Yil*_*ang 2 average r bioinformatics mean ggplot2
这是我现在的情节.

它是从这段代码生成的:
ggplot(data1, aes(x=POS,y=DIFF,colour=GT)) +
geom_point() +
facet_grid(~ CHROM,scales="free_x",space="free_x") +
theme(strip.text.x = element_text(size=40),
strip.background = element_rect(color='lightblue',fill='lightblue'),
legend.position="top",
legend.title = element_text(size=40,colour="lightblue"),
legend.text = element_text(size=40),
legend.key.size = unit(2.5, "cm")) +
guides(fill = guide_legend(title.position="top",
title = "Legend:GT='REF'+'ALT'"),
shape = guide_legend(override.aes=list(size=10))) +
scale_y_log10(breaks=trans_breaks("log10", function(x) 10^x, n=10)) +
scale_x_continuous(breaks = pretty_breaks(n=3)) +
geom_line(stat = "hline",
yintercept = "mean",
size = 1)
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最后一行geom_line为每个面板创建平均线.
但现在我想在每个面板中获得更具体的运行平均值.
即如果panel1('chr01')的x轴范围为0到100,000,000,我希望得到每1,000,000范围的平均值.
mean1 = mean(x = 0到x = 1,000,000)
mean2 = mean(x = 1,000,001 to x = 2,000,000)
像那样.
提供运行平均值的一种方法是geom_smooth()使用loess局部回归方法.为了演示我提出的解决方案,我使用R函数创建了一个虚假的基因组数据集.您可以调整span参数geom_smooth以使运行平均值更平滑(更接近1.0)或更粗糙(更接近1 /数据点数).
# 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)
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