GOHeat(chord, nlfc = 1) # 1是用logfc,0是不用 圆形聚类图 竟然也是用ggplot2画出来的,学习! GOCluster(circ, process = go_list$process, #感兴趣的条目 metric = "euclidean", # 聚类时计算距离的方法 clust = "average", # 聚类的方法 clust.by = 'logFC', # 聚类依据 term.width = 2, ...
GOHeat(chord[,-8], nlfc = 0) #用热图展现 GOHeat(chord, nlfc = 1, fill.col = c('red', 'yellow', 'green')) #更改热图颜色 GOCluster(circ, EC$process, clust.by = 'logFC', term.width = 2) #展现GO聚类 GOCluster(circ, EC$process, clust.by = 'term', lfc.col = c('dark...
GOHeat(chord, nlfc = 1) # 1是用logfc,0是不用 plot of chunk unnamed-chunk-18 圆形聚类图 竟然也是用ggplot2画出来的,学习! 代码语言:javascript 代码运行次数:0 运行 AI代码解释 GOCluster(circ, process = go_list$process, #感兴趣的条目 metric = "euclidean", # 聚类时计算距离的方法 clust ...
GOCluster(circ, EC$process, clust.by = 'logFC', term.width = 2) image.png 韦恩图 代码语言:javascript 代码运行次数:0 运行 AI代码解释 l1 <- subset(circ, term == 'heart development', c(genes,logFC)) l2 <- subset(circ, term == 'plasma membrane', c(genes,logFC)) l3 <- subset...
简介:`GOplot`是R中的一个宝藏包,用于GO富集分析的创新可视化。它提供多种图表类型,如GOBar、GOBubble、GOCircle、GOChord、GOHeat和GOCluster,以及GOVenn。通过调整参数,用户可自定义颜色、大小和排序。例如,GOBar和GOBubble展示富集条形和气泡,GOCircle以环形图表示,GOChord描绘基因和过程间关系,而GOHeat和GOClust...
GOCluster(circ, EC$process, clust.by = 'term', lfc.col =c('darkgoldenrod1', 'black', 'cyan1')) 接下来是韦恩图的绘制: l1 <-subset(circ, term == 'heart development', c(genes,logFC)) l2 <-subset(circ, term == 'plasma membrane', c(genes,logFC)) ...
如何使用GOplot画一张精美的GO分析图
Plot clusters can go anywhere, and can simply be invisible boundaries if you want. Actually set the plot biome... with our BiomeGenerator addon Notifications/Per plot time + weather... using the extensive flag system Complete control over plot composition... ...
GOCluster(circ,EC$process,clust.by='term',lfc.col=c('darkgoldenrod1','black','cyan1')) ## Warning: Using size for a discrete variable is not advised. ## Warning: Removed 7 rows containing missing values (geom_point). l1<-subset(circ,term=='heart development',c(genes,logFC))l2<-...
GOCluster(circ, EC$process, clust.by = 'logFC', term.width = 2) GOCluster(circ, EC$process, clust.by = 'term', lfc.col = c('darkgoldenrod1', 'black', 'cyan1')) Venn diagram l1 <- subset(circ, term == 'heart development', c(genes,logFC))l2 <- subset(circ, term == '...