{"id":28283,"date":"2024-02-06T10:23:43","date_gmt":"2024-02-06T02:23:43","guid":{"rendered":"http:\/\/www.biocloudservice.com\/wordpress\/?p=28283"},"modified":"2024-02-06T10:23:45","modified_gmt":"2024-02-06T02:23:45","slug":"%e5%b0%8f%e6%9e%9c%e5%b8%a6%e4%bd%a0%e5%a4%8d%e7%8e%b0%e5%a4%9a%e6%a0%b7%e6%95%b0%e6%8d%ae%e5%8f%af%e8%a7%86%e5%8c%96%ef%bc%81%e8%bd%bb%e6%9d%be%e6%8b%bf%e4%b8%8bif30-8%e6%96%87%e7%8c%ae%e7%bb%84","status":"publish","type":"post","link":"http:\/\/www.biocloudservice.com\/wordpress\/?p=28283","title":{"rendered":"\u5c0f\u679c\u5e26\u4f60\u590d\u73b0\u591a\u6837\u6570\u636e\u53ef\u89c6\u5316\uff01\u8f7b\u677e\u62ff\u4e0bIF30.8\u6587\u732e\u7ec4\u5408\u56fe\uff1a\u67f1\u72b6\u56fe+\u997c\u56fe+\u7ebf\u578b\u56fe\uff01\uff01"},"content":{"rendered":"

\u5728\u5f53\u4eca\u4fe1\u606f\u65f6\u4ee3\uff0c\u6570\u636e\u53ef\u89c6\u5316\u6210\u4e3a\u89e3\u8bfb\u590d\u6742\u6570\u636e\u7684\u91cd\u8981\u5de5\u5177\uff0c\u8fd9\u6b21\u5c0f\u679c\u5c06\u5e26\u5c0f\u4f19\u4f34\u4e00\u540c\u63a2\u7d22\u591a\u6837\u5316\u7684\u6570\u636e\u53ef\u89c6\u5316\uff0c\u901a\u8fc7\u7b80\u5355\u7684R\u4ee3\u7801\u8f7b\u677e\u590d\u73b0\u4e00\u7bc7IF30.8\u9ad8\u5206\u6587\u732e\u4e2d\u7684\u4ee4\u4eba\u8d4f\u5fc3\u60a6\u76ee\u7684\u7ec4\u5408\u56fe\uff1a\u67f1\u72b6\u56fe+\u997c\u56fe+\u7ebf\u578b\u56fe\u3002\u6211\u4eec\u5148\u6765\u770b\u6211\u4eec\u8fd9\u6b21\u590d\u73b0\u7684\u5185\u5bb9\uff0c\u662f\u6765\u81eaIF30.8\u300aPangenome analysis provides insight into the evolution of the orange subfamily and a key gene for citric acid accumulation in citrus fruits\u300b\u4e2d\u7684\u56fe3a\u548cb<\/p>\n

<\/p>\n

\u5176\u5b9e\uff0c\u6570\u636e\u53ef\u89c6\u5316\u662f\u89e3\u8bfb\u548c\u4ea4\u6d41\u6570\u636e\u7684\u5173\u952e\u5de5\u5177\uff0c\u800cR\u8bed\u8a00\u4ee5\u5176\u5f3a\u5927\u7684\u7ed8\u56fe\u80fd\u529b\u6210\u4e3a\u79d1\u5b66\u5bb6\u4eec\u7684\u9996\u9009\u3002\u5728\u8fd9\u4e2a\u56fe\u4e2d\uff0c\u6211\u4eec\u5c06\u805a\u7126\u6587\u732e\u4e2d\u7684\u7ec4\u5408\u56fe\uff0c\u8fd9\u4e2a\u56fe\u5305\u62ec\u67f1\u72b6\u56fe\u3001\u997c\u56fe\u548c\u7ebf\u578b\u56fe\u3002\u8fd9\u7bc7\u6587\u732e\u4ee5\u5176\u5353\u8d8a\u7684\u8d28\u91cf\u548c\u72ec\u7279\u7684\u6570\u636e\u5448\u73b0\u65b9\u5f0f\u83b7\u5f97\u9ad8\u5ea6\u8bc4\u4ef7\u3002\u6240\u4ee5\uff0c\u8ddf\u968f\u201c\u5c0f\u679c\u201d\u7684\u6307\u5f15\uff0c\u5c06\u5e26\u5c0f\u4f19\u4f34\u4eb2\u8eab\u4f53\u9a8c\u5982\u4f55\u7528\u7b80\u5355\u7684\u4ee3\u7801\u5728R\u4e2d\u5b9e\u73b0\u8fd9\u4e9b\u5f15\u4eba\u5165\u80dc\u7684\u53ef\u89c6\u5316\u6548\u679c\u3002<\/p>\n

