层次聚类的聚类方法与hclust()函数一样,使用clustering_method_rows和clustering_method_columns两个参数来分别指定行列的聚类方法 Heatmap( exp, name = "expression", col = col_fun, clustering_method_rows = "single" ) 或者使用其他包的聚类函数,如cluster包 library(cluster) Heatmap( exp, name = "expres...
Heatmap(gdata, clustering_method_columns = "complete",#聚类方法,这是默认方法 cluster_columns = TRUE,show_column_names = F,column_names_rot = 90,调整行列名,隐藏列名 column_km = 3,# 用k-means聚类分区 name = "heat") #命名 Heatmap 4 添加注释 ComplexHeatmap包的Complex之处之一就是可以参加...
cluster_columns = dend1,#列方向添加 簇级 树聚类column_split = length(unique(cell.meta$Identity)),#热图列方向按簇拆分#热图主体column_dend_height = unit(2,"cm"),#树的高度clustering_method_columns ="spearson",#树的聚类方法column_title ="_OH_MY_Doheatmap_",#列方向大标题column_title_sid...
clustering_method_columns Method to perform hierarchical clustering, pass tohclust. column_dend_side Should the column dendrogram be put on the top or bottom of the heatmap? column_dend_height height of the column cluster, should be aunitobject. ...
Heatmap(mat, name = "mat", clustering_method_rows = "single") plot of chunk unnamed-chunk-23 2.3.3 自定义聚类树颜色 可以借助dendextend包自定义聚类树的颜色,具体做法如下: 代码语言:text AI代码解释 library(dendextend) 代码语言:txt
Heatmap(mat,name="mat",clustering_method_rows="single") 代码语言:javascript 代码运行次数:0 运行 D:聚类树的渲染 根据聚类结果将聚类树的枝设置不同的颜色 代码语言:javascript 代码运行次数:0 运行 代码语言:javascript 代码运行次数:0 运行 AI代码解释 ...
clustering_method_columns = “complete”, #clustering_method_columns:列聚类方法,可参考hclust column_dend_side = c(“top”, “bottom”), #column_dend_side:列聚类树位置,上(“top”),下(“bottom”) column_dend_height = unit(10, “mm”), 行聚类树的高度,unit对象 show_column_dend = TRUE...
Heatmap(mat, name ="mat",</code><code> cluster_columns = T, </code><code> cluster_rows = F, ## turn off row clustering</code><code>show_column_dend = T, ## hide column dendrogram</code><code>show_row_dend = F,</code><code>column_dend_side ="top", #dendrogram location<...
Heatmap(mat, name = "mat", clustering_distance_rows = function(x, y) 1 - cor(x, y),column_title = "a function that calculates pairwise distance") C:聚类方法 支持hclust()中的聚类方法。 Heatmap(mat, name = "mat", clustering_method_rows = "single") ...
Heatmap(matrix_new, cluster_columns =T, cluster_rows=F, clustering_distance_columns = "pearson", clustering_method_columns = "ward.D") Then, I customized the dendrogram using the following codes hc <- hclust(as.dist(1-cor(t(sample.norm[,1:18]),method="pearson")),method="ward.D") ...