如何在 R 中可视化覆盖圆形图的集群？
How to visualize clusters overlaying a circle plot in R?
我使用一个名为 Revigo 的网站制作了一个情节，该网站提供了一个 R 脚本（包含在下面）来创建这样的情节：
我想看看是否可以在同一张图中的这些点之上执行和可视化聚类？因为这个图看起来像一个大圆圈，所以我想看看里面是否有我可以突出显示的更小的分组。我有生物学背景，所以我不确定从哪里开始尝试在同一个情节中获得这种可视化效果。我已经使用 hclust() 进行了探索，但我不知道将集群显示在该图顶部的步骤。
给出上图的代码和数据是：
library( ggplot2 );
library( scales );
revigo.names <- c("term_ID","description","frequency_%","plot_X","plot_Y","plot_size","log10_p_value","uniqueness","dispensability");
revigo.data <- rbind(c("GO:0002376","immune system process",16.463,-0.302,-3.807, 3.455,-8.3307,0.995,0.000),
c("GO:0006928","movement of cell or subcellular component",10.987, 1.052, 1.113, 3.280,-8.8153,0.965,0.000),
c("GO:0007610","behavior", 3.254,-1.620, 0.960, 2.752,-4.0048,0.994,0.000),
c("GO:0008150","biological_process",100.000,-6.029,-0.499, 4.239,-8.7447,1.000,0.000),
c("GO:0009987","cellular process",90.329,-5.288, 0.130, 4.195,-10.0701,0.999,0.000),
c("GO:0010243","response to organonitrogen compound", 4.697, 6.870,-2.756, 2.911,-12.7100,0.865,0.000),
c("GO:0023052","signaling",36.613, 2.976,-6.718, 3.803,-7.1931,0.996,0.000),
c("GO:0032501","multicellular organismal process",41.143, 3.761,-0.715, 3.853,-17.2741,0.997,0.000),
c("GO:0032502","developmental process",33.982,-2.865,-0.660, 3.770,-14.4202,0.996,0.000),
c("GO:0034762","regulation of transmembrane transport", 2.452,-3.982, 3.261, 2.629,-13.4123,0.788,0.000),
c("GO:0040007","growth", 5.447, 0.651,-0.889, 2.975,-4.2140,0.995,0.000),
c("GO:0040011","locomotion", 9.452, 2.305,-0.865, 3.215,-8.1068,0.995,0.000),
c("GO:0050896","response to stimulus",49.302, 3.556,-5.122, 3.932,-17.7852,0.997,0.000),
c("GO:0051179","localization",36.018,-0.564,-0.041, 3.795,-8.9245,0.996,0.000),
c("GO:0051704","multi-organism process", 9.873, 1.677,-5.787, 3.234,-4.6925,0.995,0.000),
c("GO:0065007","biological regulation",67.069,-4.157, 0.147, 4.065,-15.5918,0.998,0.000),
c("GO:0007154","cell communication",36.705,-0.841,-2.282, 3.804,-8.3595,0.992,0.006),
c("GO:0008283","cell proliferation",11.321, 2.644, 1.154, 3.293,-4.5376,0.982,0.024),
c("GO:0042391","regulation of membrane potential", 2.204,-0.568,-6.816, 2.583,-10.6073,0.895,0.035),
c("GO:0043086","negative regulation of catalytic activity", 4.934, 4.164,-2.157, 2.932,-4.9172,0.880,0.039),
c("GO:0019216","regulation of lipid metabolic process", 1.685, 3.142, 6.777, 2.467,-6.3215,0.880,0.043),
c("GO:0044057","regulation of system process", 2.862,-6.608, 1.584, 2.696,-13.1469,0.868,0.046),
c("GO:0051093","negative regulation of developmental process", 4.720,-4.950,-3.614, 2.913,-9.9031,0.830,0.050),
c("GO:0006793","phosphorus metabolic process",18.702,-4.363, 6.140, 3.511,-6.8729,0.961,0.053),
c("GO:0008284","positive regulation of cell proliferation", 4.859,-0.261, 4.128, 2.926,-9.2882,0.832,0.055),
