Nothing
ui.modules_ccle_comp_o2o = function(id) {
ns = NS(id)
fluidPage(
fluidRow(
# 初始设置
column(
3,
wellPanel(
style = "height:1100px",
h2("S1: Preset", align = "center"),
h4(strong("S1.1 Modify datasets"),"[opt]") %>%
helper(type = "markdown", size = "l", fade = TRUE,
title = "Modify datasets",
content = "data_origin"),
mol_origin_UI(ns("mol_origin2comp"), database = "ccle"),
h4(strong("S1.2 Choose sites")),
pickerInput(
ns("choose_cancer"),NULL,
choices = sort(unique(ccle_info_fine$Site_Primary)),
multiple = TRUE,
selected = sort(unique(ccle_info_fine$Site_Primary)),
options = list(`actions-box` = TRUE)
),
br(),
h4(strong("S1.3 Filter samples"),"[opt]") %>%
helper(type = "markdown", size = "l", fade = TRUE,
title = "Filter samples",
content = "choose_samples"),
h5("Exact filter:"),
filter_samples_UI(ns("filter_samples2comp"), database = "ccle"),
br(),
verbatimTextOutput(ns("filter_phe_id_info")),
br(),
h4(strong("S1.4 Upload metadata"),"[opt]") %>%
helper(type = "markdown", size = "l", fade = TRUE,
title = "Upload metadata",
content = "custom_metadata"),
shinyFeedback::useShinyFeedback(),
custom_meta_UI(ns("custom_meta2comp")),
br(),
h4(strong("S1.5 Add signature"),"[opt]") %>%
helper(type = "markdown", size = "l", fade = TRUE,
title = "Add signature",
content = "add_signature"),
add_signature_UI(ns("add_signature2comp"), database = "ccle"),
)
),
# 分组设置
column(
4,
wellPanel(
style = "height:1100px",
h2("S2: Get data", align = "center"),
# 调用分组模块UI
h4(strong("S2.1 Divide 2 groups by one condition")) %>%
helper(type = "markdown", size = "l", fade = TRUE,
title = "Divide 2 groups",
content = "set_groups"),
group_samples_UI(ns("group_samples2comp"),database = "ccle"),
# 下载待比较数据
h4(strong("S2.2 Get data for comparison")) %>%
helper(type = "markdown", size = "l", fade = TRUE,
title = "Get one data",
content = "get_one_data"),
download_feat_UI(ns("download_y_axis"),
button_name="Query",database = "ccle")
)
),
# 分析/绘图/下载
column(
5,
wellPanel(
h2("S3: Analyze & Visualize", align = "center") %>%
helper(type = "markdown", size = "l", fade = TRUE,
title = "Analyze & Visualize",
content = "analyze_comp_1"),
style = "height:1100px",
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")),
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(
actionBttn(ns("more_visu"), label = "Other options", style = "bordered",color = "success",icon = icon("bars")),
div(h3("1. Select ggplot theme:"),style="width:400px;"),
fluidRow(
column(6,
selectInput(inputId = ns("theme"), label = NULL,
choices = names(themes_list), selected = "ggstatplot")
)
),
div(h3("2. 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("3. 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("4. 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",
style = "gradient",
icon = icon("chart-line"),
color = "primary",
block = TRUE,
size = "sm"
),
br(),
fluidRow(
column(10, offset = 1,
plotOutput({ns("comp_plot_box")}, height = "500px")
)
),
br(),
h4(strong("S3.3 Download results")),
download_res_UI(ns("download_res2comp"))
)
)
)
)
}
server.modules_ccle_comp_o2o = function(input, output, session) {
ns <- session$ns
# 记录选择癌症
cancer_choose <- reactiveValues(name = "lung", phe_primary="",
filter_phe_id=query_tcga_group(database = "ccle", cancer = "lung", return_all = T))
observe({
cancer_choose$name = input$choose_cancer
cancer_choose$phe_primary <- query_tcga_group(database = "ccle",
