{"id":27819,"date":"2024-02-02T16:35:54","date_gmt":"2024-02-02T08:35:54","guid":{"rendered":"http:\/\/www.biocloudservice.com\/wordpress\/?p=27819"},"modified":"2024-02-02T16:35:56","modified_gmt":"2024-02-02T08:35:56","slug":"%e4%b8%80%e7%af%87%e5%b0%b1%e5%a4%9f%e4%ba%86%ef%bc%81scatterplot3d%e5%8c%85%e7%bb%88%e6%9e%81%e6%8c%87%e5%8d%97%ef%bc%8c%e8%ae%a9%e4%bd%a0%e7%9a%84%e4%ba%8c%e7%bb%b4%e6%95%b0%e6%8d%ae","status":"publish","type":"post","link":"http:\/\/www.biocloudservice.com\/wordpress\/?p=27819","title":{"rendered":"\u4e00\u7bc7\u5c31\u591f\u4e86\uff01scatterplot3D\u5305\u7ec8\u6781\u6307\u5357\uff0c\u8ba9\u4f60\u7684\u4e8c\u7ef4\u6570\u636e\u2018\u7acb\u2019\u8d77\u6765"},"content":{"rendered":"

\u5404\u4f4d\u5c0f\u4f19\u4f34\uff0c\u5927\u5bb6\u597d\u554a\uff0c\u5f88\u9ad8\u5174\u5c0f\u679c\u53c8\u548c\u5927\u5bb6\u89c1\u9762\u4e86\uff0c\u5176\u5b9e\u6709\u4e00\u4e2a\u95ee\u9898\u4e00\u76f4\u56f0\u6270\u7740\u6211\uff1a\u5c31\u50cf\u5927\u5bb6\u4e00\u76f4\u7528\u7684\u529f\u80fd\u5f3a\u5927\u7684ggplot2\u3001\u7b80\u5355\u7684\u70ed\u56fe\u5de5\u5177pheatmap\u8fd8\u6709\u753bcircle\u56fe\u7684R\u5305circlize\uff0c\u4ed6\u4eec\u867d\u7136\u80fd\u5c55\u793a\u5f88\u591a\u4fe1\u606f\uff0c\u4f46\u5f62\u5f0f\u5355\u4e00\uff0c\u4ec5\u80fd\u901a\u8fc7\u989c\u8272\u7684\u8c03\u8282\u6765\u6539\u53d8\u6574\u4e2a\u56fe\u7684\u683c\u8c03\uff0c\u90a3\u4f1a\u4e0d\u4f1a\u6709\u4e00\u6b3eR\u5305\u80fd\u4ee5\u4e00\u79cd\u7acb\u4f53\u7684\u5f62\u5f0f\uff0c\u6765\u5c55\u793a\u54b1\u4eec\u83b7\u5f97\u7684\u4e09\u4e2a\u57fa\u56e0\u7684\u76f8\u4e92\u4f5c\u7528\u6216\u8005\u662f\u4e09\u4e2aPCA\u6210\u5206\u6240\u5360\u6bd4\u4f8b\u5462\uff1f\u7b54\u6848\u662f\uff1a\u5f53\u7136\u5566\u3002\u4eca\u5929\u5c0f\u679c\u7ed9\u5927\u5bb6\u5e26\u6765\u7684\u8fd9\u4e2aR\u5305–scatterplot3d\u5c31\u80fd\u5b9e\u73b03\u4e2a\u53d8\u91cf\u7684\u76f8\u4e92\u4f5c\u7528\u54e6\u3002<\/p>\n

