selectInput(ns("comp_method"), "Comparison method:",choices = c("t-test", "wilcoxon")),
 					h4(strong("S3.2 Set visualization parameters")), 
-					column(3, colourpicker::colourInput(inputId = ns("group_1_color"), "Color (Group 1):", "#E69F00")),
-					column(3, colourpicker::colourInput(inputId = ns("group_2_color"), "Color (Group 2):", "#56B4E9")),
-					column(3, numericInput(inputId = ns("point_size"), label = "Point size:", value = 3, step = 0.5)),
-					column(3, numericInput(inputId = ns("point_alpha"), label = "Point alpha:", value = 0.4, step = 0.1, min = 0, max = 1)),
-
+					fluidRow(
+						column(3, colourpicker::colourInput(inputId = ns("group_1_color"), "Color (Group 1):", "#E69F00")),
+						column(3, colourpicker::colourInput(inputId = ns("group_2_color"), "Color (Group 2):", "#56B4E9")),
+					),
+					dropMenu(
+						actionButton(ns("more_visu"), "Set more visualization params"),
+						div(h3("1. Adjust points:"),style="width:400px;"),
+						fluidRow(
+							column(3, numericInput(inputId = ns("point_size"), label = "Point size:", value = 3, step = 0.5)),
+							column(3, numericInput(inputId = ns("point_alpha"), label = "Point alpha:", value = 0.4, step = 0.1, min = 0, max = 1)),
+						),
+						div(h3("2. Adjust text size:"),style="width:400px;"),
+						fluidRow(
+							column(4, numericInput(inputId = ns("axis_size"), label = "Text size:", value = 18, step = 0.5)),
+							column(4, numericInput(inputId = ns("title_size"), label = "Title size:", value = 20, step = 0.5))
+						),				
+						div(h3("3. Adjust lab and title name:"),style="width:400px;"),
+						fluidRow(
+							column(4, textInput(inputId = ns("x_name"), label = "X-axis name:")),
+							column(4, textInput(inputId = ns("y_name"), label = "Y-axis name:")),
+							column(4, textInput(inputId = ns("title_name"), label = "Title name:"))
+						),	
+						div(h5("Note: You can download the raw data and plot in local R environment for more detailed adjustment.")),
+
+					),
+					br(),
 					# verbatimTextOutput(ns("tmp123")),
 					shinyWidgets::actionBttn(
 						ns("step3_plot_box"), "Run",
@@ -230,6 +251,11 @@ server.modules_ccle_comp_o2o = function(input, output, session) {
 		cancer_choose$single_cancer_ok = min(table(merge_data_box()$group))>=3
 	})
 
+	observe({
+		updateTextInput(session, "x_name", value = "group")
+		updateTextInput(session, "y_name", value = unique(y_axis_data()$id))
+		updateTextInput(session, "title_name", value = "CCLE")
+	})
 
 	comp_plot_box = eventReactive(input$step3_plot_box, {
 		shiny::validate(
@@ -247,9 +273,9 @@ server.modules_ccle_comp_o2o = function(input, output, session) {
 			  data  = merge_data_box,
 			  x     = "group",
 			  y     = "value",
-			  xlab = cancer_choose$group,
-			  ylab = unique(merge_data_box$id),
-			  title = unique(merge_data_box$cancer),
+			  xlab = isolate(input$x_name),
+			  ylab = isolate(input$y_name),
+			  title = isolate(input$title_name),
 			  bf.message = FALSE,
 			  type = comp_method,
 			  centrality.plotting = FALSE,
@@ -258,8 +284,8 @@ server.modules_ccle_comp_o2o = function(input, output, session) {
 			  	position = ggplot2::position_jitterdodge(dodge.width = 0.6), stroke = 0, na.rm = TRUE)
 			) + 
 			  ggplot2::scale_color_manual(values = c(isolate(input$group_1_color), isolate(input$group_2_color))) +
-			  theme(text = element_text(size=18),
-			        plot.title = element_text(size=20, hjust = 0.5),
+			  theme(text = element_text(size=isolate(input$axis_size)),
+			        plot.title = element_text(size=isolate(input$title_size), hjust = 0.5),
 			        plot.subtitle = element_text(size = 12))
 			return(p)
 		}

