这是一个有点doozie！
在绘制它之前，您需要弄清楚有多少个0是连续的。

library(tidyverse)
library(ggplot2)
library(ggbreak)

breakers <- breakers <- cov %>% 
mutate(gr = cumsum(coverage == 0), 
       gs = cumsum(coverage != lag(coverage, default = 0))) %>% 
group_by(gs, coverage) %>% 
summarise(min_pos = min(position), 
          max_pos = max(position), 
          min_gr = min(gr), 
          max_gr = max(gr)) %>% 
ungroup() %>% 
mutate(diff_pos = max_pos - min_pos, diff_gr = max_gr - min_gr) %>% 
filter(coverage == 0)

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然后你可以在此基础上进行绘图。让我说清楚，我找不到一种方法来编程化这个。

ggplot(data = cov, aes(x = position, y = coverage)) +
geom_line() +
xlab("Position") +
ylab("Coverage") +
ggtitle("Coverage vs. Position") + 
scale_x_break(breaks = c(breakers$min_pos[1], breakers$max_pos[1]), scales = 'free') + 
scale_x_break(breaks = c(breakers$min_pos[2], breakers$max_pos[2]), scales = 'free') + 
scale_x_break(breaks = c(breakers$min_pos[3], breakers$max_pos[3]), scales = 'free') + 
scale_x_break(breaks = c(breakers$min_pos[4], breakers$max_pos[4]), scales = 'free')

型
最后的图看起来像
x1c 0d1x的数据

我的方法是取+/-带宽平均值，每次平均值变为或停止为零时创建一个“部分”，过滤掉无聊的零部分，留下尖峰周围区域的视图。

library(dplyr)
bandwidth = 200
cov %>%
  mutate(avg = slider::slide_dbl(coverage, mean, 
                                 .before = bandwidth, .after = bandwidth)) %>%
  mutate(section = cumsum((avg > 0) != (lag(avg>0,1,0)))) %>%
  filter(avg != 0) %>%
  ggplot(aes(position, coverage)) +
  geom_line() +
  facet_wrap(~section, scales = "free_x")

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x1c 0d1x的数据