ggplot2渐变如何设置自定义颜色和值？

Question

我想使用自定义颜色和值为我的数据创建类似热图的图。我在StackOverflow上阅读了一些博客和QA，发现scale_colour_gradientn是执行此操作的最佳方法。但是，我尽了最大努力，但是我无法对齐颜色和值，而且我仍然不知道为什么。

似乎我的关键代码有问题：

sc <- scale_colour_gradientn(
  colors = c("blue", "white", "yellow", "red"),
  values = scales::rescale(c(
    data$measure %>% min(na.rm = TRUE),
    0, 
    60,
    data$measure %>% max(na.rm = TRUE))
  ))

以下是我的可复制代码。

library(dplyr)
library(ggplot2)

data <- structure(list(signature = structure(c(1L, 1L, 1L, 1L, 2L, 2L, 
                                               2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 
                                               5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 
                                               6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 
                                               8L, 8L, 8L, 8L, 8L, 8L, 8L), .Label = c("expo1", "expo2", 
                                                                                       "expo3", "expo4", "expo5", "expo6", "expo7", "expo8"
                                               ), class = "factor"), feature = structure(c(1L, 2L, 9L, 10L, 
                                                                                           1L, 2L, 11L, 4L, 6L, 8L, 9L, 10L, 11L, 4L, 8L, 9L, 10L, 1L, 2L, 
                                                                                           4L, 6L, 10L, 1L, 2L, 11L, 4L, 6L, 8L, 9L, 10L, 1L, 2L, 11L, 3L, 
                                                                                           4L, 6L, 8L, 7L, 9L, 10L, 1L, 2L, 11L, 3L, 4L, 5L, 6L, 8L, 7L, 
                                                                                           9L, 10L, 1L, 2L, 11L, 4L, 5L, 6L, 8L, 7L, 9L, 10L), .Label = c("Cell_Cycle", 
                                                                                                                                                          "Hippo", "MYC", "NOTCH", "NRF2", "PI3K", "TGF-Beta", "RTK-RAS", 
                                                                                                                                                          "TP53_pathway", "WNT", "HR_pathway"), class = "factor"), count = c(923, 
                                                                                                                                                                                                                             923, 923, 923, 923, 923, 923, 923, 923, 923, 923, 923, 923, 923, 
                                                                                                                                                                                                                             923, 923, 923, 923, 923, 923, 923, 923, 923, 923, 923, 923, 923, 
                                                                                                                                                                                                                             923, 923, 923, 959, 959, 959, 959, 959, 959, 959, 959, 959, 959, 
                                                                                                                                                                                                                             959, 959, 959, 959, 959, 959, 959, 959, 959, 959, 959, 959, 959, 
                                                                                                                                                                                                                             959, 959, 959, 959, 959, 959, 959, 959), measure = c(3.35, 2.58, 
                                                                                                                                                                                                                                                                                  4.5, 1.58, 25.7, 17.2, 12.1, 12.8, 4.93, 15, 16.8, 14.1, 7.83, 
                                                                                                                                                                                                                                                                                  7.9, 8.62, 23.5, 8.74, 10.9, 5.44, 7.49, 9, 8.04, -3.51, -2.66, 
                                                                                                                                                                                                                                                                                  -2.8, -2.85, -2, -2.48, -4.68, -3, 18.9, 18.6, 27, 39.4, 23.6, 
                                                                                                                                                                                                                                                                                  13.3, 18.2, 35.1, 11.3, 18, 173, 60.8, 114, 323, 67.5, 531, 78.5, 
                                                                                                                                                                                                                                                                                  56.7, 297, 39, 59.8, 144, 55, 73.4, 59.2, 262, 67.1, 51.2, 197, 
                                                                                                                                                                                                                                                                                  25.9, 53.8), p = c(0.0228148148148148, 0.0379866666666667, 3.21391304347826e-07, 
                                                                                                                                                                                                                                                                                                     0.0083330612244898, 0.000378888888888889, 5.78285714285714e-06, 
                                                                                                                                                                                                                                                                                                     0.0270175438596491, 7.65333333333333e-05, 0.00438044444444444, 
                                                                                                                                                                                                                                                                                                     1.8392e-05, 5.56285714285714e-11, 0.000377142857142857, 0.0416918032786885, 
                                                                                                                                                                                                                                                                                                     0.00926588235294118, 0.000426461538461538, 1.6368e-17, 0.00550468085106383, 
                                                                                                                                                                                                                                                                                                     0.0359457627118644, 0.0286758620689655, 0.00438044444444444, 
