🤣 easylabel | 完美拯救手残党不会标注突出重点！（Label！~ Label！~）

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发布于 2023-02-24 14:18:08

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发布于 2023-02-24 14:18:08

1写在前面

我们在画图的时候经常需要标记某个值, 如散点图中的某个具体的点, 火山图中的某个基因, 但对于代码不太熟悉的小白来说, 还是有一定难度的.🤪 本期和大家介绍一个基于shiny轻松进行label的包, 即easylabel包, 轻松实现交互式label, 麻麻再也不用担心你的画图标记啦.😗

2用到的包

rm(list = ls())
# devtools::install_github("myles-lewis/easylabel")
library(easylabel)
library(tidyverse)
library(qvalue)
library(AnnotationDbi)
library(org.Hs.eg.db)
library(ggstatsplot)
library(plotly)

3散点图

3.1 初步绘图

我们先简单画个散点图, 试着标记一下吧。

这里是可以交互的，大家直接点一下就标记了哦。🤩

dat <- movies_long

dat %>%
  easylabel(x = 'length', 
            y = 'rating',
            labs = 'title',
            colScheme = 'royalblue')

3.2 简单导出文件

接着在底部可以找到输出按钮，点击后导出你需要的格式吧~😏

3.3 导出为ploty对象

我们也可以通过设置output_shiny = F, 不激活shiny而直接导出为ploty对象, 依然是可交互的哦, 请随意移动。🤓

p1 <- easylabel(dat, 
                x = 'length', y = 'rating', col = 'genre', 
                startLabels = dat$rating[dat$year == 1994], 
                output_shiny = F) %>% 
  layout(yaxis = list(zeroline = F))

p2 <- easylabel(dat, 
                x = 'length', y = 'votes', col = 'genre', 
                startLabels = dat$votes[dat$year == 1994], 
                output_shiny = F) %>% 
  layout(yaxis = list(zeroline = F))

plotly::subplot(p1, p2, nrows = 2, shareY = T, titleX = T, margin = 0.05)

4美化细节

接着我们做一些细节的美化, 支持colour, shape, size等设置。😉

4.1 colour

easylabel(dat, 
          x = 'length', y = 'budget',
          col = 'genre', alpha = 0.6,
          output_shiny = F,
          labs = 'title',
          main = 'colour')%>% 
  layout(yaxis = list(zeroline = F))

4.2 shape

  dat[1:100,] %>% 
  easylabel(
          x = 'length', y = 'rating', 
          col = 'genre', alpha = 0.6,
          shape = 'mpaa', shapeScheme = 21,
          output_shiny = F,
          labs = 'title',
          main = 'shape') %>% 
  layout(yaxis = list(zeroline = F))

4.3 size

  dat[1:100,] %>% 
  easylabel(
          x = 'length', y = 'rating', 
          col = 'genre', alpha = 0.6,
          shape = 'mpaa', shapeScheme = 21,
          size = 'budget',
          output_shiny = F,
          labs = 'title',
          main = 'size') %>% 
  layout(yaxis = list(zeroline = F))

4.4 轴标题

  dat[1:100,] %>% 
  easylabel(
          x = 'length', y = 'rating', 
          col = 'genre', alpha = 0.6,
          shape = 'mpaa', shapeScheme = 21,
          size = 'budget',
          output_shiny = F,
          labs = 'title',
          main = 'axis title',
          xlab = 'lenght minutes',
          ylab = 'rating score',
          showgrid = T) %>% 
  layout(yaxis = list(zeroline = F))

5火山图

5.1 用到的数据

这里我们需要用到easyVolcano()函数, 输入文件为DESeq2, limma或EdgeR计算的差异基因结果。😂 这里我准备另一个我之前计算的limma结果。🥰

all_diff <- read.csv("./alldiff_paired.csv",row.names = 1)
DT::datatable(all_diff)

5.2 初步绘图

easyVolcano会使用自动识别DESeq2, limma或EdgeR计算的差异基因结果, 但默认是使用FDR。🫠

easyVolcano(all_diff,useQ = T,
            output_shiny = F,
            startLabels = rownames(all_diff)[all_diff$adj.P.Val < 0.05])

5.3 调整输入数据

手动选择x, y轴。😏

easyVolcano(all_diff, 
            useQ = F,
            x = 'logFC',
            y = 'P.Value',
            startLabels = rownames(all_diff)[all_diff$adj.P.Val < 0.05],
            output_shiny = F)

