volcano function

Volcano Plots

Produce volcano plot(s) of the given effect size and p values.

volcano(x, ...)

## Default S3 method:
volcano(x, pval, effect0 = 0, sig.level = 0.05, 
                            effect.low = NULL, effect.high = NULL,
                            color.low = "#4575B4", color.high = "#D73027",
                            xlab = "effect size", ylab = "-log10(p value)",
                            title = "Volcano Plot", alpha = 1, shape = 19, 
                            na.rm = TRUE, ...)

Arguments

• x: in case of default method: measure of effect size.
• pval: numeric, (adjusted) p values.
• effect0: single numeric, value for no effect.
• sig.level: single numeric, significance level.
• effect.low: NULL or single numeric, boundary for low effect sizes.
• effect.high: NULL or single numeric, boundary for low effect sizes.
• color.low: color used if effect size smaller than effect.low and (adjusted) p value smaller than sig.level.
• color.high: color used if effect size larger than effect.high and (adjusted) p value smaller than sig.level.
• xlab: label of x-axis.
• ylab: label of y-axis.
• title: title of plot.
• alpha: blending factor (default: no blending.
• shape: point shape used.
• na.rm: single logical, remove NA values before plotting.
• ...: further arguments that may be passed through.

Details

The plot generates a ggplot2 object that is shown.

Returns

Object of class gg and ggplot.

References

Wikipedia contributors, Volcano plot (statistics), Wikipedia, The Free Encyclopedia, https://en.wikipedia.org/w/index.php?title=Volcano_plot_(statistics)&oldid=900217316

(accessed December 25, 2019).

For more sophisticated and flexible volcano plots see for instance: Blighe K, Rana S, Lewis M (2019). EnhancedVolcano: Publication-ready volcano plots with enhanced colouring and labeling. R/Bioconductor package. https://github.com/kevinblighe/EnhancedVolcano.

Author(s)

Matthias Kohl Matthias.Kohl@stamats.de

Examples

## Generate some data
x <- matrix(rnorm(1000, mean = 10), nrow = 10)
g1 <- rep("control", 10)
y1 <- matrix(rnorm(500, mean = 11.75), nrow = 10)
y2 <- matrix(rnorm(500, mean = 9.75, sd = 3), nrow = 10)
g2 <- rep("treatment", 10)
group <- factor(c(g1, g2))
Data <- rbind(x, cbind(y1, y2))
pvals <- apply(Data, 2, function(x, group) hsu.t.test(x ~ group)$p.value,
               group = group)
## compute log-fold change
logfc <- function(x, group){
  res <- tapply(x, group, mean)
  log2(res[1]/res[2])
}
lfcs <- apply(Data, 2, logfc, group = group)

volcano(lfcs, pvals, xlab = "log-fold change")
volcano(lfcs, pvals, effect.low = -0.25, effect.high = 0.25, 
        xlab = "log-fold change")
volcano(lfcs, p.adjust(pvals, method = "fdr"), 
        effect.low = -0.25, effect.high = 0.25, 
        xlab = "log-fold change", ylab = "-log10(adj. p value)")
volcano(2^lfcs, pvals, effect0 = 1, effect.low = 1/2^0.25, effect.high = 2^0.25,
        xlab = "mean difference")