scatterplot3d\u662fR\u8bed\u8a00\u4e2d\u7528\u4e8e\u521b\u5efa3D\u6563\u70b9\u56fe\u7684\u5305\u3002\u8fd9\u4e2a\u5305\u63d0\u4f9b\u4e86\u4e00\u79cd\u53ef\u89c6\u5316\u6570\u636e\u7684\u65b9\u5f0f\uff0c\u80fd\u5e2e\u52a9\u5927\u5bb6\u5728\u4e09\u4e2a\u7ef4\u5ea6\u4e2d\u540c\u65f6\u63a2\u7d22\u6570\u636e\u70b9\u7684\u5206\u5e03\u4e0e\u5173\u7cfb\u54e6\u3002\u90a3\u4e48\u5c0f\u679c\u5728\u8fd9\u91cc\u5462\uff0c\u7ed9\u5927\u5bb6\u5217\u4e3e\u4e86\u4e00\u4e9bscatterplot3d\u5305\u7684\u4e00\u4e9b\u5e94\u7528\u573a\u666f\uff1a\u9996\u5148\u662f\u4f7f\u7528scatterplot3d\u5305\uff0c\u5927\u5bb6\u53ef\u4ee5\u5c06\u4e09\u7ef4\u6570\u636e\u4ee5\u6563\u70b9\u56fe\u7684\u5f62\u5f0f\u5448\u73b0\uff0c\u4ece\u800c\u66f4\u5168\u9762\u7684\u4e86\u89e3\u6570\u636e\u70b9\u5728\u4e09\u4e2a\u7ef4\u5ea6\u4e0a\u7684\u5206\u5e03\u60c5\u51b5\u3002\u5176\u6b21\u5927\u5bb6\u90fd\u77e5\u9053\u4e09\u7ef4\u6563\u70b9\u56fe\u6709\u52a9\u4e8e\u89c2\u5bdf\u4e0d\u540c\u53d8\u91cf\u4e4b\u95f4\u7684\u5173\u7cfb\uff0c\u5404\u4f4d\u5c0f\u4f19\u4f34\u53ef\u4ee5\u5c06\u4e0d\u540c\u53d8\u91cf\u6620\u5c04\u5230\u4e09\u7ef4\u7a7a\u95f4\u7684\u5750\u6807\u8f74\u4e0a\u54e6\uff0c\u5e76\u901a\u8fc7\u89c2\u5bdf\u6563\u70b9\u7684\u5206\u5e03\u60c5\u51b5\u5c31\u80fd\u4e86\u89e3\u4ed6\u4eec\u4e4b\u95f4\u7684\u8d8b\u52bf\u548c\u5173\u8054\u6027\u5566\u3002\u7136\u540e\uff0c\u5927\u5bb6\u901a\u8fc7\u89c2\u5bdf\u4e09\u7ef4\u6563\u70b9\u56fe\uff0c\u8fd8\u6709\u76ca\u4e8e\u53d1\u73b0\u6570\u636e\u4e2d\u7684\u5f02\u5e38\u503c\u6216\u79bb\u7fa4\u70b9\uff0c\u56e0\u4e3a\u4ed6\u4eec\u53ef\u80fd\u5728\u4e09\u7ef4\u7a7a\u95f4\u4e2d\u5448\u73b0\u51fa\u72ec\u7279\u7684\u6a21\u5f0f\uff0c\u8fd9\u80af\u5b9a\u662f\u6709\u5229\u4e8e\u6211\u4eec\u5bf9\u6570\u636e\u7684\u7b5b\u9009\u54e6\u3002\u8be5\u5305\u8fd8\u6709\u76ca\u4e8e\u89c2\u5bdf\u6570\u636e\u70b9\u662f\u5426\u5728\u4e09\u4e2a\u7ef4\u5ea6\u4e0a\u805a\u96c6\u6210\u7fa4\u6216\u7c7b\u522b\uff0c\u8fd9\u5bf9\u6211\u4eec\u7684\u5e73\u884c\u8bd5\u9a8c\u6216\u5206\u7ec4\u8ba8\u8bba\u5f88\u6709\u7528\u54e6\u3002\u6700\u540e\uff0c\u5982\u679c\u5927\u5bb6\u6709\u591a\u4e2a\u81ea\u53d8\u91cf\u548c\u4e00\u4e2a\u56e0\u53d8\u91cf\uff0c\u4e5f\u53ef\u4ee5\u4f7f\u7528\u4e09\u7ef4\u6563\u70b9\u56fe\u6765\u53ef\u89c6\u5316\u4ed6\u4eec\u4e4b\u95f4\u7684\u5173\u7cfb\uff0c\u8fdb\u800c\u4e86\u89e3\u53d8\u91cf\u5bf9\u56e0\u53d8\u91cf\u7684\u5f71\u54cd\u54e6\u3002<\/p>\n

\u90a3\u6839\u636e\u4ee5\u4e0a\u4ecb\u7ecd\uff0c\u76f8\u4fe1\u5927\u5bb6\u4e5f\u80fd\u6216\u591a\u6216\u5c11\u7684\u4e86\u89e3\u4e86\u4e0d\u540c\u6570\u636e\u4e4b\u95f4\u5177\u6709\u7740\u4e0d\u540c\u5173\u7cfb\uff0c\u800c\u9488\u5bf9\u57fa\u56e0\u7ec4\u6216\u8f6c\u5f55\u7ec4\u7b49\u5927\u6570\u636e\u7ec4\u7684\u4fe1\u606f\u7684\u63d0\u53d6\uff0c\u8fd8\u662f\u63a8\u8350\u5927\u5bb6\u4f7f\u7528\u670d\u52a1\u5668\u547d\u4ee4\u884c\u7684\u5f62\u5f0f\u8fdb\u884c\uff0c\u56e0\u4e3a\u8fd9\u4f1a\u66f4\u52a0\u7684\u65b9\u4fbf\u5feb\u6377\uff0c\u5982\u679c\u6ca1\u6709\u81ea\u5df1\u7684\u670d\u52a1\u5668\u6b22\u8fce\u8054\u7cfb\u6211\u4eec\u8fdb\u884c\u670d\u52a1\u5668\u79df\u8d41\u54e6~<\/p>\n

