R 生信数据可视化（聚类热图）

其他 2020-01-11 12:21:34 阅读次数: 0

一.准备示例数据

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文件说明
示例数据，其中数据均为虚拟数据，与实际生物学过程无关
文件名：dataset_heatmap.txt
列分别为：基因，cell1的5个重复样本，cell2的5个重复样本
行代表：每个基因在所有样本的FPKM值

二.绘制聚类热图

1.聚类热图绘制

# 执行前设置====================================
# 清空暂存数据
rm(list=ls())
# 载入R包
library(pheatmap)
# 设置工作目录
setwd("D:/R")

# 整理数据集====================================
# 载入数据
dataset <- read.table('resource/dataset_heatmap.txt',header = TRUE, row.names = 1)
# 截取表达矩阵的一部分数据来绘制热图
exp_ds = dataset[c(1:60),c(1:10)]
# 构建样本分类数据
cell_list=c(rep('cell_1',5),
            rep('cell_2',5))
annotation_c <- data.frame(cell_list)
rownames(annotation_c) <- colnames(exp_ds)

# 绘制热图=====================================
pheatmap(exp_ds, #表达数据
         cluster_rows = T,#行聚类
         cluster_cols = T,#列聚类
         annotation_col =annotation_c, #样本分类数据
         annotation_legend=TRUE, # 显示样本分类
         show_rownames = T,# 显示行名
         show_colnames = T,# 显示列名
         scale = "row", #对行标准化
         color =colorRampPalette(c("#8854d0", "#ffffff","#fa8231"))(100) # 热图基准颜色
)

2. 无分类信息热图

# 执行前设置====================================
# 清空暂存数据
rm(list=ls())
# 载入R包
library(pheatmap)
# 设置工作目录
setwd("D:/R")

# 整理数据集====================================
# 载入数据
dataset <- read.table('resource/dataset_heatmap.txt',header = TRUE, row.names = 1)
# 截取表达矩阵的一部分数据来绘制热图
exp_ds = dataset[c(1:60),c(1:10)]
# 构建样本分类数据
cell_list=c(rep('cell_1',5),
            rep('cell_2',5))
annotation_c <- data.frame(cell_list)
rownames(annotation_c) <- colnames(exp_ds)

# # 绘制热图=====================================1
# pheatmap(exp_ds, #表达数据
#          cluster_rows = T,#行聚类
#          cluster_cols = T,#列聚类
#          annotation_col =annotation_c, #样本分类数据
#          annotation_legend=TRUE, # 显示样本分类
#          show_rownames = T,# 显示行名
#          show_colnames = T,# 显示列名
#          scale = "row", #对行标准化
#          color =colorRampPalette(c("#8854d0", "#ffffff","#fa8231"))(100) # 热图基准颜色
# )

# 将绘制热图部分替换为下列代码
# 绘制热图=====================================2
pheatmap(exp_ds, 
         show_rownames = T,
         show_colnames = T,
         scale = "row", 
         color =colorRampPalette(c("#8854d0", "#ffffff","#fa8231"))(100)
)

3. 无聚类热图

# 执行前设置====================================
# 清空暂存数据
rm(list=ls())
# 载入R包
library(pheatmap)
# 设置工作目录
setwd("D:/R")

# 整理数据集====================================
# 载入数据
dataset <- read.table('resource/dataset_heatmap.txt',header = TRUE, row.names = 1)
# 截取表达矩阵的一部分数据来绘制热图
exp_ds = dataset[c(1:60),c(1:10)]
# 构建样本分类数据
cell_list=c(rep('cell_1',5),
            rep('cell_2',5))
annotation_c <- data.frame(cell_list)
rownames(annotation_c) <- colnames(exp_ds)

# # 绘制热图=====================================1
# pheatmap(exp_ds, #表达数据
#          cluster_rows = T,#行聚类
#          cluster_cols = T,#列聚类
#          annotation_col =annotation_c, #样本分类数据
#          annotation_legend=TRUE, # 显示样本分类
#          show_rownames = T,# 显示行名
#          show_colnames = T,# 显示列名
#          scale = "row", #对行标准化
#          color =colorRampPalette(c("#8854d0", "#ffffff","#fa8231"))(100) # 热图基准颜色
# )

# # 将绘制热图部分替换为下列代码
# # 绘制热图=====================================2
# pheatmap(exp_ds, 
#          show_rownames = T,
#          show_colnames = T,
#          scale = "row", 
#          color =colorRampPalette(c("#8854d0", "#ffffff","#fa8231"))(100)
# )

# 将绘制热图部分替换为下列代码=================3
pheatmap(exp_ds, #表达数据
         cluster_rows = F,
         cluster_cols = F,
         show_rownames = T,
         show_colnames = T,
         scale = "row", 
         color =colorRampPalette(c("#8854d0", "#ffffff","#fa8231"))(100)
)