\u67f1\u72b6\u56fe\u662f\u5c55\u793a\u5404\u7c7b\u522b\u4e4b\u95f4\u5173\u7cfb\u7684\u7ecf\u5178\u5de5\u5177\uff0c\u800c\u997c\u56fe\u5219\u4ee5\u6e05\u6670\u7684\u90e8\u5206\u6574\u4f53\u6bd4\u4f8b\u4e3a\u6211\u4eec\u5448\u73b0\u6570\u636e\u7684\u6784\u6210\u3002\u7ebf\u578b\u56fe\u5219\u9002\u7528\u4e8e\u63cf\u7ed8\u8d8b\u52bf\u548c\u53d8\u5316\u3002\u8fd9\u4e09\u8005\u7684\u5de7\u5999\u7ed3\u5408\u4f7f\u5f97\u6211\u4eec\u80fd\u591f\u5168\u9762\u4e86\u89e3\u6587\u732e\u4e2d\u6240\u63a2\u8ba8\u7684\u6570\u636e\u80cc\u540e\u7684\u6545\u4e8b\u3002<\/p>\n

\u8fd9\u91cc\u5462\u5c0f\u679c\u8fd8\u8981\u8bf4\u660e\u4e00\u4e0b\uff0c\u6211\u4eec\u8fd9\u6b21\u505a\u7684\u6570\u636e\u5206\u6790\u6570\u636e\u91cf\u662f\u975e\u5e38\u5927\u7684\uff0c\u6211\u4eec\u64cd\u4f5c\u5360\u7528\u5185\u5b58\u6bd4\u8f83\u5927\uff0c\u6240\u4ee5\u5efa\u8bae\u5404\u4f4d\u5c0f\u4f19\u4f34\u53ef\u4ee5\u4f7f\u7528\u670d\u52a1\u5668\u53bb\u8fd0\u884c\uff0c\u5982\u679c\u6ca1\u6709\u81ea\u5df1\u7684\u670d\u52a1\u5668\u4e5f\u6ca1\u6709\u5173\u7cfb\uff0c\u53ef\u4ee5\u6b22\u8fce\u8054\u7cfb\u6211\u4eec\u4f7f\u7528\u670d\u52a1\u5668\u79df\u8d41~<\/p>\n

\u6211\u4eec\u590d\u73b0\u4e4b\u524d\u89c2\u5bdf\u4e00\u4e0b\u8fd9\u5e45\u56fe\uff0c\u997c\u56fe\u548c\u67f1\u72b6\u56fe\u7ec4\u5408\u5728\u4e00\u8d77\uff0c\u800c\u4e14\u8fd9\u4e2a\u997c\u56fe\u4eff\u4f5b\u662f\u9576\u5d4c\u5728\u67f1\u72b6\u56fe\u4e2d\u95f4\uff0c\u975e\u5e38\u6709\u610f\u601d\uff0c\u6211\u4eec\u4e0b\u9762\u5c31\u53bb\u5b66\u4e60\u4e00\u4e0b\u5982\u4f55\u7ed8\u5236\u8fd9\u7c7b\u578b\u7684\u56fe\uff01<\/p>\n

\u9996\u5148\u5462\uff0c\u6211\u4eec\u5148\u770b\u4e00\u4e0b\u56fe\u7684\u6570\u636e\u683c\u5f0f\uff0c<\/p>\n

library(tidyverse)<\/p>\n

#\u8fd9\u91cc\u6211\u4eec\u8fd8\u6709\u52a0\u8f7d\u4e00\u4e2a\u5305<\/p>\n

library(readxl)#\u5c06 Excel \u6587\u4ef6\uff08.xls \u548c .xlsx\uff09\u8bfb\u53d6\u5230 R \u4e2d\u800c\u8bbe\u8ba1\u7684<\/p>\n