\u8bf4\u4e86\u8fd9\u4e48\u591a\uff0c\u8ba9\u6211\u4eec\u8fd9\u5c31\u5f00\u59cbscatterplot3d\u7684\u5b66\u4e60\u5427\uff01<\/p>\n

#\u5b89\u88c5R\u5305\u53ca\u8c03\u7528<\/p>\n

install.packages(‘scatterplot3d’)<\/p>\n

library(scatterplot3d)<\/p>\n

library(RColorBrewer)<\/p>\n

#\u8bfb\u5165\u6570\u636e<\/p>\n

data <- read.table(‘\u8f93\u5165\u6570\u636e.txt’,sep = ‘\\t’,row.names = 1,header = T)<\/p>\n

data<\/p>\n

<\/p>\n

#\u8bbe\u7f6e\u989c\u8272\uff0c\u56e0\u4e3a\u6bcf\u4e2a\u57fa\u56e0\u8bbe\u7f6e\u4e8610\u4e2a\u6570\u636e\uff0c\u56e0\u6b64\u9009\u53d6\u4e24\u4e2a\u8272\u7cfb<\/p>\n

Set1 <- brewer.pal(n=6,name = ‘Set1’)<\/p>\n

Set2 <- brewer.pal(n=6,name = ‘Set2’)<\/p>\n

Set <- unique(c(Set2,Set1))<\/p>\n

\"\u7535\u8111\u622a\u56fe\n\n\u63cf\u8ff0\u5df2\u81ea\u52a8\u751f\u6210\"<\/p>\n

#\u7b80\u5355\u51fa\u56fe<\/p>\n

P1 <- scatterplot3d(x = data$Gene1,y = data$Gene2,z = data$Gene3)<\/p>\n

\"\u56fe\u8868,<\/p>\n

#\u5bf9\u8f74\u6807\u9898\u7684\u8c03\u8282<\/p>\n

P2 <- scatterplot3d(x = data$Gene1,y = data$Gene2,z = data$Gene3,<\/p>\n

xlab = ‘Gene1’,ylab = ‘Gene2’,zlab = ‘Gene3’,<\/p>\n

main = ‘scatterplot3d’,sub = ‘test’)<\/p>\n

\"\u56fe\u8868,<\/p>\n

#\u5bf9\u5b57\u4f53\u5927\u5c0f\u8fdb\u884c\u8c03\u8282<\/p>\n

P3 <- scatterplot3d(x = data$Gene1,y = data$Gene2,z = data$Gene3,<\/p>\n

xlab = ‘Gene1’,ylab = ‘Gene2’,zlab = ‘Gene3’,<\/p>\n

main = ‘scatterplot3d’,sub = ‘test’,<\/p>\n

cex.lab = 2,cex.axis = 1,cex.symbols = 4, #\u5206\u522b\u662f\u8f74\u6807\u9898\uff0c\u8f74\u6807\u7b7e\u53ca\u70b9\u7684\u5927\u5c0f<\/p>\n

font.lab = 45)<\/p>\n

<\/p>\n

#\u5bf9\u7ebf\u8fdb\u884c\u8c03\u8282<\/p>\n

P4 <- scatterplot3d(x = data$Gene1,y = data$Gene2,z = data$Gene3,<\/p>\n

xlab = ‘Gene1’,ylab = ‘Gene2’,zlab = ‘Gene3’,main = ‘scatterplot3d’,sub = ‘test’,<\/p>\n

grid = T, #\u5e95\u9762\u662f\u5426\u6709\u6a2a\u7ebf<\/p>\n

box = F, #\u662f\u5426\u6dfb\u52a0\u5176\u4f59\u8fb9<\/p>\n

#x.ticklabs = colnames(data), #\u7528\u4e8e\u6307\u5b9a\u523b\u5ea6<\/p>\n

lty.grid = 3, #\u5e95\u9762\u7ebf\u7684\u7c7b\u578b<\/p>\n

lty.axis = 2, #\u5750\u6807\u8f74\u7ebf\u7684\u7c7b\u578b<\/p>\n

ylim = c(-2,2),xlim = c(-2,2),zlim = c(-2,2), #\u5404\u5750\u6807\u8f74\u7684\u957f\u5ea6<\/p>\n

col.grid = ‘grey’,col.axis = ‘grey’ #\u5206\u522b\u5bf9\u5e95\u7ebf\/\u5750\u6807\u8f74\u7684\u989c\u8272\u8fdb\u884c\u6539\u53d8<\/p>\n

