Nothing
ui.modules_pcawg_sur_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_origin2sur"), database = "pcawg"),
h4(strong("S1.2 Choose project")) %>%
helper(type = "markdown", size = "l", fade = TRUE,
title = "PCAWG projects",
content = "pcawg_projects"),
pickerInput(
ns("choose_cancer"),NULL,
choices = pcawg_names),
br(),
h4(strong("S1.3 Filter samples"),"[opt]") %>%
helper(type = "markdown", size = "l", fade = TRUE,
title = "Filter samples",
content = "choose_samples"),
h5("Quick filter:"),
pickerInput(
ns("filter_by_code"), NULL,
choices = NULL, selected = NULL,
multiple = TRUE, options = list(`actions-box` = TRUE)
),
h5("Exact filter:"),
filter_samples_UI(ns("filter_samples2sur"), database = "pcawg"),
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_meta2sur")),
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_signature2sur"), database = "pcawg")
)
),
column(
4,
wellPanel(
style = "height:1100px",
h2("S2: Get data", align = "center"),
h4(strong("S2.1 Select survival endpoint")),
p("Only OS (Overall Survial) is supported."),
br(),br(),
h4(strong("S2.2 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_samples2sur"),database = "pcawg")
)
),
# 分析/可视化/下载
column(
5,
wellPanel(
style = "height:1100px",
h2("S3: Analyze & Visualize", align = "center") %>%
helper(type = "markdown", size = "l", fade = TRUE,
title = "Analyze & Visualize",
content = "analyze_sur_1"),
h4(strong("S3.1 Set analysis parameters")),
selectInput(ns("sur_method"), "Survival method:",
choices = c("Log-rank test", "Univariate Cox regression")),
materialSwitch(ns("use_origin"),
"Whether use initial data before grouping?"),
# %>%
# helper(type = "markdown", size = "m", fade = TRUE,
# title = "About the initial phenotype",
# content = "sur_initial_group"),
h4(strong("S3.2 Set visualization parameters")),
uiOutput(ns("one_params.ui")),
dropMenu(
actionBttn(ns("more_visu"), label = "Other options", style = "bordered",color = "success",icon = icon("bars")),
div(h3(strong("Params for Log-rank test")),style="width:500px;"),
div(h4("1. Wheather to dislpay risk.table:"),style="width:500px;"),
fluidRow(
column(6, radioButtons(inputId = ns("plot_table"), label = NULL,
choices = c("NO", "YES"), selected="NO",inline = TRUE)),
),
div(h4("2. Wheather to dislpay ncensor.plot:"),style="width:500px;"),
fluidRow(
column(6, radioButtons(inputId = ns("plot_ncensor"), label = NULL,
choices = c("NO", "YES"), selected="NO",inline = TRUE)),
),
div(h4("3. Wheather to dislpay confidence interval:"),style="width:500px;"),
fluidRow(
column(12, radioButtons(inputId = ns("plot_CI"), label = NULL,
choices = c("NO", "YES(ribbon)", "YES(step)"), selected="NO",inline = TRUE)),
),
div(h4("2. Adjust text size:"),style="width:400px;"),
fluidRow(
column(4, numericInput(inputId = ns("axis_size"), label = "Text size:", value = 14, step = 0.5)),
column(4, numericInput(inputId = ns("title_size"), label = "Title size:", value = 18, step = 0.5))
),
div(h4("4. Adjust lab and title name:"),style="width:500px;"),
fluidRow(
column(4, textInput(inputId = ns("x_name"), label = "X-axis name:")),
column(4, textInput(inputId = ns("title_name"), label = "Title name:"))
),
div(h3(strong("Params for Cox regression")),style="width:500px;"),
div(h4("1. Adjust text size:"),style="width:400px;"),
fluidRow(
column(4, numericInput(inputId = ns("axis_size_2"), label = "Font size:", value = 0.7, step = 0.1)),
