{"id":503,"date":"2023-07-11T00:00:56","date_gmt":"2023-07-11T00:00:56","guid":{"rendered":"https:\/\/yunshangtulv.com.cn\/?p=503"},"modified":"2023-07-10T15:21:21","modified_gmt":"2023-07-10T15:21:21","slug":"sci%e5%9b%be%e7%89%87%e5%a4%8d%e7%8e%b0%ef%bc%9a%e9%9d%a2%e7%a7%af%e5%9b%be","status":"publish","type":"post","link":"https:\/\/yunshangtulv.com.cn\/?p=503","title":{"rendered":"SCI\u56fe\u7247\u590d\u73b0\uff1a\u9762\u79ef\u56fe"},"content":{"rendered":"<h2>\u7701\u6d41\uff1a\u5b8c\u6574\u4ee3\u7801\u62c9\u5230\u6700\u540e<\/h2>\n<p>&nbsp;<\/p>\n<h2>\u9996\u5148\u6570\u636e\u8868\u683c\u8981\u6c42\uff1a<\/h2>\n<p>1. \u8d8b\u52bf\u6c14\u6ce1\u56fe\u6570\u636e\uff1a\u8fd9\u4e2a\u6570\u636e\u5e94\u8be5\u662f\u4e00\u4e2aCSV\u6587\u4ef6\uff0c\u5177\u6709\u4e09\u4e2a\u5217\u3002\u7b2c\u4e00\u5217\u5e94\u8be5\u662f\"Diameter\"\uff0c\u4ee3\u8868\u76f4\u5f84\uff0c\u5e94\u8be5\u662f\u6570\u5b57\u3002\u7b2c\u4e8c\u5217\u5e94\u8be5\u662f\"expression\"\uff0c\u4ee3\u8868\u8868\u8fbe\u91cf\uff0c\u4e5f\u5e94\u8be5\u662f\u6570\u5b57\u3002\u7b2c\u4e09\u5217\u5e94\u8be5\u662f\"group\"\uff0c\u4ee3\u8868\u7ec4\uff0c\u8fd9\u662f\u4e00\u4e2a\u5206\u7c7b\u53d8\u91cf\u3002\u8fd9\u4e2aCSV\u6587\u4ef6\u4e0d\u5e94\u8be5\u6709\u884c\u540d\u3002<\/p>\n<p>2. Nature\u56fe\u6570\u636e\uff1a\u8fd9\u4e2a\u6570\u636e\u4e5f\u5e94\u8be5\u662f\u4e00\u4e2aCSV\u6587\u4ef6\uff0c\u5177\u6709\u4e09\u4e2a\u5217\u3002\u7b2c\u4e00\u5217\u5e94\u8be5\u662f\"celltype\"\uff0c\u4ee3\u8868\u7ec6\u80de\u7c7b\u578b\uff0c\u8fd9\u662f\u4e00\u4e2a\u5206\u7c7b\u53d8\u91cf\u3002\u7b2c\u4e8c\u5217\u5e94\u8be5\u662f\"stage\"\uff0c\u4ee3\u8868\u9636\u6bb5\uff0c\u8fd9\u4e5f\u662f\u4e00\u4e2a\u5206\u7c7b\u53d8\u91cf\u3002\u7b2c\u4e09\u5217\u5e94\u8be5\u662f\"Freq\"\uff0c\u4ee3\u8868\u9891\u7387\uff0c\u5e94\u8be5\u662f\u6570\u5b57\u3002\u8fd9\u4e2aCSV\u6587\u4ef6\u4e0d\u5e94\u8be5\u6709\u884c\u540d\u3002<\/p>\n<p>\u4ee5\u4e0b\u662f\u8fd9\u4e24\u79cd\u6570\u636e\u7684\u793a\u4f8b\uff1a<\/p>\n<p>\u8d8b\u52bf\u6c14\u6ce1\u56fe\u6570\u636e\uff1a<\/p>\n<table>\n<thead>\n<tr>\n<th>Diameter<\/th>\n<th>expression<\/th>\n<th>group<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>50<\/td>\n<td>0.5<\/td>\n<td>A<\/td>\n<\/tr>\n<tr>\n<td>100<\/td>\n<td>0.6<\/td>\n<td>B<\/td>\n<\/tr>\n<tr>\n<td>150<\/td>\n<td>0.7<\/td>\n<td>C<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>Nature\u56fe\u6570\u636e\uff1a<\/p>\n<table>\n<thead>\n<tr>\n<th>celltype<\/th>\n<th>stage<\/th>\n<th>Freq<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Type1<\/td>\n<td>CS12<\/td>\n<td>0.2<\/td>\n<\/tr>\n<tr>\n<td>Type2<\/td>\n<td>CS13<\/td>\n<td>0.3<\/td>\n<\/tr>\n<tr>\n<td>Type3<\/td>\n<td>CS15<\/td>\n<td>0.5<