cancer = cancer_choose$name, return_all = T)
})
# 数据源设置
opt_pancan = callModule(mol_origin_Server, "mol_origin2comp", database = "ccle")
# 自定义上传metadata数据
custom_meta = callModule(custom_meta_Server, "custom_meta2comp", database = "ccle")
# signature
sig_dat = callModule(add_signature_Server, "add_signature2comp", database = "ccle")
custom_meta_sig = reactive({
if(is.null(custom_meta())){
return(sig_dat())
} else {
if(is.null(sig_dat())){
return(custom_meta())
} else {
custom_meta_sig = dplyr::inner_join(custom_meta(),sig_dat())
return(custom_meta_sig)
}
}
})
## 过滤样本
# exact filter module
filter_phe_id = callModule(filter_samples_Server, "filter_samples2comp",
database = "ccle",
cancers=reactive(cancer_choose$name),
custom_metadata=reactive(custom_meta_sig()),
opt_pancan = reactive(opt_pancan()))
observe({
# exact filter
if(is.null(filter_phe_id())){
cancer_choose$filter_phe_id = cancer_choose$phe_primary$Sample
} else {
cancer_choose$filter_phe_id = filter_phe_id()
}
output$filter_phe_id_info = renderPrint({
cat(paste0("Tip: ", length(cancer_choose$filter_phe_id), " samples are retained"))
})
})
# 设置分组
group_final = callModule(group_samples_Server, "group_samples2comp",
database = "ccle",
cancers=reactive(cancer_choose$name),
samples=reactive(cancer_choose$filter_phe_id),
custom_metadata=reactive(custom_meta_sig()),
opt_pancan = reactive(opt_pancan())
)
# 下载待比较数据
y_axis_data = callModule(download_feat_Server, "download_y_axis",
database = "ccle",
samples=reactive(cancer_choose$filter_phe_id),
custom_metadata=reactive(custom_meta_sig()),
opt_pancan = reactive(opt_pancan()),
check_numeric=TRUE,
table.ui = FALSE
)
# 合并分析
# boxviolin逻辑:先绘图,再提取相关性结果
merge_data_box = eventReactive(input$step3_plot_box, {
group_data = group_final()[,c(1,3,4)]
colnames(group_data) = c("Sample","group","phenotype")
y_axis_data = y_axis_data()
data = dplyr::inner_join(y_axis_data, group_data) %>%
dplyr::select(cancer, Sample, value, group, everything())
data
})
# output$tmp123 = renderPrint({head(merge_data_box())})
# 检查数据
observe({
cancer_choose$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$step3_plot_box, {
shiny::validate(
need(try(nrow(merge_data_box())>0),
"Please inspect whether to set groups or download variable data in S2 or S3 step."),
)
merge_data_box = merge_data_box()
if(!cancer_choose$single_cancer_ok){
return("No enough samples for comparing, please check your input.")
} else {
p = plot_comb_o2o(
data = merge_data_box(), xlab=input$x_name, ylab=input$y_name, title=input$title_name,
comp_method=input$comp_method, point_size=input$point_size, point_alpha=input$point_alpha,
group_1_color=input$group_1_color, group_2_color=input$group_2_color,
axis_size=input$axis_size, title_size=input$title_size,
custom_theme=themes_list[[input$theme]]
)
return(p)
}
})
output$comp_plot_box = renderPlot({comp_plot_box()})
# Download results
observeEvent(input$step3_plot_box,{
res1 = comp_plot_box()
res2 = merge_data_box()
p_comp = extract_stats(comp_plot_box())$subtitle_data
p_comp = p_comp[-which(colnames(p_comp)=="expression")]
p_comp$identifier = unique(merge_data_box()$id)
p_comp$phenotype = unique(merge_data_box()$phenotype)
p_comp$group_1 = levels(merge_data_box()$group)[1]
p_comp$group_2 = levels(merge_data_box()$group)[2]
res3 = p_comp
callModule(download_res_Server, "download_res2comp", res1, res2, res3)
})
}
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