inst/shinyapp/modules/ccle/modules-ccle-cor-o2o.R

@@ -92,10 +92,27 @@ ui.modules_ccle_cor_o2o = function(id) {
 						column(3, colourpicker::colourInput(inputId = ns("x_hist_color"), "Hist color(x):", "#009E73")),
 						column(3, colourpicker::colourInput(inputId = ns("y_hist_color"), "Hist color(y):", "#D55E00"))
 					),
-					fluidRow(
-						column(3, numericInput(inputId = ns("point_size"), label = "Point size:", value = 3, step = 0.5)),
-						column(3, numericInput(inputId = ns("point_alpha"), label = "Point alpha:", value = 0.4, step = 0.1, min = 0, max = 1))
+					dropMenu(
+						actionButton(ns("more_visu"), "Set more visualization params"),
+						div(h3("1. Adjust points:"),style="width:400px;"),
+						fluidRow(
+							column(4, numericInput(inputId = ns("point_size"), label = "Point size:", value = 3, step = 0.5)),
+							column(3, numericInput(inputId = ns("point_alpha"), label = "Point alpha:", value = 0.4, step = 0.1, min = 0, max = 1))
+						),
+						div(h3("2. Adjust text size:"),style="width:400px;"),
+						fluidRow(
+							column(4, numericInput(inputId = ns("axis_size"), label = "Text size:", value = 18, step = 0.5)),
+							column(4, numericInput(inputId = ns("title_size"), label = "Title size:", value = 20, step = 0.5))
+						),				
+						div(h3("3. Adjust lab and title name:"),style="width:400px;"),
+						fluidRow(
+							column(4, textInput(inputId = ns("x_name"), label = "X-axis name:")),
+							column(4, textInput(inputId = ns("y_name"), label = "Y-axis name:")),
+							column(4, textInput(inputId = ns("title_name"), label = "Title name:"))
+						),	
+						div(h5("Note: You can download the raw data and plot in local R environment for more detailed adjustment.")),
 					),
+					br(),
 					shinyWidgets::actionBttn(
 						ns("step3_plot_sct"), "Run",
 				        style = "gradient",
@@ -226,6 +243,13 @@ server.modules_ccle_cor_o2o = function(input, output, session) {
 		data
 	})
 
+
+	observe({
+		updateTextInput(session, "x_name", value = unique(x_axis_data()$id))
+		updateTextInput(session, "y_name", value = unique(y_axis_data()$id))
+		updateTextInput(session, "title_name", value = "CCLE")
+	})
+
 	cor_plot_sct = eventReactive(input$step3_plot_sct, {
 		shiny::validate(
 			need(try(nrow(merge_data_sct())>0), 
@@ -240,18 +264,18 @@ server.modules_ccle_cor_o2o = function(input, output, session) {
 		  merge_data_sct,
 		  x = "x_value",
 		  y = "y_value",
-		  xlab = unique(merge_data_sct$x_id),
-		  ylab = unique(merge_data_sct$y_id),
-		  # title = unique(merge_data_sct$cancer),
+		  xlab = isolate(input$x_name),
+		  ylab = isolate(input$y_name),
+		  title = isolate(input$title_name),
 		  type = cor_method,
 		  point.args = list(size = isolate(input$point_size), alpha = isolate(input$point_alpha)),
 		  smooth.line.args = list(color = isolate(input$line_color),linewidth = 1.5,method = "lm",formula = y ~ x),
 		  xsidehistogram.args = list(fill = isolate(input$x_hist_color), color = "black", na.rm = TRUE),
 		  ysidehistogram.args = list(fill = isolate(input$y_hist_color), color = "black", na.rm = TRUE),
 		  bf.message = FALSE
 		) + 
-			theme(text = element_text(size=18),
-				  plot.title = element_text(size=20, hjust = 0.5))
+			theme(text = element_text(size=isolate(input$axis_size)),
+				  plot.title = element_text(size=isolate(input$title_size), hjust = 0.5))
 		return(p)
 	})
 	output$cor_plot_sct = renderPlot({cor_plot_sct()})