4.分割聚类树热图

# 执行前设置====================================
# 清空暂存数据
rm(list=ls())
# 载入R包
library(pheatmap)
# 设置工作目录
setwd("D:/R")

# 整理数据集====================================
# 载入数据
dataset <- read.table('resource/dataset_heatmap.txt',header = TRUE, row.names = 1)
# 截取表达矩阵的一部分数据来绘制热图
exp_ds = dataset[c(1:60),c(1:10)]
# 构建样本分类数据
cell_list=c(rep('cell_1',5),
            rep('cell_2',5))
annotation_c <- data.frame(cell_list)
rownames(annotation_c) <- colnames(exp_ds)

# # 绘制热图=====================================1
# pheatmap(exp_ds, #表达数据
#          cluster_rows = T,#行聚类
#          cluster_cols = T,#列聚类
#          annotation_col =annotation_c, #样本分类数据
#          annotation_legend=TRUE, # 显示样本分类
#          show_rownames = T,# 显示行名
#          show_colnames = T,# 显示列名
#          scale = "row", #对行标准化
#          color =colorRampPalette(c("#8854d0", "#ffffff","#fa8231"))(100) # 热图基准颜色
# )

# # 将绘制热图部分替换为下列代码
# # 绘制热图=====================================2
# pheatmap(exp_ds, 
#          show_rownames = T,
#          show_colnames = T,
#          scale = "row", 
#          color =colorRampPalette(c("#8854d0", "#ffffff","#fa8231"))(100)
# )

# # 将绘制热图部分替换为下列代码=================3
# pheatmap(exp_ds, #表达数据
#          cluster_rows = F,
#          cluster_cols = F,
#          show_rownames = T,
#          show_colnames = T,
#          scale = "row", 
#          color =colorRampPalette(c("#8854d0", "#ffffff","#fa8231"))(100)
# )

# 绘制热图======================================4
pheatmap(exp_ds, 
         show_rownames = T,
         show_colnames = T,
         scale = "row", 
         color =colorRampPalette(c("#8854d0", "#ffffff","#fa8231"))(100),
         cutree_cols = 2,
         cutree_rows = 20
)

5.多分组聚类热图

# 清空暂存数据
rm(list=ls())
# 载入R包
library(pheatmap)
# 设置工作目录
setwd("D:/R")

# 整理数据集====================================
# 参数'./resource/dataset.txt'，表示载入E:/R/WorkSpace/baimoc/visualization/resource/dataset_heatmap.txt
dataset <- read.table('resource/dataset_heatmap.txt',header = TRUE, row.names = 1)
# 截取表达矩阵的一部分数据来绘制热图
exp_ds = dataset[c(1:60),c(1:10)]
# 构建样本分类数据
cell_type=c(rep('cell_1',5),
            rep('cell_2',5))
sample_calss=c(rep('normal',5),
               rep('cancer',5))
sample_type=c(rep('control',5),
              rep('case',5))
level = c(1:10)

annotation_c <- data.frame(cell_type, sample_calss, sample_type, level)
rownames(annotation_c) <- colnames(exp_ds)

gene_class=c(rep('good',30),
             rep('bad',30))
gene_type=c(rep('fat',20),
            rep('blood',20),
            rep('Immunology',20))
annotation_r <- data.frame(gene_class, gene_type)
rownames(annotation_r) <- rownames(exp_ds)
# 绘制热图=====================================
pheatmap(exp_ds, #表达数据
         cluster_rows = T,#行聚类
         cluster_cols = T,#列聚类
         annotation_col =annotation_c, #样本分类数据
         annotation_row = annotation_r,
         annotation_legend=TRUE, # 显示样本分类
         show_rownames = T,# 显示行名
         show_colnames = T,# 显示列名
         scale = "row", #对行标准化
         color =colorRampPalette(c("#8854d0", "#ffffff","#fa8231"))(100), # 热图基准颜色
)

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6.分组调色

# 清空暂存数据
rm(list=ls())
# 载入R包
library(pheatmap)
# 设置工作目录
setwd("D:/R")

# 整理数据集====================================
# 参数'./resource/dataset.txt'，表示载入E:/R/WorkSpace/baimoc/visualization/resource/dataset_heatmap.txt
dataset <- read.table('resource/dataset_heatmap.txt',header = TRUE, row.names = 1)
# 截取表达矩阵的一部分数据来绘制热图
exp_ds = dataset[c(1:60),c(1:10)]
# 构建样本分类数据
sample_calss=c(rep('Normal',5),
               rep('Cancer',5))

annotation_c <- data.frame(sample_calss)
rownames(annotation_c) <- colnames(exp_ds)

gene_type=c(rep('Fat',20),
            rep('Blood',20),
            rep('Immunology',20))
annotation_r <- data.frame(gene_type)
rownames(annotation_r) <- rownames(exp_ds)