c("GO:0065008","regulation of biological quality",20.202,-0.644, 5.306, 3.544,-20.3125,0.920,0.057),
c("GO:0050865","regulation of cell activation", 3.174, 2.497,-4.613, 2.741,-4.7878,0.917,0.062),
c("GO:0065009","regulation of molecular function",17.288,-1.972, 4.374, 3.477,-17.1959,0.923,0.078),
c("GO:0048518","positive regulation of biological process",30.969, 0.597, 8.033, 3.730,-15.2993,0.895,0.095),
c("GO:0051128","regulation of cellular component organization",13.335, 1.445, 4.799, 3.364,-4.5003,0.899,0.104),
c("GO:0007169","transmembrane receptor protein tyrosine kinase signaling pathway", 3.976, 6.886, 4.332, 2.839,-7.5986,0.845,0.114),
c("GO:0032101","regulation of response to external stimulus", 3.976, 6.545, 2.631, 2.839,-9.0405,0.838,0.114),
c("GO:0048519","negative regulation of biological process",26.855,-0.542, 7.989, 3.668,-12.9469,0.898,0.134),
c("GO:0044238","primary metabolic process",60.306,-3.339, 6.565, 4.019,-4.5229,0.961,0.140),
c("GO:0042221","response to chemical",23.993, 7.702,-0.115, 3.619,-24.7399,0.923,0.160),
c("GO:0009628","response to abiotic stimulus", 6.705, 8.008, 1.525, 3.066,-11.1726,0.938,0.174),
c("GO:0007187","G-protein coupled receptor signaling pathway, coupled to cyclic nucleotide second messenger", 1.033, 5.463, 5.279, 2.255,-5.0942,0.874,0.186),
c("GO:0031099","regeneration", 1.016,-3.828,-4.295, 2.248,-4.6216,0.955,0.186),
c("GO:0009719","response to endogenous stimulus", 9.175, 6.984, 0.467, 3.202,-15.8069,0.935,0.188),
c("GO:0098657","import into cell", 0.415,-3.347, 2.843, 1.863,-4.7077,0.928,0.190),
c("GO:0051239","regulation of multicellular organismal process",15.268,-6.085, 3.408, 3.423,-18.1433,0.885,0.192),
c("GO:0007268","chemical synaptic transmission", 3.474, 5.008, 6.277, 2.780,-4.7471,0.914,0.194),
c("GO:0050794","regulation of cellular process",60.427, 0.909, 7.170, 4.020,-15.1986,0.875,0.198),
c("GO:0009605","response to external stimulus",12.043, 7.990, 0.686, 3.320,-7.7520,0.932,0.202),
c("GO:0019222","regulation of metabolic process",35.730,-0.798, 6.924, 3.792,-8.7399,0.888,0.217),
c("GO:0048589","developmental growth", 3.272,-5.765,-3.288, 2.754,-4.6289,0.942,0.220),
c("GO:0048729","tissue morphogenesis", 3.572,-4.454,-4.112, 2.792,-4.2055,0.942,0.223),
c("GO:0048732","gland development", 2.418,-5.936,-2.151, 2.623,-3.9830,0.930,0.224),
c("GO:0032879","regulation of localization",14.409,-3.921, 4.876, 3.398,-19.9830,0.839,0.231),
c("GO:0006950","response to stress",21.310, 7.271,-0.252, 3.567,-11.7905,0.925,0.241),
c("GO:0048583","regulation of response to stimulus",21.610, 7.157, 2.071, 3.574,-14.0640,0.849,0.242),
c("GO:0031323","regulation of cellular metabolic process",33.837, 1.983, 7.193, 3.768,-7.3665,0.804,0.256),
c("GO:0003008","system process",11.575,-6.863, 1.039, 3.303,-14.5229,0.965,0.259),
c("GO:0050789","regulation of biological process",63.456, 0.625, 7.037, 4.041,-16.1244,0.896,0.274),
c("GO:0051899","membrane depolarization", 0.565,-1.372,-6.528, 1.996,-4.2411,0.910,0.284),
c("GO:0061024","membrane organization", 7.299,-5.167, 4.037, 3.102,-4.4237,0.987,0.286),
c("GO:0035150","regulation of tube size", 0.675,-0.489,-6.505, 2.072,-5.1129,0.901,0.290),
c("GO:0009636","response to toxic substance", 1.264, 6.039,-4.714, 2.342,-5.3072,0.915,0.292),
c("GO:0061041","regulation of wound healing", 0.750, 5.568, 3.354, 2.117,-5.7696,0.869,0.296),
c("GO:0007165","signal transduction",33.618, 5.808, 3.554, 3.765,-20.2403,0.784,0.296),
c("GO:0010038","response to metal ion", 1.829, 6.463,-4.185, 2.502,-5.0768,0.911,0.307),
c("GO:0001101","response to acid chemical", 1.858, 6.221,-3.913, 2.509,-5.3072,0.911,0.308),
c("GO:0043066","negative regulation of apoptotic process", 4.749,-4.234,-2.719, 2.916,-5.6778,0.845,0.315),
c("GO:0042493","response to drug", 2.366, 6.792,-3.628, 2.614,-12.6716,0.908,0.319),
c("GO:0010035","response to inorganic substance", 2.816, 6.714,-3.232, 2.689,-6.8125,0.906,0.327),
c("GO:0048856","anatomical structure development",31.558,-4.976,-4.080, 3.738,-12.4157,0.940,0.339),
c("GO:0051716","cellular response to stimulus",40.358, 7.607, 0.187, 3.845,-21.1158,0.911,0.359),
c("GO:0055082","cellular chemical homeostasis", 4.108,-1.327,-6.764, 2.853,-9.7520,0.840,0.364),
c("GO:0007267","cell-cell signaling", 9.025, 5.287, 5.987, 3.195,-5.1175,0.925,0.366),
c("GO:0043408","regulation of MAPK cascade", 3.883, 5.837, 4.526, 2.829,-6.8794,0.689,0.371),
c("GO:0048522","positive regulation of cellular process",27.732,-1.038, 7.337, 3.682,-15.2518,0.798,0.376),
c("GO:0010817","regulation of hormone levels", 2.764,-0.848,-6.705, 2.681,-6.8697,0.892,0.376),
c("GO:0050878","regulation of body fluid levels", 2.862,-1.665,-6.559, 2.696,-4.2541,0.891,0.378),
c("GO:1901654","response to ketone", 1.039, 5.708,-4.850, 2.258,-5.2644,0.898,0.394));
one.data <- data.frame(revigo.data);
names(one.data) <- revigo.names;
one.data <- one.data [(one.data$plot_X != "null" & one.data$plot_Y != "null"), ];
one.data$plot_X <- as.numeric( as.character(one.data$plot_X) );
one.data$plot_Y <- as.numeric( as.character(one.data$plot_Y) );
one.data$plot_size <- as.numeric( as.character(one.data$plot_size) );
one.data$log10_p_value <- as.numeric( as.character(one.data$log10_p_value) );
one.data$frequency <- as.numeric( as.character(one.data$frequency) );
one.data$uniqueness <- as.numeric( as.character(one.data$uniqueness) );
one.data$dispensability <- as.numeric( as.character(one.data$dispensability) );
p1 <- ggplot( data = one.data );
p1 <- p1 + geom_point( aes( plot_X, plot_Y, colour = log10_p_value, size = plot_size), alpha = I(0.6) ) + scale_size_area();
p1 <- p1 + scale_colour_gradientn( colours = c("blue", "green", "yellow", "red"), limits = c( min(one.data$log10_p_value), 0) );
p1 <- p1 + geom_point( aes(plot_X, plot_Y, size = plot_size), shape = 21, fill = "transparent", colour = I (alpha ("black", 0.6) )) + scale_size_area();
p1 <- p1 + scale_size( range=c(5, 30)) + theme_bw(); # + scale_fill_gradientn(colours = heat_hcl(7), limits = c(-300, 0) );
ex <- one.data [ one.data$dispensability < 0.15, ];
p1 <- p1 + geom_text( data = ex, aes(plot_X, plot_Y, label = description), colour = I(alpha("black", 0.85)), size = 3 );
p1 <- p1 + labs (y = "semantic space x", x = "semantic space y");
p1 <- p1 + theme(legend.key = element_blank()) ;
one.x_range = max(one.data$plot_X) - min(one.data$plot_X);
one.y_range = max(one.data$plot_Y) - min(one.data$plot_Y);
p1 <- p1 + xlim(min(one.data$plot_X)-one.x_range/10,max(one.data$plot_X)+one.x_range/10);
p1 <- p1 + ylim(min(one.data$plot_Y)-one.y_range/10,max(one.data$plot_Y)+one.y_range/10);
p1
抱歉，代码很忙，这是网站为我提供的，所以这是我的起点 - 任何关于库的指导或任何我可以用来弄清楚如何突出显示此 ggplot 中的较小分组的任何东西，我们将不胜感激.
编辑：我还研究了 k-means 聚类，它给我一些分组来尝试使用 -
进行可视化
xy <- dplyr::select(one.data, plot_X, plot_Y)
res.km <- kmeans(scale(xy), 3, nstart = 25)
我期望重叠分组看起来像一个制作糟糕且不准确的示例，但如果我使用 k-means 来识别 3 个组，那么我试图查看的 style/type 情节将看起来像：
我可以看到 res.km <- kmeans(scale(xy), 3, nstart = 25) 给出了 1-3 之间的每一行簇有没有办法突出显示这样的组？
将它们分组的一种方法是使用不同的形状。
# Take the cluster from kmean result
one.data$cluster <- factor(res.km$cluster)
# define shape_manual to provided to scale_shape_manual
shape_manual <- c(21, 22, 23)
names(shape_manual) <- levels(one.data$cluster)
ggplot( data = one.data ) +
# mapping shape with cluster
# As shape 21-23 have both fill & colour I am using fill here
geom_point( aes( plot_X, plot_Y, fill = log10_p_value,
size = plot_size, shape = cluster),
alpha = I(0.6) ) + scale_size_area() +
geom_point( aes(plot_X, plot_Y, size = plot_size, shape = cluster),
fill = "transparent", colour = I (alpha ("black", 0.6) )) + scale_size_area() +
scale_fill_gradientn( colours = c("blue", "green", "yellow", "red"),
limits = c( min(one.data$log10_p_value), 0) ) +
# Define the manual scales for shapes with shape_manual define earlier
scale_shape_manual(values = shape_manual) +
scale_size( range=c(5, 30)) + theme_bw() +
geom_text( data = ex, aes(plot_X, plot_Y, label = description),
colour = I(alpha("black", 0.85)), size = 3 ) +
labs (y = "semantic space x", x = "semantic space y") +
theme(legend.key = element_blank()) +
xlim(min(one.data$plot_X)-one.x_range/10,max(one.data$plot_X)+one.x_range/10) +
ylim(min(one.data$plot_Y)-one.y_range/10,max(one.data$plot_Y)+one.y_range/10)
输出
ggforce 提供了一些符合要求的分组几何：
one.data$cluster = res.km$cluster
#...all your code above...
p1 + ggforce::geom_mark_ellipse(aes(plot_X, plot_Y, group = cluster, label = cluster))
[可以省略“label = cluster”部分以单独获得省略号。]
为了更详细地描述组，在这种情况下，您还可以尝试 ggforce::geom_mark_hull，它可以让您绘制一个更接近集群轮廓的凸包：
p1 + ggforce::geom_mark_hull(aes(plot_X, plot_Y, group = cluster, label = cluster), expand = unit(10, "mm"), radius = unit(10, "mm"), concavity = 1.8)
我使用一个名为 Revigo 的网站制作了一个情节，该网站提供了一个 R 脚本（包含在下面）来创建这样的情节：
我想看看是否可以在同一张图中的这些点之上执行和可视化聚类？因为这个图看起来像一个大圆圈，所以我想看看里面是否有我可以突出显示的更小的分组。我有生物学背景，所以我不确定从哪里开始尝试在同一个情节中获得这种可视化效果。我已经使用 hclust() 进行了探索，但我不知道将集群显示在该图顶部的步骤。
给出上图的代码和数据是：
library( ggplot2 );
library( scales );
revigo.names <- c("term_ID","description","frequency_%","plot_X","plot_Y","plot_size","log10_p_value","uniqueness","dispensability");
revigo.data <- rbind(c("GO:0002376","immune system process",16.463,-0.302,-3.807, 3.455,-8.3307,0.995,0.000),
c("GO:0006928","movement of cell or subcellular component",10.987, 1.052, 1.113, 3.280,-8.8153,0.965,0.000),
c("GO:0007610","behavior", 3.254,-1.620, 0.960, 2.752,-4.0048,0.994,0.000),
c("GO:0008150","biological_process",100.000,-6.029,-0.499, 4.239,-8.7447,1.000,0.000),
c("GO:0009987","cellular process",90.329,-5.288, 0.130, 4.195,-10.0701,0.999,0.000),
c("GO:0010243","response to organonitrogen compound", 4.697, 6.870,-2.756, 2.911,-12.7100,0.865,0.000),
c("GO:0023052","signaling",36.613, 2.976,-6.718, 3.803,-7.1931,0.996,0.000),
c("GO:0032501","multicellular organismal process",41.143, 3.761,-0.715, 3.853,-17.2741,0.997,0.000),
c("GO:0032502","developmental process",33.982,-2.865,-0.660, 3.770,-14.4202,0.996,0.000),
c("GO:0034762","regulation of transmembrane transport", 2.452,-3.982, 3.261, 2.629,-13.4123,0.788,0.000),
c("GO:0040007","growth", 5.447, 0.651,-0.889, 2.975,-4.2140,0.995,0.000),
c("GO:0040011","locomotion", 9.452, 2.305,-0.865, 3.215,-8.1068,0.995,0.000),
c("GO:0050896","response to stimulus",49.302, 3.556,-5.122, 3.932,-17.7852,0.997,0.000),
c("GO:0051179","localization",36.018,-0.564,-0.041, 3.795,-8.9245,0.996,0.000),
c("GO:0051704","multi-organism process", 9.873, 1.677,-5.787, 3.234,-4.6925,0.995,0.000),
c("GO:0065007","biological regulation",67.069,-4.157, 0.147, 4.065,-15.5918,0.998,0.000),
c("GO:0007154","cell communication",36.705,-0.841,-2.282, 3.804,-8.3595,0.992,0.006),
c("GO:0008283","cell proliferation",11.321, 2.644, 1.154, 3.293,-4.5376,0.982,0.024),
c("GO:0042391","regulation of membrane potential", 2.204,-0.568,-6.816, 2.583,-10.6073,0.895,0.035),
c("GO:0043086","negative regulation of catalytic activity", 4.934, 4.164,-2.157, 2.932,-4.9172,0.880,0.039),
c("GO:0019216","regulation of lipid metabolic process", 1.685, 3.142, 6.777, 2.467,-6.3215,0.880,0.043),
c("GO:0044057","regulation of system process", 2.862,-6.608, 1.584, 2.696,-13.1469,0.868,0.046),
c("GO:0051093","negative regulation of developmental process", 4.720,-4.950,-3.614, 2.913,-9.9031,0.830,0.050),
c("GO:0006793","phosphorus metabolic process",18.702,-4.363, 6.140, 3.511,-6.8729,0.961,0.053),
c("GO:0008284","positive regulation of cell proliferation", 4.859,-0.261, 4.128, 2.926,-9.2882,0.832,0.055),
c("GO:0065008","regulation of biological quality",20.202,-0.644, 5.306, 3.544,-20.3125,0.920,0.057),
c("GO:0050865","regulation of cell activation", 3.174, 2.497,-4.613, 2.741,-4.7878,0.917,0.062),
c("GO:0065009","regulation of molecular function",17.288,-1.972, 4.374, 3.477,-17.1959,0.923,0.078),
c("GO:0048518","positive regulation of biological process",30.969, 0.597, 8.033, 3.730,-15.2993,0.895,0.095),
c("GO:0051128","regulation of cellular component organization",13.335, 1.445, 4.799, 3.364,-4.5003,0.899,0.104),
c("GO:0007169","transmembrane receptor protein tyrosine kinase signaling pathway", 3.976, 6.886, 4.332, 2.839,-7.5986,0.845,0.114),
c("GO:0032101","regulation of response to external stimulus", 3.976, 6.545, 2.631, 2.839,-9.0405,0.838,0.114),
c("GO:0048519","negative regulation of biological process",26.855,-0.542, 7.989, 3.668,-12.9469,0.898,0.134),
c("GO:0044238","primary metabolic process",60.306,-3.339, 6.565, 4.019,-4.5229,0.961,0.140),
c("GO:0042221","response to chemical",23.993, 7.702,-0.115, 3.619,-24.7399,0.923,0.160),
c("GO:0009628","response to abiotic stimulus", 6.705, 8.008, 1.525, 3.066,-11.1726,0.938,0.174),
c("GO:0007187","G-protein coupled receptor signaling pathway, coupled to cyclic nucleotide second messenger", 1.033, 5.463, 5.279, 2.255,-5.0942,0.874,0.186),
c("GO:0031099","regeneration", 1.016,-3.828,-4.295, 2.248,-4.6216,0.955,0.186),
c("GO:0009719","response to endogenous stimulus", 9.175, 6.984, 0.467, 3.202,-15.8069,0.935,0.188),
c("GO:0098657","import into cell", 0.415,-3.347, 2.843, 1.863,-4.7077,0.928,0.190),
c("GO:0051239","regulation of multicellular organismal process",15.268,-6.085, 3.408, 3.423,-18.1433,0.885,0.192),
c("GO:0007268","chemical synaptic transmission", 3.474, 5.008, 6.277, 2.780,-4.7471,0.914,0.194),
c("GO:0050794","regulation of cellular process",60.427, 0.909, 7.170, 4.020,-15.1986,0.875,0.198),
c("GO:0009605","response to external stimulus",12.043, 7.990, 0.686, 3.320,-7.7520,0.932,0.202),
c("GO:0019222","regulation of metabolic process",35.730,-0.798, 6.924, 3.792,-8.7399,0.888,0.217),
c("GO:0048589","developmental growth", 3.272,-5.765,-3.288, 2.754,-4.6289,0.942,0.220),
c("GO:0048729","tissue morphogenesis", 3.572,-4.454,-4.112, 2.792,-4.2055,0.942,0.223),
c("GO:0048732","gland development", 2.418,-5.936,-2.151, 2.623,-3.9830,0.930,0.224),
c("GO:0032879","regulation of localization",14.409,-3.921, 4.876, 3.398,-19.9830,0.839,0.231),
c("GO:0006950","response to stress",21.310, 7.271,-0.252, 3.567,-11.7905,0.925,0.241),
c("GO:0048583","regulation of response to stimulus",21.610, 7.157, 2.071, 3.574,-14.0640,0.849,0.242),
c("GO:0031323","regulation of cellular metabolic process",33.837, 1.983, 7.193, 3.768,-7.3665,0.804,0.256),
c("GO:0003008","system process",11.575,-6.863, 1.039, 3.303,-14.5229,0.965,0.259),
c("GO:0050789","regulation of biological process",63.456, 0.625, 7.037, 4.041,-16.1244,0.896,0.274),
c("GO:0051899","membrane depolarization", 0.565,-1.372,-6.528, 1.996,-4.2411,0.910,0.284),
c("GO:0061024","membrane organization", 7.299,-5.167, 4.037, 3.102,-4.4237,0.987,0.286),
c("GO:0035150","regulation of tube size", 0.675,-0.489,-6.505, 2.072,-5.1129,0.901,0.290),
c("GO:0009636","response to toxic substance", 1.264, 6.039,-4.714, 2.342,-5.3072,0.915,0.292),
c("GO:0061041","regulation of wound healing", 0.750, 5.568, 3.354, 2.117,-5.7696,0.869,0.296),
c("GO:0007165","signal transduction",33.618, 5.808, 3.554, 3.765,-20.2403,0.784,0.296),
c("GO:0010038","response to metal ion", 1.829, 6.463,-4.185, 2.502,-5.0768,0.911,0.307),
c("GO:0001101","response to acid chemical", 1.858, 6.221,-3.913, 2.509,-5.3072,0.911,0.308),
c("GO:0043066","negative regulation of apoptotic process", 4.749,-4.234,-2.719, 2.916,-5.6778,0.845,0.315),
c("GO:0042493","response to drug", 2.366, 6.792,-3.628, 2.614,-12.6716,0.908,0.319),
c("GO:0010035","response to inorganic substance", 2.816, 6.714,-3.232, 2.689,-6.8125,0.906,0.327),
c("GO:0048856","anatomical structure development",31.558,-4.976,-4.080, 3.738,-12.4157,0.940,0.339),
c("GO:0051716","cellular response to stimulus",40.358, 7.607, 0.187, 3.845,-21.1158,0.911,0.359),
c("GO:0055082","cellular chemical homeostasis", 4.108,-1.327,-6.764, 2.853,-9.7520,0.840,0.364),
c("GO:0007267","cell-cell signaling", 9.025, 5.287, 5.987, 3.195,-5.1175,0.925,0.366),
c("GO:0043408","regulation of MAPK cascade", 3.883, 5.837, 4.526, 2.829,-6.8794,0.689,0.371),
c("GO:0048522","positive regulation of cellular process",27.732,-1.038, 7.337, 3.682,-15.2518,0.798,0.376),
c("GO:0010817","regulation of hormone levels", 2.764,-0.848,-6.705, 2.681,-6.8697,0.892,0.376),
c("GO:0050878","regulation of body fluid levels", 2.862,-1.665,-6.559, 2.696,-4.2541,0.891,0.378),
c("GO:1901654","response to ketone", 1.039, 5.708,-4.850, 2.258,-5.2644,0.898,0.394));
one.data <- data.frame(revigo.data);
names(one.data) <- revigo.names;
one.data <- one.data [(one.data$plot_X != "null" & one.data$plot_Y != "null"), ];
one.data$plot_X <- as.numeric( as.character(one.data$plot_X) );
one.data$plot_Y <- as.numeric( as.character(one.data$plot_Y) );
one.data$plot_size <- as.numeric( as.character(one.data$plot_size) );
one.data$log10_p_value <- as.numeric( as.character(one.data$log10_p_value) );
one.data$frequency <- as.numeric( as.character(one.data$frequency) );
one.data$uniqueness <- as.numeric( as.character(one.data$uniqueness) );
one.data$dispensability <- as.numeric( as.character(one.data$dispensability) );
p1 <- ggplot( data = one.data );
p1 <- p1 + geom_point( aes( plot_X, plot_Y, colour = log10_p_value, size = plot_size), alpha = I(0.6) ) + scale_size_area();
p1 <- p1 + scale_colour_gradientn( colours = c("blue", "green", "yellow", "red"), limits = c( min(one.data$log10_p_value), 0) );
