Fanta sea, totally
shiny-seq 复现
https://www.rna-seqblog.com/shiny-seq-advanced-guided-transcriptome-analysis/
之前的 倒在boxplot前的失败经验
终于搞定了如何用 raw data (一个表达矩阵，一个有两种分组信息的annotation file) 画出 boxplot 和 PCA. 反正作者代码一环套一环是看不懂的 (:з)∠)
ui:
ui <- navbarPage(title = "ShinySeq - Data to Plots",
#### Raw Data ####
tabPanel("Raw Data",
fluidPage(theme = shinytheme("yeti"),
h3(tags$b("Raw Data")),
sidebarLayout(
sidebarPanel(
fileInput("file1","Choose expression file to upload",
multiple = FALSE,
accept = c('text/csv',
'text/comma-separated-values',
'text/tab-separated-values',
'text/plain',
'.csv')),
hr(),
fileInput("file2","Choose annotation file to upload",
multiple = FALSE,
accept = c('text/csv',
'text/comma-separated-values',
'text/tab-separated-values',
'text/plain',
'.csv')),
hr(),
actionButton("raw_plot_run", "RUN")
),
mainPanel(
tabsetPanel(type = "tabs",
tabPanel("Expression", DT::dataTableOutput("expressionfile")),
tabPanel("Annotaiton", DT::dataTableOutput("annotationfile")),
tabPanel("Reshaped", DT::dataTableOutput("reshapedtable")))
)
),
hr(),
h3(tags$b("Plots")),
navlistPanel(
tabPanel("Box Plot",
tabsetPanel(type = "pills",
tabPanel("Phenotype",plotOutput("raw_Phenobox")),
tabPanel("Donor",plotOutput("raw_Donorbox"))
)),
tabPanel("PCA",
tabsetPanel(type = "pills",
tabPanel("Phenotype",plotOutput("raw_PhenoPCA")),
tabPanel("Donor",plotOutput("raw_DonorPCA"))
)
)
)
)),
#### Normalized ####
tabPanel("Normalized",
fluidPage(theme = shinytheme("yeti"),
h3(tags$b("Normalized Data"))
))
)
server:
ps. 某两个嵌套for循环就已经耗尽了全部力气，但突然领悟了 reactiveValues 的真实方便
# ====== shinytools 1 - Transcriptome Data Pre‑processing =====
## server
library(ggplot2)
library(reshape2)
server <- function(input, output) {
output$expressionfile <- DT::renderDataTable({
if (is.null(input$file1))
return(NULL)
read.csv(input$file1$datapath, header = TRUE,sep = "\t")
})
output$annotationfile <- DT::renderDataTable({
if (is.null(input$file2))
return(NULL)
read.csv(input$file2$datapath, header = TRUE,sep = "\t")
})
###### boxplot #####
values <- reactiveValues(
exp=NULL,
ann=NULL,
expset=NULL,
pca_data=NULL,
pcx=NULL,
pcr=NULL)
observeEvent(input$raw_plot_run, {
values$exp <- read.csv(input$file1$datapath, header = TRUE,sep = "\t",row.names = 1)
values$ann <- read.csv(input$file2$datapath, header = TRUE,sep = "\t")
values$exp <- values$exp[,values$ann$SampleID]
values$expset <- melt(values$exp)
for (j in 1:length(values$ann$SampleID)) {
for (i in 1:length(unique(values$ann$Phenotype))) {
values$expset$Phenotype <- rep(as.character(unique(values$ann$Phenotype)),
each = as.numeric(table(values$expset$variable)[j])*as.numeric(table(values$ann$Phenotype)[i]))
}
}
for (j in 1:length(values$ann$SampleID)) {
for (i in 1:length(unique(values$ann$Donor))) {
values$expset$Donor <- rep(as.character(unique(values$ann$Donor)),
each = as.numeric(table(values$expset$variable)[j]),
time = as.numeric(table(values$ann$Donor)[i]))
}
}
output$reshapedtable <- DT::renderDataTable({
values$expset
})
output$raw_Phenobox <- renderPlot({
# waitress$start() # start the waitress
ggplot(values$expset, aes(x=variable,y=value,fill=Phenotype))+geom_boxplot(outlier.shape = NA)+
ylim(0,2000)+labs(x="Rows",y="Values")
# waitress$hide() # hide when done
})
output$raw_Donorbox <- renderPlot({
# waitress$start() # start the waitress
ggplot(values$expset, aes(x=variable,y=value,fill=Donor))+geom_boxplot(outlier.shape = NA)+
ylim(0,2000)+labs(x="Rows",y="Values")
# waitress$hide() # hide when done
})
values$pca_data <- prcomp(t(values$exp),scale=TRUE)
values$pcx <- data.frame(values$pca_data$x)
values$pcr_p <- cbind(samples=rownames(values$pcx),values$ann$Phenotype, values$pcx)
values$pcr_d <- cbind(samples=rownames(values$pcx),values$ann$Donor, values$pcx)
output$raw_PhenoPCA <- renderPlot({
ggplot(values$pcr_p, aes(PC1, PC2)) + geom_point(size=5, aes(color = values$ann$Phenotype))
})
output$raw_DonorPCA <- renderPlot({
ggplot(values$pcr_d, aes(PC1, PC2)) + geom_point(size=5, aes(color = values$ann$Donor))
})
})
}
发现一个看起来很高级的做过场渲染的包，waiter https://github.com/JohnCoene/waiter 试图使用失败
目前完成到了……这样👇
画不出和作者一样的图只是因为没设置ylim, 毕竟 raw data 就是 raw data, 动是动不起的
3D PCA hmmmm 画出来都得插会儿腰
最后，向大家隆重推荐生信技能树的一系列干货！
- 生信技能树全球公益巡讲：https://mp.weixin.qq.com/s/E9ykuIbc-2Ja9HOY0bn_6g
- B站公益74小时生信工程师教学视频合辑：https://mp.weixin.qq.com/s/IyFK7l_WBAiUgqQi8O7Hxw
- 招学徒：https://mp.weixin.qq.com/s/KgbilzXnFjbKKunuw7NVfw
