我有两个时间轴的数据和每个单元格的测量值。从此我创建了一个热图。我也知道每个细胞的测量是否显着。
我的问题是在所有重要的细胞周围画一条轮廓线。如果细胞形成具有相同显着性值的聚类,我需要在聚类周围绘制轮廓而不是在每个单独的细胞周围。
数据采用以下格式:
x_time y_time metric signif
1 1 1 0.3422285 FALSE
2 2 1 0.6114085 FALSE
3 3 1 0.5381621 FALSE
4 4 1 0.5175120 FALSE
5 1 2 0.6997991 FALSE
6 2 2 0.3054885 FALSE
7 3 2 0.8353888 TRUE
8 4 2 0.3991566 TRUE
9 1 3 0.7522728 TRUE
10 2 3 0.5311418 TRUE
11 3 3 0.4972816 TRUE
12 4 3 0.4330033 TRUE
13 1 4 0.5157972 TRUE
14 2 4 0.6324151 TRUE
15 3 4 0.4734126 TRUE
16 4 4 0.4315119 TRUE
下面的代码生成这些数据,其中测量是随机的(dt $ metrics),重要性是逻辑的(dt $ signif)。
# data example
dt <- data.frame(x_time=rep(seq(1, 4), 4),
y_time=rep(seq(1, 4), each=4),
metric=(rnorm(16, 0.5, 0.2)),
signif=c(rep(FALSE, 6), rep(TRUE, 10)))
单独的热图可以使用ggplot2的geom_tile
生成
# Generate heatmap using ggplot2's geom_tile
library(ggplot2)
p <- ggplot(data = dt, aes(x = x_time, y = y_time))
p <- p + geom_tile(aes(fill = metric))
基于this question,我设法根据显着性值在每个单元格周围绘制不同颜色的轮廓。
# Heatmap with lines around each significant cell
p <- ggplot(data = dt, aes(x = x_time, y = y_time))
p <- p + geom_tile(aes(fill = metric, color = signif), size = 2)
p <- p + scale_color_manual(values = c("black", "white"))
然而,这种方法不会通过在整个组周围绘制轮廓来将相邻的重要单元组合在一起(正如我在链接的问题中所讨论的那样)。
正如this question所示,可以在指定区域周围绘制框,但我不认为这可以扩展到所有可能的单元集群。
这个答案是基于How to get contour lines around the grids in R-raster?。
library(data.table)
library(raster)
另请注意,clump
要求安装igraph
包,dissolve = TRUE
中的rasterToPolygons
需要rgeos
。
# convert data.frame to data.table
# not strictly necessary, but enables use of convenient functions: dcast and rbindlist.
setDT(d)
# reshape to wide
d2 <- dcast(d, y ~ x, value.var = "sig")
# reverse order of rows to match raster order
# remove first column
# convert to matrix and then to raster
r <- raster(as.matrix(d2[ , .SD[.N:1, -1]]),
xmn = 0, xmx = ncol(d2) - 1, ymn = 0, ymx = ncol(d2) - 1)
# detect clumps of connected cells of the value TRUE
# convert raster to polygons
# dissolve polygons into multi-polygons
polys <- rasterToPolygons(clump(r), dissolve = TRUE)
# grab coordinates of individual polygons and convert to a data.table
# use idcol = TRUE to enable grouping of paths when plotting
d_poly <- rbindlist(lapply(polys@polygons,
function(x) as.data.table(x@Polygons[[1]]@coords)),
idcol = TRUE)
# plot an outline around each 'patch of significant values' using geom_path
ggplot(d, aes(x = x, y = y)) +
geom_tile(aes(fill = z)) +
geom_path(data = d_poly, aes(x = x + 0.5, y = y + 0.5, group = .id),
size = 2, color = "red")
d <- structure(list(x = c(1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L),
y = c(1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L,
1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L),
sig = c(FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, TRUE, TRUE,
TRUE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, TRUE),
z = c(0.96, 0.76, 0.14, 0.93, 0.39, 0.06, 0.99, 0.77,
0.7, 0.72, 0.08, 0.94, 0.98, 0.83, 0.12, 0.42)),
row.names = c(NA, -16L), class = "data.frame")
当然,如果您要创建大量的热图(即使可能使用数据中的必要值创建数据框),这将会有点单调乏味,但除此之外您还可以使用geom_segment
s:
p + geom_segment(aes(x = .5, xend = 4.5, y = 4.5, yend = 4.5), colour = "white", size = 2) +
geom_segment(aes(x = .5, xend = 2.5, y = 2.5, yend = 2.5), colour = "white", size = 2) +
geom_segment(aes(x = 2.5, xend = 4.5, y = 1.5, yend = 1.5), colour = "white", size = 2) +
geom_segment(aes(x = .5, xend = .5, y = 2.5, yend = 4.5), colour = "white", size = 2) +
geom_segment(aes(x = 2.5, xend = 2.5, y = 1.5, yend = 2.5), colour = "white", size = 2) +
geom_segment(aes(x = 4.5, xend = 4.5, y = 1.5, yend = 4.5), colour = "white", size = 2)