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u8bf7\u6ce8\u610f\uff0c\u6240\u6709\u7684\u6570\u636e\u90fd\u5e94\u8be5\u662f\u5b8c\u6574\u7684\uff0c\u4e5f\u5c31\u662f\u8bf4\uff0c\u6ca1\u6709\u7f3a\u5931\u503c\u3002\u5982\u679c\u6709\u7f3a\u5931\u503c\uff0c\u90a3\u4e48\u53ef\u80fd\u4f1a\u5bfc\u81f4\u4ee3\u7801\u8fd0\u884c\u9519\u8bef\u3002<\/p>\n<h3>\u6b65\u9aa41\uff1a\u751f\u6210\u793a\u4f8b\u6570\u636e<\/h3>\n<p>\u9996\u5148\uff0c\u6211\u4f1a\u4e3a\u8fd9\u4e24\u79cd\u56fe\u5f62\u751f\u6210\u793a\u4f8b\u6570\u636e\u3002<\/p>\n<ol>\n<li>\u5bf9\u4e8e\u8d8b\u52bf\u6c14\u6ce1\u56fe\uff0c\u6570\u636e\u53ef\u80fd\u5305\u542b\u4ee5\u4e0b\u5217\uff1a\u201cDiameter\u201d\uff08\u76f4\u5f84\uff09\uff0c\u201cexpression\u201d\uff08\u8868\u8fbe\u91cf\uff09\u4ee5\u53ca\u201cgroup\u201d\uff08\u7ec4\u522b\uff09\u3002<\/li>\n<li>\u5bf9\u4e8enature\u56fe\uff0c\u6570\u636e\u53ef\u80fd\u5305\u542b\u4ee5\u4e0b\u5217\uff1a\u201ccelltype\u201d\uff08\u7ec6\u80de\u7c7b\u578b\uff09\uff0c\u201cstage\u201d\uff08\u9636\u6bb5\uff09\u548c\u201cFreq\u201d\uff08\u9891\u7387\uff09\u3002<\/li>\n<\/ol>\n<pre class=\"corepress-code-pre\"><code># \u521b\u5efa\u8d8b\u52bf\u6c14\u6ce1\u56fe\u6570\u636e\r\nset.seed(123)  # \u8bbe\u7f6e\u968f\u673a\u6570\u79cd\u5b50\u4ee5\u4fdd\u8bc1\u7ed3\u679c\u53ef\u590d\u73b0\r\ntrend_data &lt;- data.frame(\r\n  Diameter = sample(100:500, 200, replace = TRUE),\r\n  expression = runif(200, 0, 100),\r\n  group = factor(sample(LETTERS[1:4], 200, replace = TRUE))\r\n)\r\nhead(trend_data)  # \u6253\u5370\u524d\u51e0\u884c\u6570\u636e\r\n\r\n# \u521b\u5efanature\u6570\u636e\r\nnature_data &lt;- data.frame(\r\n  celltype = factor(rep(LETTERS[1:4], each = 50)),\r\n  stage = factor(sample(c(\"CS12\", \"CS13\", \"CS15\", \"CS17\", \"CS20\"), 200, replace = TRUE)),\r\n  Freq = runif(200, 0, 0.5)\r\n)\r\nhead(nature_data)  # \u6253\u5370\u524d\u51e0\u884c\u6570\u636e\r\n<\/code><\/pre>\n<h3>\u4fee\u6539\u540e\u7684\u8d8b\u52bf\u6c14\u6ce1\u56fe\u4ee3\u7801<\/h3>\n<pre class=\"corepress-code-pre\"><code>library(ggplot2)\r\n\r\n# \u8bfb\u5165\u8d8b\u52bf\u6c14\u6ce1\u56fe\u6570\u636e\r\n# data &lt;- read.csv(\"\u8d8b\u52bf\u6c14\u6ce1\u56fe.csv\", header = T)  # \u539f\u59cb\u4ee3\u7801\r\ndata &lt;- trend_data  # \u4fee\u6539\u540e\u7684\u4ee3\u7801\r\n\r\ncol &lt;- c(\"#D57444\", \"#33B1C0\", \"#34993B\", \"#8C67AC\")\r\n\r\nggplot(data, aes(x=Diameter, y=expression, fill=group)) + \r\n  geom_area(size=0.01,colour=\"white\") +  # \u52a0\u9762\u79ef\r\n  labs(y=\"Expression\") +\r\n  theme_bw() +\r\n  scale_fill_manual(values=col) +\r\n  theme(panel.grid.major=element_blank(),panel.grid.minor=element_blank(),\r\n        axis.text = element_text(color = \"black\",size = 10),\r\n        axis.title = element_text(color = 'black', size=12)) +\r\n  geom_vline(aes(xintercept =100),linetype=\"dashed\", size=1.2, colour=\"white\") +  # \u52a0\u7ad6\u7ebf\r\n  geom_vline(aes(xintercept =200),linetype=\"dashed\", size=1.2, colour=\"white\") +\r\n  geom_vline(aes(xintercept =300),linetype=\"dashed\", size=1.2, colour=\"white\") +\r\n  geom_vline(aes(xintercept =400),linetype=\"dashed\", size=1.2, colour=\"white\")\r\n<\/code><\/pre>\n<h3>\u4fee\u6539\u540e\u7684Nature\u56fe\u4ee3\u7801<\/h3>\n<pre class=\"corepress-code-pre\"><code># \u8bfb\u5165Nature\u6570\u636e\r\n# df &lt;- read.csv(\"nature_ratio.csv\", header = T)  # \u539f\u59cb\u4ee3\u7801\r\ndf &lt;- nature_data  # \u4fee\u6539\u540e\u7684\u4ee3\u7801\r\n\r\ntable(df$Stage)\r\nA &lt;- prop.table(table(df$celltype, df$stage), margin = 2)\r\nA &lt;- as.data.frame(A)\r\ncolnames(A) &lt;- c(\"celltype\", \"stage\", \"Freq\")\r\n\r\ncluster_cols &lt;- c(\"#DC050C\", \"#FB8072\", \"#1965B0\", \"#7BAFDE\", \"#882E72\",\r\n                  \"#B17BA6\", \"#FF7F00\", \"#FDB462\", \"#E7298A\", \"#E78AC3\",\r\n                  \"#33A02C\", \"#B2DF8A\", \"#55A1B1\", \"#8DD3C7\", \"#A6761D\",\r\n                  \"#E6AB02\", \"#7570B3\", \"#BEAED4\", \"#666666\", \"#999999\",\r\n                  \"#aa8282\", \"#d4b7b7\", \"#8600bf\", \"#ba5ce3\", \"#808000\",\r\n                  \"#aeae5c\", \"#1e90ff\", \"#00bfff\", \"#56ff0d\", \"#ffff00\")\r\n\r\nggplot(A,aes(x = stage, y =Freq, group=celltype)) +\r\n  stat_summary(geom = 'line',fun='mean',cex=1,col='white') +  # \u5148\u8981\u6709\u6298\u7ebf\r\n  geom_area(data = A,aes(fill=celltype)) +  # \u6298\u7ebf\u4e0b\u9762\u79ef\uff0c\u586b\u5145\u7528celltype\r\n  scale_fill_manual(values=cluster_cols) +\r\n  labs(x=NULL, y=NULL) +\r\n  theme_bw() +\r\n  theme(panel.grid.major=element_blank(),panel.grid.minor=element_blank(),\r\n        axis.text = element_text(color = \"black\",size = 10)) +\r\n  geom_vline(aes(xintercept =\"CS12\"),linetype=\"dashed\", size=1.2, colour=\"white\") +\r\n  geom_vline(aes(xintercept =\"CS13\"),linetype=\"dashed\", size=1.2, colour=\"white\") +\r\n  geom_vline(aes(xintercept =\"CS15\"),linetype=\"dashed\", size=1.2, colour=\"white\") +\r\n  geom_vline(aes(xintercept =\"CS17\"),linetype=\"dashed\", size=1.2, colour=\"white\") +\r\n  geom_vline(aes(xintercept =\"CS20\"),linetype=\"dashed\", size=1.2, colour=\"white\")\r\n<\/code><\/pre>\n<h3>\u4f18\u5316\u540e\u7684\u8d8b\u52bf\u6c14\u6ce1\u56fe\u4ee3\u7801<\/h3>\n<pre class=\"corepress-code-pre\"><code>library(ggplot2)\r\n\r\n# \u8bfb\u5165\u8d8b\u52bf\u6c14\u6ce1\u56fe\u6570\u636e\r\ndata &lt;- trend_data  # \u4f7f\u7528\u6211\u4eec\u524d\u9762\u521b\u5efa\u7684\u793a\u4f8b\u6570\u636e\r\n\r\ncol &lt;- c(\"#D57444\", \"#33B1C0\", \"#34993B\", \"#8C67AC\")  # \u6307\u5b9a\u6bcf\u4e2a\u7ec4\u522b\u7684\u989c\u8272\r\n\r\n# \u521b\u5efaggplot\u5bf9\u8c61\r\np &lt;- ggplot(data, aes(x = Diameter, y = expression, fill = group))\r\n\r\n# \u6dfb\u52a0\u56fe\u5c42\r\np &lt;- p +\r\n  geom_area(size = 0.01, colour = \"white\") +  # \u6dfb\u52a0\u9762\u79ef\u5c42\r\n  labs(y = \"Expression\") +  # \u8bbe\u7f6ey\u8f74\u6807\u7b7e\r\n  theme_bw() +  # \u4f7f\u7528\u9ed1\u767d\u4e3b\u9898\r\n  scale_fill_manual(values = col) +  # \u624b\u52a8\u8bbe\u7f6e\u586b\u5145\u989c\u8272\r\n  theme(\r\n    panel.grid.major = element_blank(),\r\n    panel.grid.minor = element_blank(),\r\n    axis.text = element_text(color = \"black\", size = 10),\r\n    axis.title = element_text(color = 'black', size = 12)\r\n  )  # \u8bbe\u7f6e\u4e3b\u9898\u5143\u7d20\r\n\r\n# \u6dfb\u52a0\u7ad6\u7ebf\r\nfor (i in seq(100, 400, by = 100)) {\r\n  p &lt;- p + geom_vline(aes(xintercept = i), linetype = \"dashed\", size = 1.2, colour = \"white\")\r\n}\r\n\r\np  # \u6253\u5370\u56fe\u5f62\r\n<\/code><\/pre>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<h3>\u4f18\u5316\u540e\u7684Nature\u56fe\u4ee3\u7801<\/h3>\n<pre class=\"corepress-code-pre\"><code># \u8bfb\u5165Nature\u6570\u636e\r\ndf &lt;- nature_data  # \u4f7f\u7528\u6211\u4eec\u524d\u9762\u521b\u5efa\u7684\u793a\u4f8b\u6570\u636e\r\n\r\n# \u8ba1\u7b97\u6bcf\u4e2a\u9636\u6bb5\u7684\u9891\u7387\r\nA &lt;- prop.table(table(df$celltype, df$stage), margin = 2)\r\nA &lt;- as.data.frame(A)\r\ncolnames(A) &lt;- c(\"celltype\", \"stage\", \"Freq\")\r\n\r\n# \u6307\u5b9a\u6bcf\u4e2a\u7ec6\u80de\u7c7b\u578b\u7684\u989c\u8272\r\ncluster_cols &lt;- c(\"#DC050C\", \"#FB8072\", \"#1965B0\", \"#7BAFDE\", \"#882E72\",\r\n                  \"#B17BA6\", \"#FF7F00\", \"#FDB462\", \"#E7298A\", \"#E78AC3\",\r\n                  \"#33A02C\", \"#B2DF8A\", \"#55A1B1\", \"#8DD3C7\", \"#A6761D\",\r\n                  \"#E6AB02\", \"#7570B3\", \"#BEAED4\", \"#666666\", \"#999999\",\r\n                  \"#aa8282\", \"#d4b7b7\", \"#8600bf\", \"#ba5ce3\", \"#808000\",\r\n                  \"#aeae5c\", \"#1e90ff\", \"#00bfff\", \"#56ff0d\", \"#ffff00\")\r\n\r\n# \u521b\u5efaggplot\u5bf9\u8c61\r\np &lt;- ggplot(A, aes(x = stage, y = Freq, group = celltype))\r\n\r\n# \u6dfb\u52a0\u56fe\u5c42\r\np &lt;- p +\r\n  stat_summary(geom = 'line', fun = 'mean', cex = 1, col = 'white') +  # \u6dfb\u52a0\u6298\u7ebf\r\n  geom_area(aes(fill = celltype)) +  # \u6dfb\u52a0\u9762\u79ef\u5c42\r\n  scale_fill_manual(values = cluster_cols) +  # \u624b\u52a8\u8bbe\u7f6e\u586b\u5145\u989c\u8272\r\n  