)<\/p>\n

\"\u56fe\u793a\n\n\u4e2d\u5ea6\u53ef\u4fe1\u5ea6\u63cf\u8ff0\u5df2\u81ea\u52a8\u751f\u6210\"<\/p>\n

#\u7ed9\u5b9a\u5f62\u72b6\u53c2\u6570<\/p>\n

shape <- c(16,17)<\/p>\n

#\u5176\u4f59\u5c0f\u51fd\u6570<\/p>\n

P5 <- scatterplot3d(x = data$Gene1,y = data$Gene2,z = data$Gene3,<\/p>\n

xlab = ‘Gene1’,ylab = ‘Gene2’,zlab = ‘Gene3’,main = ‘scatterplot3d’,sub = ‘test’,<\/p>\n

pch = shape[factor(data$group)],color = Set[factor(data$group)], #\u70b9\u7684\u5f62\u72b6\u53ca\u989c\u8272<\/p>\n

cex.lab = 2,cex.axis = 1,cex.symbols = 2, #\u5206\u522b\u662f\u8f74\u6807\u9898\uff0c\u8f74\u6807\u7b7e\u53ca\u70b9\u7684\u5927\u5c0f<\/p>\n

ylim = c(-2,2.5),xlim = c(-2,2),zlim = c(-2,2), #\u5404\u5750\u6807\u8f74\u7684\u957f\u5ea6<\/p>\n

scale.y = 2, #y\u8f74\u5355\u4e2a\u65b9\u6846\u7684\u5bbd\u5ea6<\/p>\n

angle = 25, #\u957f\u65b9\u4f53\u6574\u4f53\u89d2\u5ea6<\/p>\n

lab.z = c(3,5),grid = T, #\u5e95\u9762\u662f\u5426\u6709\u6a2a\u7ebf<\/p>\n

box = F, #\u662f\u5426\u6dfb\u52a0\u5176\u4f59\u8fb9\u6846<\/p>\n

#\u8868\u793a\u70b9\u7684\u72b6\u6001\uff0c\u2018p\u2019\u662f\u53ea\u6709\u70b9\uff0c\u2018l\u2019\u662f\u53ea\u6709\u7ebf\uff0c\u2018h\u2019\u662f\u65e2\u6709\u70b9\u5230\u5e95\u9762\u7684\u5782\u7ebf\uff0c\u2018b\u2019\u662f\u70b9\u4e0e\u7ebf\u4e92\u76f8\u8fde\u63a5<\/p>\n

type = ‘h’,<\/p>\n

#x.ticklabs = colnames(data), #\u7528\u4e8e\u6307\u5b9a\u523b\u5ea6<\/p>\n

lty.grid = 3, #\u5e95\u9762\u7ebf\u7684\u7c7b\u578b<\/p>\n

lty.axis = 2, #\u5750\u6807\u8f74\u7ebf\u7684\u7c7b\u578b<\/p>\n

col.grid = ‘grey’,col.axis = ‘grey’,col.lab = ‘black’ #\u5206\u522b\u5bf9\u5e95\u7ebf\/\u5750\u6807\u8f74\u7684\u989c\u8272\u8fdb\u884c\u6539\u53d8<\/p>\n

)<\/p>\n

<\/p>\n

#\u6dfb\u52a0\u56fe\u4f8b\u548c\u6587\u672c\u4fe1\u606f\uff1a<\/p>\n

legend(“topright”, legend = c(“A”, “B”),<\/p>\n

col = Set, pch = c(16, 17))<\/p>\n

#\u524d\u9762\u7684\u6570\u5b57\u662f\u6839\u636e\u5e95\u7ebf\u7684\u6761\u6570\u6765\u51b3\u5b9a\u7684\u54e6\uff0c\u4f46\u662f\u4e2d\u95f4\u7684\u6570\u7684\u786e\u662f\u6839\u636ez\u8f74\u5750\u6807\u800c\u5b9a<\/p>\n

text(1.1,-0.2,1,labels = ‘sample00’) #\u5f53\u7136\u662f\u53ef\u4ee5\u6539\u53d8\u989c\u8272\u548c\u5927\u5c0f\u7684\u554a<\/p>\n

text(1,0.5,-0.3,labels = ‘samplexx’,col = ‘red4’,cex = 1.5)<\/p>\n

\"\u56fe\u8868,<\/p>\n

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