Could you edit your question by adding some test data and also what you already tried.

You can check ggplot2 documentation, ggplot2 tutorial, ggrepel. These are enough to plot what you have asked for.

Hi Kevin, I am trying to use your code with my data but I am not being able to produce any plot (eventhough I don't get any error messages back). So I wonder wether you could help me to localize my problem. The code I am using is the following

results <- read.table("DEseq2 output.txt", sep = "\t", header = T, as.is = T)
toptable <- as.data.frame(results)

pdf("Volcanoplot_DoubleKD_gene_type.pdf",width = 10.15, height = 8.10)
library(ggplot2)


EnhancedVolcanoDESeq2 <- function(toptable, AdjustedCutoff=0.05, LabellingCutoff=0.05, FCCutoff=2.0, main="DoubleKD VolcanoPlot")
{
  toptable$Significance <- "NS"
  toptable$Significance[(abs(toptable$log2FoldChange) > FCCutoff)] <- "FC"
  toptable$Significance[(toptable$padj<AdjustedCutoff)] <- "FDR"
  toptable$Significance[(toptable$padj<AdjustedCutoff) & (abs(toptable$log2FoldChange)>FCCutoff)] <- "FC_FDR"
  table(toptable$Significance)
  toptable$Significance <- factor(toptable$Significance, levels=c("NS", "FC", "FDR", "FC_FDR"))

  plot <- ggplot(toptable, aes(x=log2FoldChange, y=-log10(padj))) +

    #Add points:
    #   Colour based on factors set a few lines up
    #   'alpha' provides gradual shading of colour
    #   Set size of points
    geom_point(aes(color=factor(Significance)), alpha=1/2, size=0.8) +

    #Choose which colours to use; otherwise, ggplot2 choose automatically (order depends on how factors are ordered in toptable$Significance)
    scale_color_manual(values=c(NS="grey30", FC="forestgreen", FDR="royalblue", FC_FDR="red2"), labels=c(NS="NS", FC=paste("LogFC>|", FCCutoff, "|", sep=""), FDR=paste("FDR Q<", AdjustedCutoff, sep=""), FC_FDR=paste("FDR Q<", AdjustedCutoff, " & LogFC>|", FCCutoff, "|", sep=""))) +

    #Set the size of the plotting window
    theme_bw(base_size=24) +

    #Modify various aspects of the plot text and legend
    theme(legend.background=element_rect(),
          plot.title=element_text(angle=0, size=12, face="bold", vjust=1),

          panel.grid.major=element_blank(), #Remove gridlines
          panel.grid.minor=element_blank(), #Remove gridlines

          axis.text.x=element_text(angle=0, size=12, vjust=1),
          axis.text.y=element_text(angle=0, size=12, vjust=1),
          axis.title=element_text(size=12),

          #Legend
          legend.position="top",            #Moves the legend to the top of the plot
          legend.key=element_blank(),       #removes the border
          legend.key.size=unit(0.5, "cm"),  #Sets overall area/size of the legend
          legend.text=element_text(size=8), #Text size
          title=element_text(size=8),       #Title text size
          legend.title=element_blank()) +       #Remove the title

    #Change the size of the icons/symbols in the legend
    guides(colour = guide_legend(override.aes=list(size=2.5))) +

    #Set x- and y-axes labels
    xlab(bquote(~Log[2]~ "fold change")) +
    ylab(bquote(~-Log[10]~adjusted~italic(P))) +

    #Set the axis limits
    #xlim(-6.5, 6.5) +
    #ylim(0, 100) +

    #Set title
    ggtitle(main) +

    #Tidy the text labels for a subset of genes
    geom_text(data=subset(toptable, padj<LabellingCutoff & abs(log2FoldChange)>FCCutoff),
              aes(label=rownames(subset(toptable, padj<LabellingCutoff & abs(log2FoldChange)>FCCutoff))),
              size=2.25,
              #segment.color="black", #This and the next parameter spread out the labels and join them to their points by a line
              #segment.size=0.01,
              check_overlap=TRUE,
              vjust=1.0) +

    #Add a vertical line for fold change cut-offs
    geom_vline(xintercept=c(-FCCutoff, FCCutoff), linetype="longdash", colour="black", size=0.4) +

    #Add a horizontal line for P-value cut-off
    geom_hline(yintercept=-log10(AdjustedCutoff), linetype="longdash", colour="black", size=0.4)

  return(plot)
}

The DEseq2 output has the regular structure, being: Contig baseMean log2FoldChange lfcSE stat pvalue padj NReads FBgn0000003 135701.1304 3.841593895 0.218300714 17.59771566 2.56E-69 3.72E-66 513393 etc.

Thanks in advance,

Jordi Planells