ViDGER--直观地可视化差异基因表达结果

作图丫

发布于 2022-03-28 14:58:24

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导语

GUIDE ╲

进行差异基因表达 (DGE) 是 RNA 测序 (RNA-seq) 数据最常见的应用之一。

背景介绍

差异基因表达 (DGE) 是 RNA 测序 (RNA-seq) 数据最常见的应用之一。由于对RNA-seq数据进行分析的工具种类繁多以及得到的结果文件中提供了大量信息，导致对DGE 结果的解释可能不直观且耗时。在这里，小编给大家介绍一个 R 包 ViDGER，包含九个函数，这些函数可以进行信息丰富的可视化，用于解释来自三个广泛使用的差异表达工具 Cuffdiff、DESeq2 和 edgeR 的 DGE 结果。

R包安装

if (!requireNamespace("BiocManager", quietly=TRUE))
    install.packages("BiocManager")
BiocManager::install("vidger")
##新版 
if (!require("devtools")) install.packages("devtools")
devtools::install_github("btmonier/vidger", ref = "devel")

可视化展示

boxplot

使用 vsBoxPlot() 函数确定 FPKM 或 CPM 值的分布(修改type，可以选择Cuffdiff、DESeq2 和 edgeR方法)

vsBoxPlot(
    data = df.cuff, d.factor = NULL, type = 'cuffdiff', title = TRUE, 
    legend = TRUE, grid = TRUE
)

data("df.edger")
vsBoxPlot(
   data = df.edger, d.factor = NULL, type = "edger", title = TRUE,
   legend = TRUE, grid = TRUE, aes = "boxdot"
)

data("df.edger")
##画小提琴
vsBoxPlot(
   data = df.edger, d.factor = NULL, type = "edger", title = TRUE,
   legend = TRUE, grid = TRUE, aes = "viosumm"
)

data("df.edger")
##颜色设置
vsBoxPlot(
   data = df.edger, d.factor = NULL, type = "edger", title = TRUE,
   legend = TRUE, grid = TRUE, aes = "viosumm", fill.color = "Paired"
)

scatter plot

基本散点图函数 vsScatterPlot()

vsScatterPlot(
    x = 'hESC', y = 'iPS', data = df.cuff, type = 'cuffdiff',
    d.factor = NULL, title = TRUE, grid = TRUE
)

vsScatterMatrix()是vsScatterPlot() 函数的扩展

vsScatterMatrix(
    data = df.cuff, d.factor = NULL, type = 'cuffdiff', 
    comp = NULL, title = TRUE, grid = TRUE, man.title = NULL
)

差异基因表达矩阵

使用 vsDEGMatrix() 函数可视化每个实验处理水平的差异表达基因 (DEG) 的数量。

vsDEGMatrix(
    data = df.deseq, padj = 0.05, d.factor = 'condition', 
    type = 'deseq', title = TRUE, legend = TRUE, grid = TRUE
)

MA Plots

vsMAPlot() 根据基因表达值可视化两个样本之间的差异

vsMAPlot(
    x = 'treated_paired.end', y = 'untreated_paired.end', 
    data = df.deseq, d.factor = 'condition', type = 'deseq', 
    padj = 0.05, y.lim = NULL, lfc = NULL, title = TRUE, 
    legend = TRUE, grid = TRUE
)

 vsMAMatrix(
    data = df.cuff, d.factor = NULL, type = 'cuffdiff', 
    padj = 0.05, y.lim = NULL, lfc = 1, title = TRUE, 
    grid = TRUE, counts = TRUE, data.return = FALSE
)

volcano plots

使用 vsVolcano() 函数进行可视化,根据基因表达值可视化两个样本之间的方差，其中计算的 p 值（y 轴）的 -log10 是针对 log2 变化（x 轴）绘制的。

vsVolcano(
    x = 'iPS', y = 'hESC', data = df.cuff, d.factor = NULL, 
    type = 'cuffdiff', padj = 0.05, x.lim = NULL, lfc = NULL, 
    title = TRUE, legend = TRUE, grid = TRUE, data.return = FALSE
)

vsVolcanoMatrix(
    data = df.cuff, d.factor = NULL, type = 'cuffdiff', 
    padj = 0.05, x.lim = NULL, lfc = NULL, title = TRUE, 
    legend = TRUE, grid = TRUE, counts = TRUE
)

highlight数据点

data("df.cuff")
hl <- c(
  "XLOC_000033",
  "XLOC_000099",
  "XLOC_001414",
  "XLOC_001409"
)
vsScatterPlot(
    x = "hESC", y = "iPS", data = df.cuff, d.factor = NULL,
    type = "cuffdiff", title = TRUE, grid = TRUE, highlight = hl
)

hl <- c(
  "FBgn0036248",
  "FBgn0026573",
  "FBgn0259742",
  "FBgn0038961",
  "FBgn0038928"
)
vsVolcano(
    x = "treated_paired.end", y = "untreated_paired.end",
    data = df.deseq, d.factor = "condition",
    type = "deseq", padj = 0.05, x.lim = NULL, lfc = NULL,
    title = TRUE, grid = TRUE, data.return = FALSE, highlight = hl
)