labs(x = NULL, y = NULL) +  # \u6e05\u9664x\u548cy\u8f74\u7684\u6807\u7b7e\r\n  theme_bw() +  # \u4f7f\u7528\u9ed1\u767d\u4e3b\u9898\r\n  theme(\r\n    panel.grid.major = element_blank(),\r\n    panel.grid.minor = element_blank(),\r\n    axis.text = element_text(color = \"black\", size = 10)\r\n  )  # \u8bbe\u7f6e\u4e3b\u9898\u5143\u7d20\r\n\r\n# \u6dfb\u52a0\u7ad6\u7ebf\r\nfor (stage in c(\"CS12\", \"CS13\", \"CS15\", \"CS17\", \"CS20\")) {\r\n  p &lt;- p + geom_vline(aes(xintercept = stage), linetype = \"dashed\", size = 1.2, colour = \"white\")\r\n}\r\n\r\np  # \u6253\u5370\u56fe\u5f62\r\n<\/code><\/pre>\n<p><span>\u4f18\u5316\u4ee3\u7801\uff0c\u4f7f\u5f97\u56fe\u7247\u66f4\u4e3a\u4e30\u5bcc\u548c\u7f8e\u89c2\u3002\u6211\u5c06\u4f7f\u7528ggthemes\u5305\u548cggtitle\u51fd\u6570\u6765\u6539\u5584\u56fe\u8868\u7684\u5916\u89c2\uff0c\u5e76\u7ed9\u56fe\u8868\u6dfb\u52a0\u6807\u9898\u3002<\/span><\/p>\n<pre class=\"corepress-code-pre\"><code># \u5b89\u88c5\u548c\u52a0\u8f7dggthemes\u5305\r\nif (!require(ggthemes)) {\r\n  install.packages(\"ggthemes\")\r\n}\r\nlibrary(ggthemes)\r\n\r\n# \u4f18\u5316\u8d8b\u52bf\u6c14\u6ce1\u56fe\r\np_trend &lt;- ggplot(trend_data, aes(x = Diameter, y = expression, fill = group)) +\r\n  geom_area(size = 0.01, colour = \"white\") +\r\n  scale_fill_manual(values = col) +\r\n  labs(x = \"Diameter\", y = \"Expression\", title = \"Trend Bubble Plot\") +\r\n  theme_tufte() +  # \u4f7f\u7528Tufte\u4e3b\u9898\uff0c\u8fd9\u4e2a\u4e3b\u9898\u5177\u6709\u7b80\u6d01\u7684\u98ce\u683c\r\n  theme(\r\n    plot.title = element_text(hjust = 0.5),\r\n    legend.position = \"bottom\",\r\n    panel.grid.major = element_blank(),\r\n    panel.grid.minor = element_blank(),\r\n    axis.text = element_text(color = \"black\", size = 10),\r\n    axis.title = element_text(color = 'black', size = 12)\r\n  )\r\nfor (i in seq(100, 400, by = 100)) {\r\n  p_trend &lt;- p_trend + geom_vline(aes(xintercept = i), linetype = \"dashed\", size = 1.2, colour = \"white\")\r\n}\r\np_trend\r\n\r\n# \u4f18\u5316Nature\u56fe\r\np_nature &lt;- ggplot(A, aes(x = stage, y = Freq, group = celltype)) +\r\n  stat_summary(geom = 'line', fun = 'mean', cex = 1, col = 'white') +\r\n  geom_area(aes(fill = celltype)) +\r\n  scale_fill_manual(values = cluster_cols) +\r\n  labs(x = NULL, y = NULL, title = \"Nature Plot\") +\r\n  theme_tufte() +\r\n  theme(\r\n    plot.title = element_text(hjust = 0.5),\r\n    legend.position = \"bottom\",\r\n    panel.grid.major = element_blank(),\r\n    panel.grid.minor = element_blank(),\r\n    axis.text = element_text(color = \"black\", size = 10)\r\n  )\r\nfor (stage in c(\"CS12\", \"CS13\", \"CS15\", \"CS17\", \"CS20\")) {\r\n  p_nature &lt;- p_nature + geom_vline(aes(xintercept = stage), linetype = \"dashed\", size = 1.2, colour = \"white\")\r\n}\r\np_nature<\/code><\/pre>\n<p><span>\u5728\u8fd9\u4e00\u6b65\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528\u4e86ggthemes\u5305\u7684Tufte\u4e3b\u9898\uff0c\u5b83\u662f\u4e00\u4e2a\u7b80\u6d01\u4e14\u5bf9\u6bd4\u5ea6\u8f83\u9ad8\u7684\u4e3b\u9898\u3002\u6211\u4eec\u8fd8\u4f7f\u7528\u4e86ggtitle\u51fd\u6570\u6765\u7ed9\u56fe\u8868\u6dfb\u52a0\u6807\u9898\uff0c\u5e76\u5c06\u56fe\u4f8b\u653e\u5728\u56fe\u8868\u5e95\u90e8\u4ee5\u66f4\u597d\u5730\u5229\u7528\u7a7a\u95f4\u3002\u53e6\u5916\uff0c\u6211\u4eec\u6dfb\u52a0\u4e86panel.grid.major\u548cpanel.grid.minor\u7684element_blank()\u5143\u7d20\uff0c\u4ee5\u79fb\u9664\u4e3b\u8981\u548c\u6b21\u8981\u7684\u7f51\u683c\u7ebf\u3002\u6b64\u5916\uff0c\u6211\u4eec\u4f7f\u7528\u4e86theme\u51fd\u6570\u7684hjust\u53c2\u6570\u6765\u6c34\u5e73\u5c45\u4e2d\u6807\u9898\uff0c\u5e76\u4f7f\u7528\u4e86theme\u51fd\u6570\u7684legend.position\u53c2\u6570\u5c06\u56fe\u4f8b\u653e\u5728\u5e95\u90e8\u3002<\/span><\/p>\n<p>&nbsp;<\/p>\n<h2>\u5b8c\u6574\u4ee3\u7801<\/h2>\n<pre class=\"corepress-code-pre\"><code>\r\n# \u5b89\u88c5\u548c\u52a0\u8f7d\u9700\u8981\u7684\u5305\r\nif (!require(ggplot2)) {\r\n  install.packages(\"ggplot2\")\r\n}\r\nif (!require(ggthemes)) {\r\n  install.packages(\"ggthemes\")\r\n}\r\nlibrary(ggplot2)\r\nlibrary(ggthemes)\r\n\r\n# \u5b9a\u4e49\u6570\u636e\r\ntrend_data &lt;- data.frame(Diameter = c(50, 100, 150, 200, 250),\r\n                         expression = c(0.5, 0.6, 0.7, 0.6, 0.5),\r\n                         group = c(\"A\", \"B\", \"C\", \"A\", \"B\"))\r\n\r\nnature_data &lt;- data.frame(celltype = c(\"Type1\", \"Type2\", \"Type3\", \"Type1\", \"Type2\"),\r\n                          stage = c(\"CS12\", \"CS13\", \"CS15\", \"CS17\", \"CS20\"),\r\n                          Freq = c(0.2, 0.3, 0.5, 0.3, 0.2))\r\n\r\n# \u5b9a\u4e49\u989c\u8272\r\ncol &lt;- c(\"#D57444\", \"#33B1C0\", \"#34993B\", \"#8C67AC\")\r\ncluster_cols &lt;- c(\"#DC050C\", \"#FB8072\", \"#1965B0\", \"#7BAFDE\", \"#882E72\",\r\n                  \"#B17BA6\", \"#FF7F00\", \"#FDB462\", \"#E7298A\", \"#E78AC3\",\r\n                  \"#33A02C\", \"#B2DF8A\", \"#55A1B1\", \"#8DD3C7\", \"#A6761D\",\r\n                  \"#E6AB02\", \"#7570B3\", \"#BEAED4\", \"#666666\", \"#999999\",\r\n                  \"#aa8282\", \"#d4b7b7\", \"#8600bf\", \"#ba5ce3\", \"#808000\",\r\n                  \"#aeae5c\", \"#1e90ff\", \"#00bfff\", \"#56ff0d\", \"#ffff00\")\r\n\r\n# \u521b\u5efa\u8d8b\u52bf\u6c14\u6ce1\u56fe\r\np_trend &lt;- ggplot(trend_data, aes(x = Diameter, y = expression, fill = group)) +\r\n  geom_area(linewidth = 0.01, colour = \"white\") +\r\n  scale_fill_manual(values = col) +\r\n  labs(x = \"Diameter\", y = \"Expression\", title = \"Trend Bubble Plot\") +\r\n  theme_tufte() +\r\n  theme(\r\n    plot.title = element_text(hjust = 0.5),\r\n    legend.position = \"bottom\",\r\n    panel.grid.major = element_blank(),\r\n    panel.grid.minor = element_blank(),\r\n    axis.text = element_text(color = \"black\", size = 10),\r\n    axis.title = element_text(color = 'black', size = 12)\r\n  )\r\nfor (i in seq(100, 400, by = 100)) {\r\n  p_trend &lt;- p_trend + geom_vline(aes(xintercept = i), linetype = \"dashed\", size = 1.2, colour = \"white\")\r\n}\r\n\r\n# \u521b\u5efaNature\u56fe\r\np_nature &lt;- ggplot(nature_data, aes(x = stage, y = Freq, group = celltype)) +\r\n  stat_summary(geom = 'line', fun = 'mean', cex = 1, col = 'white') +\r\n  geom_area(aes(fill = celltype)) +\r\n  scale_fill_manual(values = cluster_cols) +\r\n  labs(x = NULL, y = NULL, title = \"Nature Plot\") +\r\n  theme_tufte() +\r\n  theme(\r\n    plot.title = element_text(hjust = 0.5),\r\n    legend.position = \"bottom\",\r\n    panel.grid.major = element_blank(),\r\n    panel.grid.minor = element_blank(),\r\n    axis.text = element_text(color = \"black\", size = 10)\r\n  )\r\nfor (stage in c(\"CS12\", \"CS13\", \"CS15\", \"CS17\", \"CS20\")) {\r\n  p_nature &lt;- p_nature + geom_vline(aes(xintercept = stage), linetype = \"dashed\", size = 1.2, colour = \"white\")\r\n}\r\n\r\n# \u663e\u793a\u56fe\u50cf\r\nprint(p_trend)\r\nprint(p_nature)\r\n\r\n# \u4fdd\u5b58\u56fe\u50cf\u4e3aPDF\r\nggsave(filename = \"TrendBubblePlot.pdf\", plot = p_trend, device = \"pdf\", width = 10, height = 7, units = \"in\")\r\nggsave(filename = \"NaturePlot.pdf\", plot = p_nature, device = \"pdf\", width = 10, height = 7, units = \"in\")\r\n\r\n# \u4fdd\u5b58\u6570\u636e\u4e3aCSV\r\nwrite.csv(trend_data, file = \"TrendData.csv\", row.names = FALSE)\r\nwrite.csv(nature_data, file = \"NatureData.csv\", row.names = FALSE)\r\n\r\n<\/code><\/pre>\n<p>&nbsp;<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/yunshangtulv.com.cn\/wp-content\/uploads\/2023\/07\/\u622a\u56fe_20230710152614-1024x720.png\" alt=\"\" width=\"1024\" height=\"720\" class=\"alignnone size-large wp-image-504\" srcset=\"https:\/\/yunshangtulv.com.cn\/wp-content\/uploads\/2023\/07\/\u622a\u56fe_20230710152614-1024x720.png 1024w, https:\/\/yunshangtulv.com.cn\/wp-content\/uploads\/2023\/07\/\u622a\u56fe_20230710152614-600x422.png 600w, https:\/\/yunshangtulv.com.cn\/wp-content\/uploads\/2023\/07\/\u622a\u56fe_20230710152614-300x211.png 300w, https:\/\/yunshangtulv.com.cn\/wp-content\/uploads\/2023\/07\/\u622a\u56fe_20230710152614-768x540.png 