inst/shinyapp/modules/pcawg/modules-pcawg-comp-o2m.R

@@ -94,11 +94,32 @@ ui.modules_pcawg_comp_o2m = function(id) {
 					h4(strong("S3.1 Set analysis parameters")), 
 					selectInput(ns("comp_method"), "Comparison method:",choices = c("t-test", "wilcoxon")),
 					h4(strong("S3.2 Set visualization parameters")), 
-					column(3, colourpicker::colourInput(inputId = ns("group_1_color_2"), "Color (Group 1):", "#E69F00")),
-					column(3, colourpicker::colourInput(inputId = ns("group_2_color_2"), "Color (Group 2):", "#56B4E9")),
-					column(6, radioButtons(inputId = ns("significance"), label = "Significance:", 
-						choices = c("Value", "Symbol"), selected="Symbol",inline = TRUE)),
-
+					fluidRow(
+						column(3, colourpicker::colourInput(inputId = ns("group_1_color_2"), "Color (Group 1):", "#E69F00")),
+						column(3, colourpicker::colourInput(inputId = ns("group_2_color_2"), "Color (Group 2):", "#56B4E9")),
+					),
+					dropMenu(
+						actionButton(ns("more_visu"), "Set more visualization params"),
+						div(h3("1. Significance display:"),style="width:400px;"),
+						fluidRow(
+							column(6, radioButtons(inputId = ns("significance"), label = "Significance:", 
+								choices = c("Value", "Symbol"), selected="Symbol",inline = TRUE)),
+						),
+						div(h3("2. Adjust text size:"),style="width:400px;"),
+						fluidRow(
+							column(4, numericInput(inputId = ns("axis_size"), label = "Text size:", value = 18, step = 0.5)),
+							column(4, numericInput(inputId = ns("title_size"), label = "Title size:", value = 20, step = 0.5)),
+							column(4, numericInput(inputId = ns("label_size"), label = "Label size:", value = 5, step = 0.5)),
+						),				
+						div(h3("3. Adjust lab and title name:"),style="width:400px;"),
+						fluidRow(
+							column(4, textInput(inputId = ns("x_name"), label = "X-axis name:")),
+							column(4, textInput(inputId = ns("title_name"), label = "Title name:",
+								value = NULL))
+						),	
+						div(h5("Note: You can download the raw data and plot in local R environment for more detailed adjustment.")),
+					),
+					br(),
 					shinyWidgets::actionBttn(
 						ns("step3_plot_line"), "Run",
 				        style = "gradient",
@@ -272,16 +293,6 @@ server.modules_pcawg_comp_o2m = function(input, output, session) {
 		comp_method = switch(isolate(input$comp_method),
 			`t-test` = "parametric", wilcoxon = "nonparametric")
 		valid_cancer_choose = sort(cancer_choose$multi_cancer_ok)
-		# stat_comp = lapply(sort(valid_cancer_choose), function(tcga_type){
-		#   p = ggbetweenstats(
-		#     subset(merge_data_line, cancer==tcga_type),
-		#   	x = "group",
-		#   	y = "value",
-		#     type = comp_method)
-		#   extract_stats(p)$subtitle_data
-		# }) %>% do.call(rbind, .) %>% 
-		# dplyr::select(!expression) %>% 
-		# dplyr::mutate(cancer = sort(valid_cancer_choose), .before=1)
 		withProgress(message = "Please wait for a while.",{
 			stat_comp = lapply(seq(valid_cancer_choose), function(i){
 			  tcga_type = valid_cancer_choose[i]
@@ -299,7 +310,11 @@ server.modules_pcawg_comp_o2m = function(input, output, session) {
 		})
 	})
 
-
+	observe({
+		updateTextInput(session, "x_name", value = unique(y_axis_data()$id))
+		# updateTextInput(session, "y_name", value = unique(y_axis_data()$id))
+		updateTextInput(session, "title_name", value = "")
+	})
 	comp_plot_line = eventReactive(input$step3_plot_line, {
 		shiny::validate(
 			need(try(nrow(merge_data_line())>0), 
@@ -311,18 +326,21 @@ server.modules_pcawg_comp_o2m = function(input, output, session) {
 		p1 = ggplot(merge_data_line_sub) + 
 		  stat_summary(aes(x=cancer, y=value, color=group),
 		               position=position_dodge(width=0.5)) + 
-		  xlab("") + ylab("TP53") + 
+		  xlab("") + ylab(isolate(input$x_name)) + #转置
+		  ggtitle(label = isolate(input$title_name)) +
 		  ggplot2::scale_color_manual(values = c(isolate(input$group_1_color_2), isolate(input$group_2_color_2))) +
 		  coord_flip() +
 		  theme_minimal() +
 		  theme(legend.position = "top",
 		        plot.margin = margin(0,0,0,0),
-		        text = element_text(size=15))
+		  		text = element_text(size=isolate(input$axis_size)),
+				plot.title = element_text(size=isolate(input$title_size), hjust = 0.5)
+		        )
 
 		if(isolate(input$significance)=="Value"){
 			p2 = comp_data_line() %>% ggplot() + 
 			  geom_text(aes(label=formatC(p.value, format = "e", digits = 2),
-			                x=cancer,y=1),) +
+			                x=cancer,y=1), size = isolate(input$label_size)) +
 			  coord_flip()
 		} else if (isolate(input$significance)=="Symbol"){
 			p2 = comp_data_line() %>%
@@ -332,7 +350,7 @@ server.modules_pcawg_comp_o2m = function(input, output, session) {
 			    p.value < 0.05 ~ "*",
 			    TRUE ~ "ns"
 			  )) %>% ggplot() + 
-			  geom_text(aes(label=p.label, x=cancer,y=1),) +
+			  geom_text(aes(label=p.label, x=cancer,y=1), size = isolate(input$label_size)) +
 			  coord_flip()
 		}
 		p2 = p2 +
@@ -346,7 +364,7 @@ server.modules_pcawg_comp_o2m = function(input, output, session) {
 		  theme(panel.grid.major.x = element_blank(),
 		        panel.grid.minor.x = element_blank())
 
-		p = p1 + p2 + patchwork::plot_layout(widths = c(5,1))
+		p = p1 + p2 + patchwork::plot_layout(widths = c(5,0.3))
 
 		return(p)
 	})