),
div(h4("2. Adjust lab and title name:"),style="width:500px;"),
fluidRow(
column(4, textInput(inputId = ns("title_name_2"), label = "Title name:", value = "Hazard ratio"))
),
div(h5("Note: You can download the raw data and plot in local R environment for more detailed adjustment.")),
),
br(),
shinyWidgets::actionBttn(
ns("sur_analysis_bt_single"), "Run",
style = "gradient",
icon = icon("chart-line"),
color = "primary",
block = TRUE,
size = "sm"
),
br(),
fluidRow(
column(10, offset = 1,
plotOutput({ns("sur_plot_one")}, height = "500px")
)
),
br(),
h4(strong("S3.3 Download results")),
download_res_UI(ns("download_res2sur"))
)
)
)
)
}
server.modules_pcawg_sur_o2o = function(input, output, session) {
ns <- session$ns
# 记录选择癌症
cancer_choose <- reactiveValues(name = "BLCA-US", phe_primary="",
filter_phe_id=query_tcga_group(database = "pcawg", cancer = "BLCA-US", return_all = T))
observe({
cancer_choose$name = input$choose_cancer
cancer_choose$phe_primary <- query_tcga_group(database = "pcawg",
cancer = cancer_choose$name, return_all = T)
})
# 数据源设置
opt_pancan = callModule(mol_origin_Server, "mol_origin2sur", database = "pcawg")
# 自定义上传metadata数据
custom_meta = callModule(custom_meta_Server, "custom_meta2sur", database = "pcawg")
# signature
sig_dat = callModule(add_signature_Server, "add_signature2sur", database = "pcawg")
custom_meta_sig = reactive({
if(is.null(custom_meta())){
return(sig_dat())
} else {
if(is.null(sig_dat())){
return(custom_meta())
} else {
custom_meta_sig = dplyr::inner_join(custom_meta(),sig_dat())
return(custom_meta_sig)
}
}
})
## 过滤样本
# quick filter widget
observe({
code_types_valid = unique(cancer_choose$phe_primary$Type)
updatePickerInput(
session,
"filter_by_code",
choices = code_types_valid,
selected = code_types_valid
)
})
# exact filter module
filter_phe_id = callModule(filter_samples_Server, "filter_samples2sur",
database = "pcawg",
cancers=reactive(cancer_choose$name),
custom_metadata=reactive(custom_meta_sig()),
opt_pancan = reactive(opt_pancan()))
# 综合上述二者
observe({
# quick filter
filter_phe_id2 = cancer_choose$phe_primary %>%
dplyr::filter(Type %in% input$filter_by_code) %>%
dplyr::pull("Sample")
# exact filter
if(is.null(filter_phe_id())){
cancer_choose$filter_phe_id = filter_phe_id2
} else {
cancer_choose$filter_phe_id = intersect(filter_phe_id2,filter_phe_id())
}
output$filter_phe_id_info = renderPrint({
cat(paste0("Tip: ", length(cancer_choose$filter_phe_id), " samples are retained"))
})
})
# 生存资料
sur_dat_v1 = reactive({
sur_dat_raw = pcawg_info[,c("dcc_project_code","icgc_specimen_id","OS","OS.time")]
colnames(sur_dat_raw) = c("cancer","Sample","status","time")
sur_dat_sub = sur_dat_raw %>%
dplyr::distinct() %>% na.omit() %>%
dplyr::filter(Sample %in% cancer_choose$filter_phe_id)
sur_dat_sub
})
# 设置分组
group_final = callModule(group_samples_Server, "group_samples2sur",
database = "pcawg",
cancers=reactive(cancer_choose$name),
samples=reactive(sur_dat_v1()$Sample),
custom_metadata=reactive(custom_meta_sig()),
opt_pancan = reactive(opt_pancan())
)
sur_res_one = reactiveValues(sur_dat = NULL, cutoff=NULL, sur_res = NULL)
group_sur_final = reactive({
dat = dplyr::inner_join(group_final(),sur_dat_v1()[,-1],by=c("Sample"="Sample"))
## 验证是否只有一组分组的癌症
dat = dat %>%
dplyr::filter(Cancer %in% sort(unique(dat$Cancer))[
apply(table(dat$Cancer,dat$group),1,function(x) {min(x)>=2})])