dat1<-read_excel(“data1.xlsx”,<\/p>\n

sheet = “Sheet1”)<\/p>\n

<\/p>\n

\u8fd9\u4e2a\u662f\u67f1\u72b6\u56fe\u7684\u6570\u636e\uff0c<\/p>\n

\u6211\u4eec\u4ece\u67f1\u72b6\u56fe\u7ed8\u5236\uff0c\u5176\u5b9e\u8fd9\u4e2a\u56fe\u5c0f\u4f19\u4f34\u80af\u5b9a\u90fd\u719f\u6089\uff0c\u6211\u4eec\u4e00\u6b65\u5230\u4f4d<\/p>\n

p1<-ggplot(data=dat1,aes(x=family,y=num))+<\/p>\n

geom_col(aes(fill=class),<\/p>\n

show.legend = FALSE)+# # \u57fa\u4e8e\u201cclass\u201d\u8fdb\u884c\u989c\u8272\u586b\u5145\u6761\u5f62\u56fe<\/p>\n

theme_bw(base_size = 15)+##\u5c06\u4e3b\u9898\u8bbe\u7f6e\u4e3a\u9ed1\u767d\uff0c\u57fa\u672c\u5b57\u4f53\u5927\u5c0f\u4e3a 15<\/p>\n

theme(panel.grid = element_blank(),# \u5220\u9664\u7f51\u683c\u7ebf<\/p>\n

panel.border = element_blank(),# \u5220\u9664\u9762\u677f\u8fb9\u6846<\/p>\n

axis.line = element_line())+# \u5220\u9664\u8f74\u7ebf<\/p>\n

scale_y_continuous(expand = expansion(mult=c(0,0.01)),# \u7a0d\u5fae\u6269\u5927 y \u8f74\u8303\u56f4<\/p>\n

breaks = seq(0,10000,2500),# \u8bbe\u7f6e y \u8f74\u65ad\u70b9<\/p>\n

labels = scales::comma)+# \u7528\u9017\u53f7\u683c\u5f0f\u5316 y \u8f74\u6807\u7b7e<\/p>\n

scale_x_continuous(breaks = c(0,5,10,15))+# \u8bbe\u7f6e x \u8f74\u4e2d\u65ad<\/p>\n

labs(x=”Number of genomes”,y=”Family number”)+# \u8bbe\u7f6e\u8f74\u6807\u7b7e<\/p>\n

scale_fill_manual(values = c(“special”=”#c99e22”,# \u8bbe\u7f6e’class’\u7684\u624b\u52a8\u586b\u5145\u989c\u8272<\/p>\n

“dispensable”=”#a5ae00”,<\/p>\n

“softcore”=”#df94a0”,<\/p>\n

“core”=”#d97168”))<\/p>\n

p1<\/p>\n

<\/p>\n

\u8fd9\u91cc\u6a21\u62df\u6587\u732e\u4e2d\u7684\u6570\u636e\uff0c\u5176\u5b9e\u4ee3\u7801\u90fd\u5f88\u7b80\u5355\uff0c\u5c0f\u679c\u8fd9\u91cc\u5c31\u4e0d\u5206\u89e3\u8bb2\uff0c\u4ee3\u7801\u7684\u6ce8\u91ca\u5c0f\u679c\u90fd\u8fdb\u884c\u4e86\u6807\u8bb0<\/p>\n

\u90a3\u4e48\u63a5\u4e0b\u6765\u6211\u4eec\u5c31\u662f\u7ed8\u5236\u7b2c\u4e8c\u5e45\u56fe\uff0c<\/p>\n

\u6709\u610f\u601d\u7684\u997c\u72b6\u56fe<\/p>\n

\u8fd9\u91cc\u5c0f\u679c\u6a21\u62df\u4e00\u4e0b\u5b9e\u9a8c\u6570\u636e\uff0c<\/p>\n

\u8fd8\u662f\u5148\u52a0\u8f7d\u8981\u4f7f\u7528\u7684\u5305<\/p>\n

library(scatterpie)#\u521b\u5efa\u5e26\u6709\u997c\u56fe\u6807\u8bb0\u7684\u6563\u70b9\u56fe\u7684\u5305<\/p>\n

