Plotting Answer 3

These exercises cover the sections on Plotting in R PlottingInR.

Section 1 - volcano plot

• Use the example from the course ("data/DiffEx.csv"), and

  • use padj < 0.01 and abs(log2FC) > 1 as thresholds to define DE genes

  • change the colour codes for the volcano plot to c("Up.DE","Down.DE","non.DE") = c("#B2182B","#2166AC","grey") and generate the volcano plot below

  • show the GeneSym for the top 10 DE genes based on padj

• hints:

  • use read.csv() to import DiffEx.csv to R

  • if you failed to import DiffEx.csv to R, please check whether you set up your work directory properly by using getwd() and setwd()

# install.packages("ggplot2")
library(ggplot2)
# install.packages("ggrepel")
library(ggrepel)

# This is the work directory I am using and yours will be different
# setwd("/Volumes/bioinfomatics$/yfwang/BioinformaticsCore/GitHub/LMS_PlottingInR/course/")

# reading in table 
df4ggplot <- read.csv("data/DiffEx.csv")

df4ggplot$DE.cat <- ifelse(df4ggplot$padj < 0.01 & abs(df4ggplot$log2FoldChange) > 1,
  "TRUE", "FALSE"
)

df4ggplot$DE.cat <- factor(df4ggplot$DE.cat,
                           levels=c("TRUE","FALSE"))
table(df4ggplot$DE.cat)

## 
##  TRUE FALSE 
##  1237 17120

df4ggplot$newDE.cat <- ifelse(
  df4ggplot$DE.cat == "FALSE", "non.DE","all.DE"
)
table(df4ggplot$newDE.cat)

## 
## all.DE non.DE 
##   1237  17120

df4ggplot[df4ggplot$newDE.cat=="all.DE",]$newDE.cat <- ifelse(
  df4ggplot[df4ggplot$newDE.cat=="all.DE",]$log2FoldChange > 0, "Up.DE","Down.DE"
)
table(df4ggplot$newDE.cat)

## 
## Down.DE  non.DE   Up.DE 
##     539   17120     698

df4ggplot <- df4ggplot[order(df4ggplot$padj),]

df4ggplot$newDE.cat <- factor(df4ggplot$newDE.cat,
                              levels = c("Up.DE","Down.DE","non.DE"))

vol_col <- ggplot(data=df4ggplot, 
                   aes(x=log2FoldChange, y=-log10(pvalue),col=newDE.cat)) +
  geom_point(size=0.75) +  
  xlab("log2FoldChange") + ylab("-log10 PValue")+ 
  scale_color_manual(values=c("#B2182B","#2166AC","grey"))+
  geom_label_repel(data=df4ggplot[c(1:10),],
                   mapping=aes(label = mgi_symbol),
                   box.padding = 0.5,max.overlaps = 20)+
  theme_classic() + ggtitle("Volcano plot")

vol_col

Section 2 - MA plot

• MA plot visualizes the differences between measurements taken in two samples, by transforming the data onto M (log FC ratio) and A (mean average) scales, then plotting these values

• Please use columns 'log2FoldChange' and 'log2(baseMean)' to create the MA plot

MA_col <- ggplot(data=df4ggplot, 
                   aes(x=log2(baseMean), y=log2FoldChange,col=newDE.cat)) +
  geom_point(size=0.75,alpha=0.5) +  
  xlab("log2(Averaged.Norm.Expression)") + ylab("log2(FC)")+ 
  scale_color_manual(values=c("#B2182B","#2166AC","grey"))+
  geom_label_repel(data=df4ggplot[c(1:10),],
                   mapping=aes(label = mgi_symbol),
                   box.padding = 0.5,max.overlaps = 20)+
  theme_classic() + ggtitle("MA plot")

MA_col