Question

我提前为这个问题的特殊性道歉，我希望我的神经科学术语不会混淆事情。

我有来自单细胞记录的当前痕迹，我需要在每个迹线的峰值输出后从峰值到4秒安装tau。实际上，这只是指数衰减。

所以我正在寻找的是适合的ggplot叠加（所以在请求的时间内确实是一条合适的线。）有些东西沿着stat_smooth（）的方式

我的数据如下：

dput(stackover_data)
structure(list(Time = c(0.09990001, 0.19990001, 0.29990001, 0.39990001, 
0.49990001, 0.59990001, 0.69990001, 0.79990001, 0.89990001, 0.99990001, 
1.09990001, 1.19990001, 1.29990001, 1.39990001, 1.49990001, 1.59990001, 
1.69990001, 1.79990001, 1.89990001, 1.99990001, 2.09990001, 2.19990001, 
2.29990001, 2.39990001, 2.49990001, 2.59990001, 2.69990001, 2.79990001, 
2.89990001, 2.99990001, 3.09990001, 3.19990001, 3.29990001, 3.39990001, 
3.49990001, 3.59990001, 3.69990001, 3.79990001, 3.89990001, 3.99990001, 
4.09990001, 4.19990001, 4.29990001, 4.39990001, 4.49990001, 4.59990001, 
4.69990001, 4.79990001, 4.89990001, 4.99990001, 5.09990001, 5.19990001, 
5.29990001, 5.39990001, 5.49990001, 5.59990001, 5.69990001, 5.79990001, 
5.89990001, 5.99990001, 6.09990001, 6.19990001, 6.29990001, 6.39990001, 
6.49990001, 6.59990001, 6.69990001, 6.79990001, 6.89990001, 6.99990001, 
7.09990001, 7.19990001, 7.29990001, 7.39990001, 7.49990001, 7.59990001, 
7.69990001, 7.79990001, 7.89990001, 7.99990001, 8.09990001, 8.19990001, 
8.29990001, 8.39990001, 8.49990001, 8.59990001, 8.69990001, 8.79990001, 
8.89990001, 8.99990001, 9.09990001, 9.19990001, 9.29990001, 9.39990001, 
9.49990001, 9.59990001, 9.69990001, 9.79990001, 9.89990001, 9.99990001, 
10.09990001, 10.19990001, 10.29990001, 10.39990001, 10.49990001, 
10.59990001, 10.69990001, 10.79990001, 10.89990001, 10.99990001, 
11.09990001, 11.19990001, 11.29990001, 11.39990001, 11.49990001, 
11.59990001, 11.69990001, 11.79990001, 11.89990001, 11.99990001, 
12.09990001, 12.19990001, 12.29990001, 12.39990001, 12.49990001, 
12.59990001, 12.69990001, 12.79990001, 12.89990001, 12.99990001, 
13.09990001, 13.19990001, 13.29990001, 13.39990001, 13.49990001, 
13.59990001, 13.69990001, 13.79990001, 13.89990001, 13.99990001, 
14.09990001, 14.19990001, 14.29990001, 14.39990001, 14.49990001, 
14.59990001, 14.69990001, 14.79990001, 14.89990001, 14.99990001, 
15.09990001, 15.19990001, 15.29990001, 15.39990001, 15.49990001, 
15.59990001, 15.69990001, 15.79990001, 15.89990001, 15.99990001, 
16.09990001, 16.19990001, 16.29990001, 16.39990001, 16.49990001, 
16.59990001, 16.69990001, 16.79990001, 16.89990001, 16.99990001, 
17.09990001, 17.19990001, 17.29990001, 17.39990001, 17.49990001, 
17.59990001, 17.69990001, 17.79990001, 17.89990001, 17.99990001, 
18.09990001, 18.19990001, 18.29990001, 18.39990001, 18.49990001, 
18.59990001, 18.69990001, 18.79990001, 18.89990001, 18.99990001, 
19.09990001, 19.19990001, 19.29990001, 19.39990001, 19.49990001, 
19.59990001, 19.69990001, 19.79990001, 19.89990001, 19.99990001
), `Trace 1` = c(-3.08656892325052, 9.36821982641837, 8.13806079083122, 
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-18.8208568928653, -10.7306087216159, -16.4281210876173, -19.3057174287183, 
-15.9745523586581), `Trace 2` = c(5.94927992143286, 1.42121402161905, 
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177000L, 178000L, 179000L, 180000L, 181000L, 182000L, 183000L, 
184000L, 185000L, 186000L, 187000L, 188000L, 189000L, 190000L, 
191000L, 192000L, 193000L, 194000L, 195000L, 196000L, 197000L, 
198000L, 199000L, 200000L), class = "data.frame")

如果你

plot(stackover_data$Time,stackover_data$'Trace 1',type='l'

你可以看到时间= 2是峰值的位置，我希望在ggplot中适应2秒到6秒。（是的，我知道我在示例中使用了基础图形，它更容易在ggplot2中绘制一个图形。

谢谢大家！

Answer 1

library(ggplot)
# rename the variables, so there's no space in them (is easier in aes())
names(neurons)[2:4] <- paste0('Trace', 1:3)

现在我们制作一个图，我们根据轨迹中的最低点选择数据：

ggplot(neurons, aes(x = Time, y = Trace1)) +
  geom_line() +
  geom_smooth(data = subset(neurons, Time >= Time[which.min(Trace1)] & 
                                     Time < Time[which.min(Trace1)] + 4)) +
  theme_classic() +
  xlim(0, 10)

结果：

请注意，简单基本图的ggplot等价物如下：

qplot(neurons$Time, neurons$Trace1, geom = 'line')

基本上，同样容易;）

使用ggplot2在特定时间段内拟合tau（时间常数）

1 个答案: