用黑线分隔散点图点答案

【问题标题】：Delimit scatterplot dots with black line用黑线分隔散点图点
【发布时间】：2022-01-08 02:15:32
【问题描述】：

我有下表：


Sample  UMAP1   UMAP2   colors  tissue
C_A1    1.41771557752425    1.22200433730937    yellow2 Urine
C_A2    1.76055361418532    1.34108703383903    yellow2 Urine
C_A3    0.00750224109526362 2.73864657429924    yellow2 Urine
C_A4    1.6500702891979 1.08443364179582    yellow2 Urine
C_A5    1.47433412636331    1.24338667538958    yellow2 Urine
C_A9    1.81676319795816    1.41560757063772    yellow2 Urine
C_B1    1.58085792476313    1.06578750509132    yellow2 Urine
C_B2    0.54619869458667    1.47703917280933    yellow2 Urine
CKD_C1  1.77876535894167    2.1866355905902 yellow2 Urine
CKD_C2  1.5528582393691 1.56720266537748    yellow2 Urine
CKD_C3  0.00527727382088514 2.62619847720534    yellow2 Urine
CKD_C4  0.586308172828281   2.7784840588712 yellow2 Urine
CKD_C5  0.186228467054963   2.6796009437192 yellow2 Urine
PYL_E1  -3.07772498762527   4.45095110253223    yellow2 Urine
PYL_E2  1.1175301960485 1.06349707623584    yellow2 Urine
PYL_E3  1.82042047858787    1.42547383706531    yellow2 Urine
PYL_E4  1.66009595777039    1.72432475907437    yellow2 Urine
PYL_E5  1.13056114256336    1.13760951795915    yellow2 Urine
PYL_E6  1.79463825137017    1.47814506344302    yellow2 Urine
PYL_E7  0.622690635767324   -1.96794491349824   yellow2 Urine
PYL_E8  0.63118627267307    1.29871116070517    yellow2 Urine
PYL_E9  -4.58033059073059   3.96133161881786    yellow2 Urine
PYL_F1  1.51666575038286    1.59237710006909    yellow2 Urine
UO_D1   0.709257524588014   1.24974065107936    yellow2 Urine
UO_D2   1.69833621648934    2.18100899996296    yellow2 Urine
UO_D3   1.01987315310411    1.08349161107457    yellow2 Urine
UO_D4   1.53357549894633    1.46876538726954    yellow2 Urine
UO_D5   0.618372187901223   1.1600432701727 yellow2 Urine
UO_D6   1.80740300575174    1.93028754079873    yellow2 Urine
UO_D7   -1.90935774520858   2.21114064992169    yellow2 Urine
SBU_G1  0.0684949931212032  2.6323213276562 yellow2 Urine
SBU_G2  1.37378744895387    1.15990665575179    yellow2 Urine
SBU_G3  0.681350048991714   1.29504193314325    yellow2 Urine
SBU_G4  0.585511550079965   1.46021542902406    yellow2 Urine
SBU_G5  2.13609698314454    -1.52225184779846   yellow2 Urine
BR1.RPM.    -3.28586643197682   3.74476178876053    darkgrey    Brain
BR2.RPM.    -3.66581644761413   3.78800081614875    darkgrey    Brain
BR3.RPM.    -3.96475462680686   3.8275429988704 darkgrey    Brain
BR4.RPM.    -3.6558646156002    3.90023979275899    darkgrey    Brain
BR5.RPM.    -3.77917418926683   3.65892915155001    darkgrey    Brain
CO1.RPM.    1.19249345974165    -1.0914160958934    forestgreen Colon
CO2.RPM.    0.853658643619059   -0.658496441780366  forestgreen Colon
CO3.RPM.    0.975760223303845   -1.28665248361392   forestgreen Colon
CO5.RPM.    1.36511790751794    -2.13567084452923   forestgreen Colon
DU1.RPM.    1.63592949492624    -2.46405219183458   olivedrab4  Duodenum
DU2.RPM.    1.87925711931471    -3.03633498025664   olivedrab4  Duodenum
DU5.RPM.    1.37754729522807    -0.478401566279114  olivedrab4  Duodenum
HE1.RPM.    -1.84787050174421   3.29683025222076    hotpink3    Heart
HE2.RPM.    -1.57712374669721   3.72779965312866    hotpink3    Heart
HE3.RPM.    -1.62595902698841   3.4606008066362 hotpink3    Heart
HE4.RPM.    -0.907119493708492  3.17316852377154    hotpink3    Heart
HE5.RPM.    -1.65644593797311   3.82195960352629    hotpink3    Heart
IL1.RPM.    1.44153778801558    -1.02823513385223   palegreen3  Ileum
IL2.RPM.    1.4889578583487 -2.2804483432248    palegreen3  Ileum
IL3.RPM.    1.91342848325147    -2.10569098957249   palegreen3  Ileum
IL4.RPM.    0.962529810173936   -0.757115204974117  palegreen3  Ileum
JE1.RPM.    1.93437797225407    -2.65674160788117   darkseagreen    Jejunum
JE2.RPM.    2.06996643344844    -2.78640061968243   darkseagreen    Jejunum
JE3.RPM.    1.62706829576533    -2.79624294732553   darkseagreen    Jejunum
JE4.RPM.    1.0825326758516 -2.76470251848877   darkseagreen    Jejunum
JE5.RPM.    1.38342677881686    -2.6755546386966    darkseagreen    Jejunum
KI1.RPM.    0.967578089848615   1.67184647007406    orange3 Kidney
KI2.RPM.    1.13378641637209    1.34124080803019    orange3 Kidney
KI3.RPM.    1.37130716354727    1.80996835872441    orange3 Kidney
KI4.RPM.    1.07772033561742    1.64994468114772    orange3 Kidney
KI5.RPM.    1.0955496159798 2.0869739595772 orange3 Kidney
LI1.RPM.    1.682084870402  -6.14052679905332   sienna  Liver
LI2.RPM.    1.59906448215381    -6.29131805082633   sienna  Liver
LI3.RPM.    1.45802709716468    -6.13448078364028   sienna  Liver
LI4.RPM.    1.95665039443957    -6.5130493984287    sienna  Liver
LI5.RPM.    1.80607410919614    -6.35273035030961   sienna  Liver
LU1.RPM.    -2.52491770797571   3.4865019812581 mediumpurple    Lung
LU2.RPM.    -2.86699310757743   3.34528752430189    mediumpurple    Lung
LU3.RPM.    -2.89232280631143   2.95189454920787    mediumpurple    Lung
LU4.RPM.    -2.66332768628549   3.09050672631981    mediumpurple    Lung
LU5.RPM.    -3.08812503849215   3.25327915431485    mediumpurple    Lung
PA1.RPM.    1.29849330321564    -5.33710072865804   dodgerblue  Pancreas
PA2.RPM.    1.19873085339097    -5.26535178376946   dodgerblue  Pancreas
PA3.RPM.    1.11108191265227    -5.14709232997355   dodgerblue  Pancreas
PA5.RPM.    0.895423334114921   -4.88663713272289   dodgerblue  Pancreas
PL1.RPM.    -1.06236761107225   3.49621079235936    red3    Blood
PL2.RPM.    -1.07888887772428   3.04101229356896    red3    Blood
PL3.RPM.    -0.844686506146655  3.23004986615368    red3    Blood
PL5.RPM.    -0.720030063721917  3.49005127885431    red3    Blood
SKI.RPM.    0.199109542570694   2.44218482056362    pink2   Skin
SM1.RPM.    0.563134748949276   2.02657901642232    plum    Muscle
SM2.RPM.    0.340223402531866   2.27754829463276    plum    Muscle
SM3.RPM.    0.770024511959072   1.97953000238431    plum    Muscle

加载表格后，请记得按要求对颜色和组织因子进行排序：


Cat_urine$colors <- factor(Cat_urine$colors, levels=c("yellow2","darkgrey", "forestgreen", "olivedrab4", "palegreen3", "darkseagreen", "red3", "hotpink3", "plum", "pink2",
                                                        "orange3", "sienna", "mediumpurple", "dodgerblue"))
Cat_urine$tissue <- factor(Cat_urine$tissue, levels=c("Urine","Brain", "Colon", "Duodenum", "Jejunum", "Ileum", "Plasma", "Heart", "Skeletal muscle", "Skin",
                                                        "Kidney", "Liver", "Lungs", "Pancreas"))

还有下面的ggplot2代码：


ggplot(Cat_urine, aes(x=UMAP1, y=UMAP2, color=colors)) + 
  geom_point(size=5) + 
  scale_color_manual(labels = c("Urine","Brain", "Colon", "Duodenum", "Jejunum", "Ileum", "Plasma", "Heart", "Skeletal muscle", "Skin",
                                "Kidney", "Liver", "Lungs", "Pancreas", "Urine"),
                     values = c("yellow2","darkgrey", "forestgreen", "olivedrab4", "palegreen3", "darkseagreen", "red3", "hotpink3", "plum", "pink2",
                                "orange3", "sienna", "mediumpurple", "dodgerblue")) +
  guides(colour = guide_legend(override.aes = list(size=5))) + 
  ylim(c(-7,5)) + xlim(c(-5,2.5)) + guides(shape = guide_legend(override.aes = list(size = 4))) +
  theme_classic() + theme(axis.text.x = element_text(size=10), axis.title=element_text(size=12), 
                     axis.text.y =element_text(size=10),plot.margin = margin(1, 0.5, 0.5, 1, "cm")) + 
  xlab("\nComponent 1") + ylab("Component 2\n") +
  theme(legend.position = c(0.295, 0.25), legend.title = element_blank(),
                                                         legend.background = element_rect(fill = "white", color = "black"))

生成此图：

我想用黑色圆形线来划分每个点，而不仅仅是现在的颜色，以便每个样本之间的差异变得更加明显，主要集中在部分重叠的那些。我尝试了几种引入和更改 scale_fill_manual() 和 scale_color_manual() 的方法，但它以某种方式搞砸了颜色分配......

谁能给点提示？

非常感谢

【问题讨论】：

我认为您可以将aes(..., fill = colors) 与geom_point(shape = 21, size = 5) 结合使用。

标签： r ggplot2 line sample

【解决方案1】：

您可以使用以下代码在圆点周围添加轮廓。您需要为这些点指定填充，否则它们将使用fill = colors 变得透明。我不确定你想要什么颜色的轮廓，所以我给点一个黑色的轮廓。此外，如果您尝试使用数据集中的颜色作为常规颜色，ggplot2 实际上会意识到填充和轮廓是相同的颜色，并会尝试更改轮廓。主要是因为具有相同颜色的轮廓和填充是没有意义的。这将导致创建第二个图例。

这里的一个问题是您将Urine 列为标签两次，因此很少有点用yellow2 着色，其余点用dodgerblue 着色

ggplot(Cat_urine, aes(x=UMAP1, y=UMAP2,fill=colors)) + 
  geom_point(size=5, shape=21, color="black") + 
  scale_fill_manual(labels = c("Urine","Brain", "Colon", "Duodenum", "Jejunum", "Ileum", "Plasma", "Heart", "Skeletal muscle", "Skin",
                                "Kidney", "Liver", "Lungs", "Pancreas", "Urine"),
                     values = c("yellow2","darkgrey", "forestgreen", "olivedrab4", "palegreen3", "darkseagreen", "red3", "hotpink3", "plum", "pink2",
                                "orange3", "sienna", "mediumpurple", "dodgerblue")) +
  guides(colour = guide_legend(override.aes = list(size=5))) + 
  ylim(c(-7,5)) + xlim(c(-5,2.5)) + guides(shape = guide_legend(override.aes = list(size = 4))) +
  theme_classic() + theme(axis.text.x = element_text(size=10), axis.title=element_text(size=12), 
                          axis.text.y =element_text(size=10),plot.margin = margin(1, 0.5, 0.5, 1, "cm")) + 
  xlab("\nComponent 1") + ylab("Component 2\n") +
  theme(legend.position = c(0.295, 0.25), legend.title = element_blank(),
        legend.background = element_rect(fill = "white", color = "black"))

【讨论】：

解决了围绕点的黑线，是的，但是用另一个自动颜色分配删除了我的颜色分配...我想要这个相同的图，但每个样本分配正确的颜色...跨度>
@EmilioMármolSánchez 我在您的数据中注意到的一个问题是，您将Urine 列为两种不同的颜色yellow2 和dodgerblue。你对我情节中的颜色是正确的，但我正在努力让代码与重复的标签一起工作

【解决方案2】：

我解决了：


ggplot(Cat_urine, aes(x=UMAP1, y=UMAP2, color="black", fill=colors)) + 
  geom_point(size=5, shape=21, color="black") + 
  scale_fill_manual(labels = c("Urine","Brain", "Colon", "Duodenum", "Jejunum", "Ileum", "Plasma", "Heart", "Skeletal muscle", "Skin",
                                "Kidney", "Liver", "Lungs", "Pancreas"),
                     values = c("yellow2","darkgrey", "forestgreen", "olivedrab4", "palegreen3", "darkseagreen", "red3", "hotpink3", "plum", "pink2",
                                "orange3", "sienna", "mediumpurple", "dodgerblue")) +
  guides(fill = guide_legend(ncol=2, override.aes = list(size=5))) + 
  ylim(c(-7,5)) + xlim(c(-5,2.5)) +
  theme_classic() + theme(axis.text.x = element_text(size=10), axis.title=element_text(size=12), 
                     axis.text.y =element_text(size=10),plot.margin = margin(1, 0.5, 0.5, 1, "cm")) + 
  xlab("\nComponent 1") + ylab("Component 2\n") +
  theme(legend.position = c(0.295, 0.25), legend.title = element_blank(),
                                                         legend.background = element_rect(fill = "white", color = "black"))

【讨论】：

您提供的数据不产生此图
编辑了代码。对我来说现在是这样
您有实际数据，所以我不会争论，但使用更新表和您刚刚在答案中更新的代码不会产生您在答案中发布的确切图表
我添加了一个答案，指定您需要首先将颜色和组织分类为因素。对不起...