MKinfer (version 1.2)

volcano: Volcano Plots

Description

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

Usage

volcano(x, ...)
# S3 method for default
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, ...)

Value

Object of class gg and ggplot.

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.

Author

Matthias Kohl

Details

The plot generates a ggplot2 object that is shown.

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.

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")