\u7136\u540e\u5c0f\u679c\u521b\u5efa\u4e86\u4e00\u4e2a\u540d<\/p>\n

#\u4e3adat2\u201cx\u201d\u3001\u201cy\u201d\u3001\u201cregion\u201d\u3001\u201cCore\u201d\u3001\u201cSoftcore\u201d\u3001\u201cDispensable\u201d\u548c\u201cSpecial\u201d\u5217\u7684\u6570\u636e\u6846<\/p>\n

dat2<-data.frame(x=1,y=1,<\/p>\n

region=1,<\/p>\n

Core=10331,<\/p>\n

Softcore=2036,<\/p>\n

Dispensable=19138,<\/p>\n

Special=3059)<\/p>\n

\u6211\u4eec\u770b\u770b\u6570\u636e\u957f\u4ec0\u4e48\u6837<\/p>\n

<\/p>\n

\u5f88\u7b80\u5355\uff0c\u5c31\u662f\u4e0d\u540c\u6570\u636e\u7684\u5360\u6bd4\u8fd8\u6709\u6570\u91cf\uff0c\u8fd9\u91cc\u5c0f\u679c\u968f\u673a\u8bbe\u7f6e\u7684<\/p>\n

\u4e3a\u4e86\u548c\u6587\u732e\u4e2d\u4e00\u6837\u5462\uff0c\u5c0f\u679c\u8fd9\u91cc\u628a\u56fe\u6ce8\u7684\u540d\u5b57\u4e5f\u6539\u6210\u4e00\u6837\u7684\uff0c\u76f4\u63a5\u7ed8\u5236\uff1a<\/p>\n

p2<-ggplot()+#\u521d\u59cb\u5316\u4e00\u4e2a\u65b0\u7684 ggplot \u5bf9\u8c61<\/p>\n

#\u5c06\u5e26\u6709\u997c\u56fe\u6807\u8bb0\u7684\u6563\u70b9\u56fe\u6dfb\u52a0\u5230 ggplot \u4e2d<\/p>\n

geom_scatterpie(data=dat2,#\u6307\u5b9a\u6570\u636e\u6846dat2<\/p>\n

aes(x,y,group=region,r=1),#\u5b9a\u4e49\u7f8e\u89c2\u7684x \u548c y \u5750\u6807\u3001\u6309\u201c\u533a\u57df\u201d\u5206\u7ec4\u4ee5\u53ca\u997c\u56fe\u6807\u8bb0\u7684\u56fa\u5b9a\u534a\u5f84 1<\/p>\n

cols=c(“Core”,”Softcore”,”Dispensable”,”Special”),#\u6307\u5b9a\u7528\u4f5c\u997c\u56fe\u7c7b\u522b\u7684\u5217<\/p>\n

color=”black”,#\u5c06\u997c\u56fe\u8fb9\u6846\u7684\u989c\u8272\u8bbe\u7f6e\u4e3a\u9ed1\u8272<\/p>\n

show.legend=FALSE)+#\u9690\u85cf\u56fe\u4f8b<\/p>\n

coord_equal()+#\u786e\u4fdd x \u8f74\u548c y \u8f74\u7684\u7f29\u653e\u6bd4\u4f8b\u76f8\u7b49<\/p>\n

theme_void(base_size = 15)+#\u5c06\u4e3b\u9898\u8bbe\u7f6e\u4e3a\u7a7a\uff08\u65e0\u80cc\u666f\u3001\u8f74\u7ebf\u6216\u6807\u7b7e\uff09\uff0c\u57fa\u672c\u5b57\u4f53\u5927\u5c0f\u4e3a 15<\/p>\n

#\u5411\u7ed8\u56fe\u6dfb\u52a0\u6587\u672c\u6ce8\u91ca\uff0c\u53c2\u6570\u8bbe\u7f6ex\u548cy\u5750\u6807\u3001\u6807\u7b7e\u6587\u672c\u548c\u6587\u672c\u65cb\u8f6c\u89d2\u5ea6<\/p>\n

annotate(geom = “text”,x=1.3,y=1.5,label=”Core\\n10331 (29.88%)”,<\/p>\n

angle=0)+<\/p>\n

annotate(geom = “text”,x=2.1,y=0.5,label=”SoftCore\\n2036 (5.89%)”,<\/p>\n

angle=0)+<\/p>\n

annotate(geom = “text”,x=0.5,y=1,label=”Dispensable\\n19138 (55.37%)”,<\/p>\n

angle=0)+<\/p>\n

annotate(geom = “text”,x=0.6,y=2.15,label=”Private\\n3059 (8.85%)”,<\/p>\n

angle=20)+<\/p>\n

#\u5411\u7ed8\u56fe\u6dfb\u52a0\u7ebf\u6bb5\uff0c\u53c2\u6570\u5305\u62ec\u6bcf\u6761\u7ebf\u6bb5\u7684\u8d77\u59cb\u548c\u7ed3\u675f\u5750\u6807<\/p>\n

