ggmanh 1.11.0
The package 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 ggrepel 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 data.frame
, MPdata
, or a GRanges
object. The data.frame
, at bare minimum, must have three columns containing: chromosome
, position
, and 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.
While using manhattan_plot()
on 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