使用ggplot2突出显示Scatterplot中的点

Question

我需要在ggplot2中创建-log10 p值的qq图，其中137个点的子集（＆＃34;目标＆＃34;）使用颜色友好的调色板以黄色突出显示我＆＃ 39; m使用被叫cbbPalette。我无法在备用软件包中执行此操作，因为我最终需要使用grid.arrange包中与gridExtra一起使用的ggplot2将多个qq图组合到一个网格中。

设定：

library(ggplot2)
library(reshape2)
cbbPalette <- c("#E69F00", "#000000") #part of my palette; gold & black
set.seed(100)

数据由100,137个p值组成，其中137个为目标：

p_values = c(
  runif(100000, min = 0, max = 1),
  runif(132, min = 1e-7, max = 1),
  c(6e-20, 6e-19, 7e-9, 7.5e-9, 4e-8)
)

#labels for the p-values
names_letters <-
  do.call(paste0, replicate(2, sample(LETTERS, 100137, TRUE), FALSE))
names = paste0(names_letters, sprintf("%04d", sample(9999, 100137, TRUE)))
targets = names[100001:100137] #last 137 are targets

df = as.data.frame(p_values)
df$names = names
df <-
  df[sample(nrow(df)), ] #shuffles the df to place targets randomly w/in
df$Category = ifelse(df$names %in% targets, "Target", "Non-Target")

数据外观：

head(df, 4) 
           p_values  names   Category
89863 0.4821147 NZ3385 Non-Target
20209 0.3998835 SQ3793 Non-Target
29200 0.7893478 ZT5497 Non-Target
71623 0.3459360 QF5311 Non-Target

融化df使用reshape2与观察（o）＆amp;预期（e）-log10 p值：

df.m = melt(df)
df.m$o = -log10(sort(df.m$value, decreasing = F))
df.m$e = -log10(1:nrow(df.m) / nrow(df.m))

熔化df的外观：

head(df.m,4)
   names   Category variable     value         o        e
1 NZ3385 Non-Target p_values 0.4821147 19.221849 5.000595
2 SQ3793 Non-Target p_values 0.3998835 18.221849 4.699565
3 ZT5497 Non-Target p_values 0.7893478  8.154902 4.523473
4 QF5311 Non-Target p_values 0.3459360  8.124939 4.398535

QQ-积

df_qq = ggplot(df.m, aes(e, o)) +
  geom_point(aes(color = Category)) +
  scale_colour_manual(values = cbbPalette) +
  geom_abline(intercept = 0, slope = 1) +
  ylab("Observed -log[10](p)") +
  xlab("Theoretical -log[10](p)")

然后我得到了一个没有突出显示我的137个目标的QQ。

Answer 1

你可以在非目标之后的单独library(shiny) library(ggplot2) library(dplyr) library(tidyverse) ## Only run examples in interactive R sessions if (interactive()) { ui <- fluidPage( # App title ---- titlePanel("Survey Data Analysis Template"), # Sidebar layout with input and output definitions ---- sidebarLayout( # Sidebar panel for inputs ---- sidebarPanel( # Input: Select a file ---- fileInput("file1", "Choose CSV File", multiple = TRUE, accept = c("text/csv", "text/comma-separated-values,text/plain", ".csv")), # Horizontal line ---- tags$hr(), # Input: Checkbox if file has header ---- checkboxInput("header", "Header", TRUE), # Input: Select separator ---- radioButtons("sep", "Separator", choices = c(Comma = ",", Semicolon = ";", Tab = "\t"), selected = ","), # Input: Select quotes ---- radioButtons("quote", "Quote", choices = c(None = "", "Double Quote" = '"', "Single Quote" = "'"), selected = '"'), # Horizontal line ---- tags$hr(), # Input: Select number of rows to display ---- radioButtons("disp", "Display", choices = c(Head = "head", All = "all"), selected = "head"), # Include a Slider for Strata sliderInput("strata", "strata:", min = 1, max = 20, value = c(1,20), step=1), # Select Variable from the selected Dataset selectInput("vari", "Variable", choices=colnames(df)), hr(), helpText("") ), ########################## # Main panel for displaying outputs ---- mainPanel( # Output: Data file ---- tableOutput("contents"), verbatimTextOutput("summary") # Generate Summary Statistics for the selected variable by strata ) ) ) server <- function(input, output, session) { mytable <- reactive({ req(input$file1) df <- read.csv(input$file1$datapath, header = input$header, sep = input$sep, quote = input$quote, stringsAsFactors = FALSE) print(df) df<-as.data.frame(df) # Subset the data to filter based on strata df<- df %>% filter(df$Strata>=input$strata[1] & df$Strata<=input$strata[2]) df<-as.data.frame(df) print(df) if(input$disp == "head") { return(head(df)) } else { return(df) } # Output by Strata Filter output$contents <- renderTable({ # Now do use (), since we are calling a value from a reactive. mytable() }) # Create Table of Summary Statistics from the selected Variable print(mytable) mytable<-as.data.frame(mytable) # Select based on the drop down variable mytable<- mytable %>% select(mytable$vari) # Generate a summary of the dataset output$summary <- renderPrint({ dataset <- mytable() summary(dataset) }) }) } # Run the app ---- shinyApp(ui, server) } 调用中绘制目标，按顺序绘制geom，使目标最终位于顶部：

geom_point()

我还在调色板中添加了名称，以确保在更改cbbPalette <- c(Target = "#E69F00", `Non-Target` = "#000000") df_qq = ggplot(df.m, aes(e, o)) + geom_abline(intercept = 0, slope = 1) + geom_point(aes(color = Category), data = df.m[df.m$Category == "Non-Target", ]) + geom_point(aes(color = Category), data = df.m[df.m$Category == "Target", ]) + scale_colour_manual(values = cbbPalette) + ylab("Observed -log[10](p)") + xlab("Theoretical -log[10](p)") 调用的顺序时将正确的颜色附加到每个类别，否则会混淆。

结果：

Answer 2

如果您希望避免将数据框拆分为两次geom_point调用，则可以先按类别列排序数据，然后将其输入ggplot。对于这两个类别值，您可以简单地安排：

df.m %>%
    arrange(Category) %>%
    ggplot(...)

将使用非目标观察按字母顺序排列数据，然后按目标观察。点数按顺序绘制，因此这将使得目标类别中的点位于顶部。

要更好地控制排序，可以将Category设为一个因子，并明确设置级别，然后按因子顺序排列：

df.m %>%
    mutate(Category = as.factor(Category) %>% fct_relevel("Target")) %>%
    arrange(desc(Category)) %>%
    ggplot(...)

我正在使用fct_relevel包中的forcats，因为这是操纵因子级别的一种非常简单的方法;你也可以用基数R订购等级。 fct_relevel将目标等级放在第一位，所以当我按类别排列时，我正在反向进行，以便最后再次绘制目标。

希望有意义！