annotate(geom = “segment”,x=0.7,xend=0.6,y=1.95,yend=2.05)+<\/p>\n

annotate(geom = “segment”,x=1.88,xend=2.0,y=0.5,yend=0.5)+<\/p>\n

#\u52a8\u8bbe\u7f6e\u4e0d\u540c\u997c\u56fe\u7c7b\u522b\u7684\u586b\u5145\u989c\u8272<\/p>\n

scale_fill_manual(values = c(“Special”=”#c99e22”,<\/p>\n

“Dispensable”=”#a5ae00”,<\/p>\n

“Softcore”=”#df94a0”,<\/p>\n

“Core”=”#d97168”))<\/p>\n

p2<\/p>\n

<\/p>\n

\u5176\u5b9e\u7ed8\u56fe\u5f88\u7b80\u5355\uff0c \u6700\u4e3b\u8981\u7684\u8fd8\u662f\u5bf9\u7ec6\u8282\u7684\u4fee\u6539\uff0c\u5c0f\u4f19\u4f34\u4e0d\u77e5\u9053\u8fd9\u4e2a\u56fe\u4e2d\u7279\u6b8a\u6807\u7b7e\u5982\u4f55\u6807\u8bb0\uff0c\u6216\u8005\u989c\u8272\u7b49\u8bbe\u7f6e\uff0c\u5c0f\u679c\u5bf9\u4ee3\u7801\u90fd\u8fdb\u884c\u4e86\u6ce8\u91ca\uff0c<\/p>\n

\u4e0b\u9762\u5c31\u662f\u6700\u91cd\u8981\u7684\u4e00\u70b9\u4e86\uff0c\u5c31\u662f\u7ed8\u5236\u6211\u4eec\u7684\u201c\u9576\u5d4c\u201d\u7684\u7ec4\u5408\u56fe<\/p>\n

\u8fd9\u91cc\u5c31\u662f\u9700\u8981\u4e00\u4e2a\u5305\uff0c\u4e13\u95e8\u6269\u5c55 ggplot2 \u7684\u9644\u52a0\u529f\u80fd\u7684\u5305ggpmisc<\/p>\n

#\u7ec4\u5408<\/p>\n

library(ggpmisc)<\/p>\n

\u7136\u540e\u6211\u4eec\u5c31\u8fdb\u884c\u7ed8\u5236<\/p>\n

p1+#<\/p>\n

#\u6307\u5b9a\u65b0\u5b50\u56fe\u7684\u6570\u636e\uff0c\u5176\u4e2d\u201cx\u201d\u548c\u201cy\u201d\u662f\u5750\u6807\uff0c\u201cplot\u201d\u662f\u5305\u542b\u7b2c\u4e8c\u4e2a\u56fe\u7684\u5217\u8868 ( p2)<\/p>\n

geom_plot(data=tibble(x=2,y=10000,plot=list(p2)),<\/p>\n

aes(x=x,y=y,label=plot),#\u5b9a\u4e49\u5b50\u56fe\u7684\u7f8e\u89c2\uff0cx \u548c y \u5750\u6807\u4ee5\u53ca\u4ee3\u8868\u7b2c\u4e8c\u4e2a\u56fe\u7684\u6807\u7b7e<\/p>\n

vp.width=0.8,vp.height=0.8)#\u8bbe\u7f6e\u5b50\u56fe\u89c6\u53e3\u7684\u5bbd\u5ea6\u548c\u9ad8\u5ea6<\/p>\n

<\/p>\n

\u6548\u679c\u600e\u4e48\u6837\uff0c\u662f\u4e0d\u662f\u548c\u6587\u732e\u4e2d\u7684\u9576\u5d4c\u7c7b\u578b\u4e00\u6837\uff0c\u5176\u5b9e\u5c31\u662f\u4e24\u5e45\u56fe\u5305\u542b\u7684\u5173\u7cfb\uff0c<\/p>\n