5.4 MA plot

需要用到easyMAplot函数, 也是非常容易上手.😗

easyMAplot(all_diff, useQ = T,
           startLabels = rownames(all_diff)[all_diff$adj.P.Val < 0.05],
           output_shiny = F)

5.5 展示基因全名

有时候我们不光想展示Gene Symbol, 还想展示它的全名, 但是手动检索会非常麻烦, easyVolcano可以完美地帮你解决这个问题.🤩

easyVolcano(all_diff, useQ = T, 
            fullGeneNames = T,
            output_shiny = F
            )

5.6 左右底角添加小标题

library(RColorBrewer)

colScheme <- c('darkgrey', brewer.pal(9, 'RdYlBu')[c(9:7, 3:1)])

easyVolcano(all_diff,
            useQ = F, fullGeneNames = T,
            Ltitle = expression(symbol("\254") ~ "Left"),
            Rtitle = expression("Right" ~ symbol("\256")),
            LRtitle_side = 1, # LRtitle_side = 1 bottom ; LRtitle_side = 3 top;
            cex.lab = 0.9, cex.axis = 0.8,
            fccut = c(1, 2), fdrcutoff = 0.2,
            ylim = c(0, 6), xlim = c(-5, 5),
            colScheme = colScheme,
            startLabels = rownames(all_diff)[all_diff$adj.P.Val < 0.05],
            output_shiny = F
            )

5.7 控制label方向

我们在这里示范一下水平方向和垂直方向.

p_horiz <- easyVolcano(all_diff,
            useQ = F, fullGeneNames = T,
            Ltitle = expression(symbol("\254") ~ "Left"),
            Rtitle = expression("Right" ~ symbol("\256")),
            LRtitle_side = 1, # LRtitle_side = 1 bottom ; LRtitle_side = 3 top;
            cex.lab = 0.9, cex.axis = 0.8,
            fccut = c(1, 2), fdrcutoff = 0.2,
            ylim = c(0, 6), xlim = c(-5, 5),
            colScheme = colScheme,
            labelDir = "horiz",
            startLabels = rownames(all_diff)[all_diff$adj.P.Val < 0.05],
            output_shiny = F
            )

p_vert <- easyVolcano(all_diff,
            useQ = F, fullGeneNames = T,
            Ltitle = expression(symbol("\254") ~ "Left"),
            Rtitle = expression("Right" ~ symbol("\256")),
            LRtitle_side = 1, # LRtitle_side = 1 bottom ; LRtitle_side = 3 top;
            cex.lab = 0.9, cex.axis = 0.8,
            fccut = c(1, 2), fdrcutoff = 0.2,
            ylim = c(0, 6), xlim = c(-5, 5),
            colScheme = colScheme,
            labelDir = "vert",
            startLabels = rownames(all_diff)[all_diff$adj.P.Val < 0.05],
            output_shiny = F
            )


plotly::subplot(p_horiz, p_vert, nrows = 2, shareY = T, titleX = T, margin = 0.05)

5.8 统一label和point的颜色

我们经常会遇到这种问题, 希望label和point统一颜色, 这样会更加美观。🤒 这也提供了相应的解决办法, 设置text_col = "match"以及 line_col = "match"即可.🥳

easyVolcano(all_diff,
            useQ = F, fullGeneNames = T,
            Ltitle = expression(symbol("\254") ~ "Left"),
            Rtitle = expression("Right" ~ symbol("\256")),
            LRtitle_side = 1, # LRtitle_side = 1 bottom ; LRtitle_side = 3 top;
            cex.lab = 0.9, cex.axis = 0.8,
            fccut = c(1, 2), fdrcutoff = 0.2,
            ylim = c(0, 6), xlim = c(-5, 5),
            colScheme = colScheme,
            startLabels = rownames(all_diff)[all_diff$P.Value < 0.01],
            line_col = "match", text_col = "match",
            rectangles = T, border_col = NA,
            rect_col = "match", border_radius = 20, padding = 5,
            output_shiny = F
            )

6曼哈顿图

这里我们也让point和label的颜色统一起来吧，颜值提升一下。😘

library(CMplot)

data("cattle50K")

chromCols <- RColorBrewer::brewer.pal(6, 'Paired')

easyManhattan(cattle50K, 
              chrom = "chr",
              pos = "pos", 
              p = "Somatic cell score", 
              chromCols = chromCols,
              ylab = "Somatic cell score",
              output_shiny = F,
              labs = "SNP",
              npeaks = 10, ## 标注peak前10
              labelDir = "vert",
              line_col = "match", text_col = "match",
              rectangles = T, border_col = NA,
              rect_col = "match", border_radius = 20, padding = 5,
              )