通常,我的习惯是使用gplots包中的heatmap.2函数来进行画图。不过这个函数中不能对聚类分析(clustering)到方法进行调整,于是,小小写一段代码即能使用不同的聚类分析方法来对heatmap进行聚类整合。 1#There are 7 methods to make cluster in the function hclust in R2Cluster_Method<-c("ward","single","complet...
main表示heatmap图的title clustering_method表示聚类方法,值可以是hclust的任何一种,如"ward.D", "single", "complete", "average", "mcquitty", "median", "centroid", "ward.D2"。 cluster_rows表示以行聚类,值可以是FALSE或TRUE cluster_cols表示以列聚类,值可以是FALSE或TRUE clustering_distance_rows表示...
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 row_split = 3, column_split = 3,# 分区 name = "heat") #命名 Heatmap k-means聚类分区 Heatmap(gdata, clustering_method_...
Heatmap(mat, name = "mat", clustering_method_rows = "single") 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: library(dendextend) ## ## --- ## Welcome to dendextend version 1.15.1 ## Type citation('dendextend') for how to cite the package. ## #...
clustering_distance_rows = "euclidean":指定行的聚类距离计算方式为欧几里得距离。 clustering_method = "complete":指定聚类的方法为完整链接法。 main = "Ordered Heatmap":为热图添加标题。 步骤5:优化和展示热图 根据需求,可以调整热图的颜色和其他参数,例如添加行和列的注释。以下代码示例展示了如何自定义热图的...
Heatmap(mat,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_si...
# Perform hierarchical clustering on the rows (genes)show_row_dend=FALSE,# Do not display the dendrogram for rowscluster_columns=TRUE,# Perform hierarchical clustering on the columns (samples)cluster_column_slices=FALSE,# Do not perform clustering on column slices after splittingshow_column_dend=...
Heatmap(mat, name = "mat", clustering_distance_rows = function(m) dist(m), column_title = "a function that calculates distance matrix") #距离 Heatmap(mat, name = "mat", clustering_distance_rows = function(x, y) 1 - cor(x, y), column_title = "a function that calculates pairwise...
Simple clustering and heat maps can be produced from the "heatmap" function in R. However, the "heatmap" function lacks certain functionalities and customizability, preventing it from generating advanced heat maps and dendrograms. To tackle the limitations of the "heatmap" function, we have ...
# clustering is similar as previous heatmap with branches in some nodes in the dendrogram flippedHeatmap(mat, name = "mat", row_split = factor(rep(LETTERS[1:3], 6), levels = LETTERS[3:1]), column_split = factor(rep(letters[1:6], 4), levels = letters[6:1])) plot of chunk...