768w, https:\/\/yunshangtulv.com.cn\/wp-content\/uploads\/2023\/07\/\u622a\u56fe_20230710152614.png 1081w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/> <img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/yunshangtulv.com.cn\/wp-content\/uploads\/2023\/07\/\u622a\u56fe_20230710152602.png\" alt=\"\" width=\"1008\" height=\"1008\" class=\"alignnone size-large wp-image-505\" srcset=\"https:\/\/yunshangtulv.com.cn\/wp-content\/uploads\/2023\/07\/\u622a\u56fe_20230710152602.png 1008w, https:\/\/yunshangtulv.com.cn\/wp-content\/uploads\/2023\/07\/\u622a\u56fe_20230710152602-300x300.png 300w, https:\/\/yunshangtulv.com.cn\/wp-content\/uploads\/2023\/07\/\u622a\u56fe_20230710152602-100x100.png 100w, https:\/\/yunshangtulv.com.cn\/wp-content\/uploads\/2023\/07\/\u622a\u56fe_20230710152602-600x600.png 600w, https:\/\/yunshangtulv.com.cn\/wp-content\/uploads\/2023\/07\/\u622a\u56fe_20230710152602-150x150.png 150w, https:\/\/yunshangtulv.com.cn\/wp-content\/uploads\/2023\/07\/\u622a\u56fe_20230710152602-768x768.png 768w\" sizes=\"auto, (max-width: 1008px) 100vw, 1008px\" \/><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u7701\u6d41\uff1a\u5b8c\u6574\u4ee3\u7801\u62c9\u5230\u6700\u540e &nbsp; \u9996\u5148\u6570\u636e\u8868\u683c\u8981\u6c42\uff1a 1. \u8d8b\u52bf\u6c14\u6ce1\u56fe\u6570\u636e\uff1a\u8fd9\u4e2a\u6570\u636e\u5e94\u8be5\u662f\u4e00\u4e2aCSV\u6587\u4ef6\uff0c [&hellip;]<\/p>\n","protected":false},"author":111,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[9],"tags":[],"class_list":["post-503","post","type-post","status-publish","format-standard","hentry","category-r"],"_links":{"self":[{"href":"https:\/\/yunshangtulv.com.cn\/index.php?rest_route=\/wp\/v2\/posts\/503","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/yunshangtulv.com.cn\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/yunshangtulv.com.cn\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/yunshangtulv.com.cn\/index.php?rest_route=\/wp\/v2\/users\/111"}],"replies":[{"embeddable":true,"href":"https:\/\/yunshangtulv.com.cn\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=503"}],"version-history":[{"count":0,"href":"https:\/\/yunshangtulv.com.cn\/index.php?rest_route=\/wp\/v2\/posts\/503\/revisions"}],"wp:attachment":[{"href":"https:\/\/yunshangtulv.com.cn\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=503"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/yunshangtulv.com.cn\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=503"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/yunshangtulv.com.cn\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=503"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}