\u90a3\u4e48\u63a5\u4e0b\u6765\u5c31\u662f\u6211\u4eec\u7b2c\u4e09\u526f\u56fe\uff0c\u7ebf\u578b\u56fe\uff0c\u5c0f\u4f19\u4f34\u4f30\u8ba1\u89c1\u7684\u4e0d\u591a\uff0c\u6240\u4ee5\u8fd9\u6b21\u5c0f\u679c\u5c31\u4e00\u6b21\u6027\u6559\u7ed9\u5927\u5bb6<\/p>\n

\u6211\u4eec\u6765\u770b\u4e00\u4e0b\u56fe\u5f62\u7684\u6570\u636e\u683c\u5f0f<\/p>\n

#\u7ebf\u578b\u56fe<\/p>\n

dat3<-read_excel(“data2.xlsx”,<\/p>\n

sheet = “Sheet1”) %>%<\/p>\n

pivot_longer(!`Number of genomes`) %>%<\/p>\n

#\u7528mutate\u51fd\u6570\u5c06\u201c\u57fa\u56e0\u7ec4\u6570\u91cf\u201d\u5217\u8f6c\u6362\u4e3a\u5177\u6709\u6307\u5b9a\u7ea7\u522b\u7684\u56e0\u5b50<\/p>\n

mutate(`Number of genomes`=factor(`Number of genomes`,levels = 1:18))<\/p>\n

\u5176\u5b9e\u5c31\u662f\u4e24\u7ec4\u4e0d\u540c\u7684\u6570\u636e\uff0c<\/p>\n

\u7136\u540e\u6211\u4eec\u5c31\u7ed8\u5236\u56fe\u5427\uff0c<\/p>\n

ggplot(<\/p>\n

#\u4f7f\u7528\u6307\u5b9a\u7684\u521d\u59cb\u5316 ggplot \u5bf9\u8c61\uff0c<\/p>\n

#\u5c06\u201c\u57fa\u56e0\u7ec4\u6570\u91cf\u201d\u6620\u5c04\u5230 x \u8f74\uff0c\u5c06\u201c\u503c\u201d\u6620\u5c04\u5230 y \u8f74\uff0c<\/p>\n

#\u5e76\u4f7f\u7528\u201cNumber of genomes\u201d\u8fdb\u884c\u586b\u5145\u548c\u989c\u8272<\/p>\n

data=dat3,aes(x=`Number of genomes`,<\/p>\n

y=value,<\/p>\n

fill=name,<\/p>\n

color=name))+<\/p>\n

#\u4f7f\u7528\u8be5\u51fd\u6570\u5c06\u8bef\u5dee\u7ebf\u6dfb\u52a0\u5230\u7ed8\u56fe\u4e2d<\/p>\n

stat_boxplot(geom=”errorbar”,<\/p>\n

width=0.3,<\/p>\n

position = position_dodge(0.8))+<\/p>\n

geom_boxplot()+#\u5c06\u4e3b\u7bb1\u7ebf\u56fe\u56fe\u5c42\u6dfb\u52a0\u5230\u56fe\u4e2d<\/p>\n

theme_bw(base_size = 15)+#\u5c06\u4e3b\u9898\u8bbe\u7f6e\u4e3a\u9ed1\u767d\uff0c\u57fa\u672c\u5b57\u4f53\u5927\u5c0f\u4e3a 15<\/p>\n

#\u6574\u4e3b\u9898\u8bbe\u7f6e<\/p>\n

theme(legend.position = c(0.8,0.5),#\u56fe\u4f8b\u4f4d\u7f6e<\/p>\n

legend.background = element_rect(fill=”transparent”),#\u56fe\u4f8b\u80cc\u666f<\/p>\n

panel.grid = element_blank())+#\u53bb\u9664\u9762\u677f\u7f51\u683c\u7ebf<\/p>\n

labs(x=”Number of genomes”,y=”Number of gene families”)+#\u8bbe\u7f6e x \u548c y \u8f74\u6807\u7b7e<\/p>\n

scale_fill_discrete(name=NULL,labels=c(“Core”,”Pan”))+#\u4fee\u6539\u56fe\u4f8b\u4ee5\u5b9e\u73b0\u586b\u5145\u7f8e\u89c2\uff0c\u5c06\u56fe\u4f8b\u540d\u79f0\u8bbe\u7f6e\u4e3a NULL \u5e76\u63d0\u4f9b\u81ea\u5b9a\u4e49\u6807\u7b7e<\/p>\n

scale_color_discrete(name=NULL,labels=c(“Core”,”Pan”))#\u4fee\u6539\u56fe\u4f8b\u7684\u989c\u8272\u7f8e\u611f\uff0c\u5c06\u56fe\u4f8b\u540d\u79f0\u8bbe\u7f6e\u4e3a NULL \u5e76\u63d0\u4f9b\u81ea\u5b9a\u4e49\u6807\u7b7e<\/p>\n

