coord_serial - idiomatic Manhattan plots in ggplot2

This is an R package - an extension of the ggplot2 package - for plotting data along a continuous axis composed of multiple discrete domains (e.g. chromosomes in a Manhattan plot), using a grammar of graphics approach.

Most current approaches require either manual pre-calculation of the new axis, or use a wrapper function which limits the customisability of the ggplot framework.

The idiomatic ggplot2 approach is to provide a new coordinate system, coord_serial(), that can be used with ggplot2 to plot data along a single axis.

The benefits of this approach are:

• no bespoke data pre-processing
• full interoperability with other ggplot2 components

Example

While coord_serial is general-purpose, it is often used for genetics data like manhattan plots. Here is a concrete example using simulated GWAS data:

library(coord.serial)
library(ggplot2)

# genetics-specific example data
simulated_gwas <- simulate_gwas(p_max=0.05)

# plot using the generalized coord_serial()
p <- ggplot(simulated_gwas, aes(x = position,
                                y = log10p,
                                domain = chrom,
                                colour = causal)) +
    geom_point() +
    coord_serial()

# extra formatting
p + scale_color_manual(values = c("TRUE" = "red", "FALSE" = "grey40")) +
    theme_minimal() +
    labs(x = "Chromosome", color = "Causal Variant") +
    geom_hline(yintercept = 7.3, linetype = "dashed", color = "red")

ggsave('man/figures/manhattan.png', width = 12, height = 5)

Because the coordinate system is seperated from the geom, you can swap it out. For example, often there are many points and don't need to plot them all at this scale, so we could instead use geom_bin_2d:

 <- ggplot(simulated_gwas, aes(x = position,
                                y = log10p,
                                domain = chrom)) +
    geom_bin_2d(bins=list(x=200, y=100)) +
    coord_serial()
ggsave('man/figures/manhattan_geom_bin_2d.png', width = 12, height = 5)

Scaffolding (fixed domain lengths)

By default, the length of each domain is calculated from the the input data. However, you can provide a scaffold (a named vector) to specify fixed lengths. This is particularly useful to spot missing data in QC.

The package provides built-in scaffolds for human genome assemblies: grch37 and grch38.

# Use the built-in GRCh38 scaffold
p = ggplot(simulated_gwas[simulated_gwas$chrom != "5", ], aes(x = position, y = log10p, domain = chrom)) +
    geom_point() +
    coord_serial(scaffold = grch38)
ggsave('man/figures/manhattan_scaffold.png', p, width = 12, height = 5)

genomic domain visualization (Exons)

An alternative use case within genomics is visualisation of data across exons of a gene.

# Simulate conservation scores for exons of varying lengths
exon_data <- simulate_serial(
    n = 1000, 
    domains = c("Exon 1", "Exon 2", "Exon 3", "Exon 4"), 
    domain_lengths = c(120, 300, 80, 200)
)

ggplot(exon_data, aes(x = position, y = log10p, domain = domain, fill = domain)) +
    geom_area(show.legend = FALSE) +
    coord_serial() +
    theme_minimal() +
    labs(title = "Conservation Scores across Exons", x = "Position", y = "Score")

Note again, we can use alternative geoms, in this case geom_area.

non-genomic time series

Outside of genomics, coord_serial can be used for plotting data across "blocks" of time or space into a single continuous view. Here is an example of monitoring system activity across sessions with different durations.

# Simulate activity metrics for sessions with different durations (seconds)
sessions <- simulate_serial(
    n = 2000,
    domains = c("A", "B", "C", "D"),
    domain_lengths = c(3600, 7200, 2400, 300)
)
# Add a random walk per session with randomized starting points
sessions$metric <- ave(rnorm(nrow(sessions)), sessions$domain, FUN = function(x) {
    cumsum(x) + runif(1, -10, 10)
})

ggplot(sessions, aes(x = position, y = metric, domain = domain, color = domain)) +
    geom_line(show.legend = FALSE) +
    coord_serial() +
    theme_light() +
    labs(title = "System Load across Sessions", x = "Time (seconds)", y = "Load Metric")

Installation

remotes::install_github("daniel-wells/coord_serial")

Nix-based local testing

If you use Nix, this repository includes a development shell with R and package dependencies.

nix develop
./nix/test-package.sh

This script will:

• build the package tarball from source
• install the tarball into the current R environment
• run tests in tests/testthat