                                                                                                                                                                                                                                                                                                     0.00421581395348837, 0.00760833333333333, 0.000426461538461538, 
                                                                                                                                                                                                                                                                                                     5.85655172413793e-06, 0.000426461538461538, 2.948e-06, 0.00156933333333333, 
                                                                                                                                                                                                                                                                                                     5.24740740740741e-06, 1.0956e-19, 1.1484e-07, 3.20774193548387e-05, 
                                                                                                                                                                                                                                                                                                     4.54892307692308e-11, 2.832e-11, 0.0106276923076923, 4.95e-15, 
                                                                                                                                                                                                                                                                                                     0.000106117647058824, 4.60533333333333e-12, 0.0240428571428571, 
                                                                                                                                                                                                                                                                                                     1.21691428571429e-12, 9.3808e-15, 1.11936e-06, 4.49533333333333e-11, 
                                                                                                                                                                                                                                                                                                     2.68631578947368e-08, 0.00127921951219512, 5.027e-10, 0.00550468085106383, 
                                                                                                                                                                                                                                                                                                     7.7e-07, 9.83529411764706e-10, 0.0126520754716981, 4.98666666666667e-14, 
                                                                                                                                                                                                                                                                                                     4.532e-12, 1.21104761904762e-07, 1.1792e-11, 0.02336, 3.21391304347826e-07, 
                                                                                                                                                                                                                                                                                                     0.0087648, 0.0010648, 7.97866666666667e-11, 4.7025e-05, 1.0956e-19, 
                                                                                                                                                                                                                                                                                                     3.92088888888889e-09), Samples = structure(c(5L, 5L, 5L, 5L, 
                                                                                                                                                                                                                                                                                                                                                  5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
                                                                                                                                                                                                                                                                                                                                                  5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
                                                                                                                                                                                                                                                                                                                                                  5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
                                                                                                                                                                                                                                                                                                                                                  5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L), .Label = c("(0,200]", "(200,400]", 
                                                                                                                                                                                                                                                                                                                                                                                                  "(400,600]", "(600,800]", "(800,1.02e+03]"), class = "factor")), class = c("tbl_df", 
                                                                                                                                                                                                                                                                                                                                                                                                                                                                             "tbl", "data.frame"), row.names = c(NA, -61L))

summary(data$measure)
sc <- scale_colour_gradientn(
  colors = c("blue", "white", "yellow", "red"),
  values = scales::rescale(c(
    data$measure %>% min(na.rm = TRUE),
    0, 
    60,
    data$measure %>% max(na.rm = TRUE))
  ))


p <- ggplot2::ggplot(data, ggplot2::aes(
  x = signature,
  y = feature
))

p <- p + ggplot2::geom_point(ggplot2::aes_string(
  colour = "measure",
  size = "Samples"
)) + ggplot2::scale_size_discrete(drop = FALSE) +
  ggplot2::scale_x_discrete(drop = FALSE)

p + sc

Answer 1

您可以简单地从scale_colour_gradientn中删除“蓝色”颜色，强制从“白色”到“红色”的渐变。因此，您将获得一个图表，其中低值（接近0）用“白色”着色。这是我对您的代码所做的唯一修改。

sc <- scale_colour_gradientn(
  colors = c("white","yellow", "red"),
  values = scales::rescale(c(
    data$measure %>% min(na.rm = TRUE),
    0, 
    60,
    data$measure %>% max(na.rm = TRUE))
  ))