<\/p>\n

\u5230\u8fd9\u91cc\u6211\u4eec\u8fd9\u4e09\u4e2a\u7ec4\u5408\u56fe\u5c31\u7ed8\u5236\u5b8c\u6210\u4e86\uff0c\u5176\u5b9e\u539f\u7406\u90fd\u5f88\u7b80\u5355\uff0c\u90fd\u662f\u4e00\u4e9b\u57fa\u7840\u7684\u56fe\u8fdb\u884c\u62fc\u63a5\u548c\u7ec4\u5408\uff0c\u5c0f\u679c\u4e5f\u628a\u4ee3\u7801\u6ce8\u91ca\u5173\u4e8e\u6bcf\u4e00\u4e2a\u6b65\u9aa4\u7684\u8bb2\u89e3\u90fd\u8fdb\u884c\u4e86\u6ce8\u91ca\uff0c\u5c0f\u4f19\u4f34\u4e0b\u53bb\u8981\u591a\u591a\u7406\u89e3\u4ee3\u7801\u7684\u610f\u601d\uff0c\u8fd9\u6837\u624d\u80fd\u7ed8\u5236\u51fa\u81ea\u5df1\u60f3\u8981\u7684\u56fe\uff0c\u597d\u4e86\uff0c\u8bdd\u4e0d\u591a\u8bf4\uff01\u5feb\u53bb\u52a8\u624b\u8bd5\u8bd5\u5427\uff01\uff01<\/p>\n

\u6700\u540e\u5462\uff0c\u5c0f\u679c\u7ed9\u5c0f\u4f19\u4f34\u5e26\u4e2a\u5c0f\u798f\u5229\uff0c\u5982\u679c\u5c0f\u4f19\u4f34\u89c9\u5f97\u81ea\u5df1\u8fd0\u884c\u4ee3\u7801\u592a\u9ebb\u70e6\uff0c\u53ef\u4ee5\u8bd5\u8bd5\u6211\u4eec\u7684\u4e91\u751f\u4fe1\u5c0f\u5de5\u5177\u54df\uff0c\u91cc\u9762\u6709\u6210\u719f\u7684\u4ee3\u7801\uff0c\u53ea\u8981\u8f93\u5165\u5408\u9002\u7684\u6570\u636e\u5c31\u53ef\u4ee5\u76f4\u63a5\u51fa\u60f3\u8981\u7684\u56fe\u5462\u3002\u8fd9\u662f\u6211\u4eec\u7684\u7f51\u5740http:\/\/www.biocloudservice.com\/home.html<\/a><\/p>\n

\u591a\u591a\u5173\u6ce8\u6211\u4eec\u516c\u4f17\u53f7\uff0c\u4e00\u6765\u5b66\u751f\u4fe1\uff01<\/p>\n","protected":false},"excerpt":{"rendered":"

\u5728\u5f53\u4eca\u4fe1\u606f\u65f6\u4ee3\uff0c\u6570\u636e\u53ef\u89c6\u5316\u6210\u4e3a\u89e3\u8bfb\u590d\u6742\u6570\u636e\u7684\u91cd\u8981\u5de5\u5177\uff0c\u8fd9\u6b21\u5c0f\u679c\u5c06\u5e26\u5c0f\u4f19\u4f34\u4e00\u540c\u63a2\u7d22\u591a\u6837\u5316\u7684\u6570\u636e\u53ef\u89c6\u5316\uff0c\u901a\u8fc7\u7b80\u5355\u7684 […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":""},"categories":[1],"tags":[],"jetpack_featured_media_url":"","_links":{"self":[{"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/28283"}],"collection":[{"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=28283"}],"version-history":[{"count":1,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/28283\/revisions"}],"predecessor-version":[{"id":28293,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/28283\/revisions\/28293"}],"wp:attachment":[{"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=28283"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=28283"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=28283"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}