dat
})
output$one_params.ui = renderUI(
if(input$sur_method=="Log-rank test"){
fluidRow(
column(4,colourpicker::colourInput(ns("one_log_color1"), "Color (Group 1):", "#E7B800")),
column(4,colourpicker::colourInput(ns("one_log_color2"), "Color (Group 2):", "#2E9FDF")),
)
} else if(input$sur_method=="Univariate Cox regression") {
fluidRow(
column(4,numericInput(ns("text_c1"), "Position of text col-1", 0.02, step = 0.01)),
column(4,numericInput(ns("text_c2"), "text col-2", 0.22, step = 0.01)),
column(4,numericInput(ns("text_c3"), "text col-3", 0.4, step = 0.01))
)
}
)
# 生存分析的输入(供绘图)与输出结果(供下载)
observeEvent(input$sur_analysis_bt_single, {
sur_res_one$sur_dat = group_sur_final()
if(input$sur_method=="Log-rank test"){
if(!input$use_origin){ #是否使用分组前的原始值
sur_res_one$sur_dat$Group = sur_res_one$sur_dat$group
} else {
if(class(group_sur_final()$origin) != "character"){ #若原始值为数值型,则寻找最佳阈值
res.cut <- surv_cutpoint(sur_res_one$sur_dat, time = "time", event = "status", variables = "origin")
groups_1_2 = sur_res_one$sur_dat %>%
dplyr::group_by(group) %>%
dplyr::summarise(mean = mean(origin)) %>%
dplyr::arrange(mean) %>%
dplyr::pull(group) %>% as.character()
sur_res_one$sur_dat$Group = ifelse(surv_categorize(res.cut)$origin=="low", groups_1_2[1], groups_1_2[2])
sur_res_one$sur_dat$Group = factor(sur_res_one$sur_dat$Group, levels=groups_1_2)
} else {
sur_res_one$sur_dat$Group = sur_res_one$sur_dat$group
}
}
# print(head(sur_res_one$sur_dat))
surv_diff <- survdiff(Surv(time, status) ~ Group, data = sur_res_one$sur_dat)
pval = 1 - pchisq(surv_diff$chisq, length(surv_diff$n) - 1)
sur_res_one$sur_res = summary(survfit(Surv(time, status) ~ Group, data = sur_res_one$sur_dat))$table %>%
as.data.frame() %>% tibble::rownames_to_column("Group") %>%
dplyr::mutate(Cancer = cancer_choose$name, .before = 1) %>%
dplyr::mutate(p.value = pval)
} else if (input$sur_method=="Univariate Cox regression"){
if(!input$use_origin){
sur_res_one$sur_dat$Group = sur_res_one$sur_dat$group
} else {
sur_res_one$sur_dat$Group = sur_res_one$sur_dat$origin
}
fit <- coxph(Surv(time, status) ~ Group, data = sur_res_one$sur_dat)
# sur_res_one$pval = summary(fit)$coefficients[1,5]
sur_res_one$sur_res = summary(fit)$coefficients %>% as.data.frame()
}
})
observe({
updateTextInput(session, "x_name", value = paste(input$endpoint_type, "(days)"))
updateTextInput(session, "title_name", value = NULL)
})
sur_plot_one = eventReactive(input$sur_analysis_bt_single,{
shiny::validate(
need(try(nrow(sur_res_one$sur_dat)>0),
"Please inspect whether to set valid groups in S3 step."),
)
p = plot_sur_o20(
sur_res_one$sur_dat, plot_CI=input$plot_CI, plot_table=input$plot_table,
plot_ncensor=input$plot_ncensor, sur_method=input$sur_method,
one_log_color1=input$one_log_color1, one_log_color2=input$one_log_color2,
axis_size=input$axis_size, x_name=input$x_name,
title_name=input$title_name, title_size=input$title_size,
text_c1=input$text_c1, text_c2=input$text_c2, text_c3=input$text_c3,
axis_size_2=input$axis_size_2, title_name_2=input$title_name_2)
p
})
output$sur_plot_one = renderPlot({sur_plot_one()})
# Download results
observeEvent(input$sur_analysis_bt_single,{
res1 = sur_plot_one()
res2 = sur_res_one$sur_dat
res3 = sur_res_one$sur_res
callModule(download_res_Server, "download_res2sur", res1, res2, res3)
})
}
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