p1 <- p1 + geom_point( aes(plot_X, plot_Y, size = plot_size), shape = 21, fill = "transparent", colour = I (alpha ("black", 0.6) )) + scale_size_area();
p1 <- p1 + scale_size( range=c(5, 30)) + theme_bw(); # + scale_fill_gradientn(colours = heat_hcl(7), limits = c(-300, 0) );
ex <- one.data [ one.data$dispensability < 0.15, ];
p1 <- p1 + geom_text( data = ex, aes(plot_X, plot_Y, label = description), colour = I(alpha("black", 0.85)), size = 3 );
p1 <- p1 + labs (y = "semantic space x", x = "semantic space y");
p1 <- p1 + theme(legend.key = element_blank()) ;
one.x_range = max(one.data$plot_X) - min(one.data$plot_X);
one.y_range = max(one.data$plot_Y) - min(one.data$plot_Y);
p1 <- p1 + xlim(min(one.data$plot_X)-one.x_range/10,max(one.data$plot_X)+one.x_range/10);
p1 <- p1 + ylim(min(one.data$plot_Y)-one.y_range/10,max(one.data$plot_Y)+one.y_range/10);
p1
抱歉，代码很忙，这是网站为我提供的，所以这是我的起点 - 任何关于库的指导或任何我可以用来弄清楚如何突出显示此 ggplot 中的较小分组的任何东西，我们将不胜感激.
编辑：我还研究了 k-means 聚类，它给我一些分组来尝试使用 -
进行可视化xy <- dplyr::select(one.data, plot_X, plot_Y)
res.km <- kmeans(scale(xy), 3, nstart = 25)
我期望重叠分组看起来像一个制作糟糕且不准确的示例，但如果我使用 k-means 来识别 3 个组，那么我试图查看的 style/type 情节将看起来像：
我可以看到 res.km <- kmeans(scale(xy), 3, nstart = 25) 给出了 1-3 之间的每一行簇有没有办法突出显示这样的组？
将它们分组的一种方法是使用不同的形状。
# Take the cluster from kmean result
one.data$cluster <- factor(res.km$cluster)
# define shape_manual to provided to scale_shape_manual
shape_manual <- c(21, 22, 23)
names(shape_manual) <- levels(one.data$cluster)
ggplot( data = one.data ) +
# mapping shape with cluster
# As shape 21-23 have both fill & colour I am using fill here
geom_point( aes( plot_X, plot_Y, fill = log10_p_value,
size = plot_size, shape = cluster),
alpha = I(0.6) ) + scale_size_area() +
geom_point( aes(plot_X, plot_Y, size = plot_size, shape = cluster),
fill = "transparent", colour = I (alpha ("black", 0.6) )) + scale_size_area() +
scale_fill_gradientn( colours = c("blue", "green", "yellow", "red"),
limits = c( min(one.data$log10_p_value), 0) ) +
# Define the manual scales for shapes with shape_manual define earlier
scale_shape_manual(values = shape_manual) +
scale_size( range=c(5, 30)) + theme_bw() +
geom_text( data = ex, aes(plot_X, plot_Y, label = description),
colour = I(alpha("black", 0.85)), size = 3 ) +
labs (y = "semantic space x", x = "semantic space y") +
theme(legend.key = element_blank()) +
xlim(min(one.data$plot_X)-one.x_range/10,max(one.data$plot_X)+one.x_range/10) +
ylim(min(one.data$plot_Y)-one.y_range/10,max(one.data$plot_Y)+one.y_range/10)
输出
ggforce 提供了一些符合要求的分组几何：
one.data$cluster = res.km$cluster
#...all your code above...
p1 + ggforce::geom_mark_ellipse(aes(plot_X, plot_Y, group = cluster, label = cluster))
[可以省略“label = cluster”部分以单独获得省略号。]
为了更详细地描述组，在这种情况下，您还可以尝试 ggforce::geom_mark_hull，它可以让您绘制一个更接近集群轮廓的凸包：
p1 + ggforce::geom_mark_hull(aes(plot_X, plot_Y, group = cluster, label = cluster), expand = unit(10, "mm"), radius = unit(10, "mm"), concavity = 1.8)