volcano: Volcano Plots

In MKinfer: Inferential Statistics

Volcano Plots

Description

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

Usage

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.

Value

Object of class gg and ggplot.

Author(s)

Matthias Kohl [e-mail]

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")

MKinfer documentation built on April 17, 2023, 9:10 a.m.