ggmanh is aimed to provide easy and direct access to visualisation to the GWAS / PWAS results while also providing many functionalities and features.
Manhattan plot is commonly used to display significant Single Nucleotide Polymorphisms (SNPs) in Genome Wide Association Study (GWAS). The x-axis is divided into chromosomes, and SNPs are plotted in their respective positions. The y-axis typically represents \(-10*log(p value)\). Majority of the points have low y-values, with some of the significant SNPs having high y-values. It is not uncommon to see strong association between SNPs and a phenotype, yielding a high y-value. This results in a wide y-scales in which SNPs with lower significance are squished.
This function addresses this problem by rescaling the y-axis according to the range of y. There are more features, such as labelling without overlap (with the help of ggprepel package), reflecting the size of chromosomes along the x-axis, and displaying significant lines.
The package can be installed by:
if (!require("BiocManager")) install.packages("BiocManager") BiocManager::install("ggmanh")
manhattan_plot() is a generic method that can take a
MPdata, or a
GRanges object. The
data.frame, at bare minimum, must have three columns containing:
p.value. For a
GRanges object, meta data column name for the p-value needs to be passed.
There are two steps to this function: preprocess data and plot data. The preprocessing step (accomplished with
manhattan_data_preprocess()) preprocesses the data by calculating the new x-position to map to the plot (
new_pos column added to the data), “thining” the data points, and saving other graphical information needed for manhattan plot, which is returned as a
MPdata object. The plot step (accomplished with
manhattan_plot()) determines if rescaling of the y-axis is needed and plots / saves the manhattan plot.
data.frame is sufficient, it is fine to separately run pre-processing and plotting for customizing the plot without having to preprocess again and again.
library(ggmanh) #> Loading required package: ggplot2 library(SeqArray) #> Loading required package: gdsfmt
First, create a simulated data to be used for demonstration.
set.seed(1000) nsim <- 50000 simdata <- data.frame( "chromosome" = sample(c(1:22,"X"), size = nsim, replace = TRUE), "position" = sample(1:100000000, size = nsim), "P.value" = rbeta(nsim, shape1 = 5, shape2 = 1)^7 )
manhattan_plot expects data.frame to have at least three columns: chromosome, position, and p.value.
head(simdata) #> chromosome position P.value #> 1 16 41575779 0.135933290 #> 2 4 73447172 0.764033749 #> 3 11 82120979 0.002440878 #> 4 22 94419970 0.644460838 #> 5 19 38141341 0.184945910 #> 6 3 43235060 0.774330251
To avoid ambiguity in plotting, it is recommended that that the chromosome column is passed as a factor, or
chr.order is specified.
simdata$chromosome <- factor(simdata$chromosome, c(1:22,"X"))
This is the bare minimum to plot manhattan plot, and
manhattan_plot can handle the rest.
g <- manhattan_plot(x = simdata, pval.colname = "P.value", chr.colname = "chromosome", pos.colname = "position", plot.title = "Simulated P.Values", y.label = "P") g
manhattan_plot is also defaulted to display the GWAS p.value threshold at
5e-7. For now, the threshold is required; the values and color can be customized.
The function is also suited to rescale the y-axis depending on the magnitude of p values.
Let’s suppose that there are signals from chromosome 5 and 21, and the significant p-value is low for chromosome 21 and even lower for chromosome 5.
tmpdata <- data.frame( "chromosome" = c(rep(5, 10), rep(21, 5)), "position" = c(sample(250000:250100, 10, replace = FALSE), sample(590000:600000, 5, replace = FALSE)), "P.value" = c(10^-(rnorm(10, 100, 3)), 10^-rnorm(5, 9, 1)) ) simdata <- rbind(simdata, tmpdata) simdata$chromosome <- factor(simdata$chromosome, c(1:22,"X"))
g <- manhattan_plot(x = simdata, pval.colname = "P.value", chr.colname = "chromosome", pos.colname = "position", plot.title = "Simulated P.Values - Significant", rescale = FALSE) g
The significant point at chromosome 5 has such a small p-value compared to other chromosomes that other significant poitns are less visible and the pattern at other chromosomes are masked. Rescaling attempts to fix this by changing the visual scale near the significant cutoff, removing the large white space in between.
g <- manhattan_plot(x = simdata, pval.colname = "P.value", chr.colname = "chromosome", pos.colname = "position", plot.title = "Simulated P.Values - Significant", rescale = TRUE) g