This is the plot I\'m having now.
It\'s generated from this code:
One way to provide a running mean is with geom_smooth()
using the loess
local regression method. In order to demonstrate my proposed solution, I created a fake genomic dataset using R functions. You can adjust the span
parameter of geom_smooth
to make the running mean smoother (closer to 1.0) or rougher (closer to 1/number of data points).
# Create example data.
set.seed(27182)
y1 = rnorm(10000) +
c(rep(0, 1000), dnorm(seq(-2, 5, length.out=8000)) * 3, rep(0, 1000))
y2 = c(rnorm(2000), rnorm(1000, mean=1.5), rnorm(1000, mean=-1, sd=2),
rnorm(2000, sd=2))
y3 = rnorm(4000)
pos = c(sort(runif(10000, min=0, max=1e8)),
sort(runif(6000, min=0, max=6e7)),
sort(runif(4000, min=0, max=4e7)))
chr = rep(c("chr01", "chr02", "chr03"), c(10000, 6000, 4000))
data1 = data.frame(CHROM=chr, POS=pos, DIFF=c(y1, y2, y3))
# Plot.
p = ggplot(data1, aes(x=POS, y=DIFF)) +
geom_point(alpha=0.1, size=1.5) +
geom_smooth(colour="darkgoldenrod1", size=1.5, method="loess", degree=0,
span=0.1, se=FALSE) +
scale_x_continuous(breaks=seq(1e7, 3e8, 1e7),
labels=paste(seq(10, 300, 10)), expand=c(0, 0)) +
xlab("Position, Megabases") +
theme(axis.text.x=element_text(size=8)) +
facet_grid(. ~ CHROM, scales="free", space="free")
ggsave(filename="plot_1.png", plot=p, width=10, height=5, dpi=150)