annotation_colors = list(sample_calss=c(Normal='#F8EFBA', Cancer='#FD7272'),
                         gene_type=c(Fat='#f1f2f6', Blood='#ced6e0', Immunology='#57606f'))
# 绘制热图=====================================
pheatmap(exp_ds, #表达数据
         cluster_rows = T,#行聚类
         cluster_cols = T,#列聚类
         annotation_col =annotation_c, #样本分类数据
         annotation_row = annotation_r,
         annotation_colors = annotation_colors,
         annotation_legend=TRUE, # 显示样本分类
         show_rownames = T,# 显示行名
         show_colnames = T,# 显示列名
         scale = "row", #对行标准化
         color =colorRampPalette(c("#8854d0", "#ffffff","#fa8231"))(100), # 热图基准颜色
)

7. 显示文本

# 清空暂存数据
rm(list=ls())
# 载入R包
library(pheatmap)
# 设置工作目录
setwd("D:/R")

# 整理数据集====================================
# 参数'./resource/dataset.txt'，表示载入E:/R/WorkSpace/baimoc/visualization/resource/dataset_heatmap.txt
dataset <- read.table('resource/dataset_heatmap.txt',header = TRUE, row.names = 1)
# 截取表达矩阵的一部分数据来绘制热图
exp_ds = dataset[c(1:60),c(1:10)]
# 构建样本分类数据
sample_calss=c(rep('Normal',5),
               rep('Cancer',5))

annotation_c <- data.frame(sample_calss)
rownames(annotation_c) <- colnames(exp_ds)

gene_type=c(rep('Fat',20),
            rep('Blood',20),
            rep('Immunology',20))
annotation_r <- data.frame(gene_type)
rownames(annotation_r) <- rownames(exp_ds)

annotation_colors = list(sample_calss=c(Normal='#F8EFBA', Cancer='#FD7272'),
                         gene_type=c(Fat='#f1f2f6', Blood='#ced6e0', Immunology='#57606f'))
# # 绘制热图=====================================6
# pheatmap(exp_ds, #表达数据
#          cluster_rows = T,#行聚类
#          cluster_cols = T,#列聚类
#          annotation_col =annotation_c, #样本分类数据
#          annotation_row = annotation_r,
#          annotation_colors = annotation_colors,
#          annotation_legend=TRUE, # 显示样本分类
#          show_rownames = T,# 显示行名
#          show_colnames = T,# 显示列名
#          scale = "row", #对行标准化
#          color =colorRampPalette(c("#8854d0", "#ffffff","#fa8231"))(100), # 热图基准颜色
# )

# 绘制热图=====================================7
pheatmap(exp_ds, 
         show_rownames = T,
         show_colnames = T,
         scale = "row", 
         color =colorRampPalette(c("#8854d0", "#ffffff","#fa8231"))(100),
         display_numbers = T, # 显示数值
         fontsize_number = 8, # 设置字体大小
         number_color = '#4a4a4a', #设置颜色
         number_format = '%.2f' # 设置显示格式
)

8.去除描边

# 清空暂存数据
rm(list=ls())
# 载入R包
library(pheatmap)
# 设置工作目录
setwd("D:/R")

# 整理数据集====================================
# 参数'./resource/dataset.txt'，表示载入E:/R/WorkSpace/baimoc/visualization/resource/dataset_heatmap.txt
dataset <- read.table('resource/dataset_heatmap.txt',header = TRUE, row.names = 1)
# 截取表达矩阵的一部分数据来绘制热图
exp_ds = dataset[c(1:60),c(1:10)]
# 构建样本分类数据
sample_calss=c(rep('Normal',5),
               rep('Cancer',5))

annotation_c <- data.frame(sample_calss)
rownames(annotation_c) <- colnames(exp_ds)

gene_type=c(rep('Fat',20),
            rep('Blood',20),
            rep('Immunology',20))
annotation_r <- data.frame(gene_type)
rownames(annotation_r) <- rownames(exp_ds)

annotation_colors = list(sample_calss=c(Normal='#F8EFBA', Cancer='#FD7272'),
                         gene_type=c(Fat='#f1f2f6', Blood='#ced6e0', Immunology='#57606f'))
# # 绘制热图=====================================6
# pheatmap(exp_ds, #表达数据
#          cluster_rows = T,#行聚类
#          cluster_cols = T,#列聚类
#          annotation_col =annotation_c, #样本分类数据
#          annotation_row = annotation_r,
#          annotation_colors = annotation_colors,
#          annotation_legend=TRUE, # 显示样本分类
#          show_rownames = T,# 显示行名
#          show_colnames = T,# 显示列名
#          scale = "row", #对行标准化
#          color =colorRampPalette(c("#8854d0", "#ffffff","#fa8231"))(100), # 热图基准颜色
# )

# # 绘制热图=====================================7
# pheatmap(exp_ds, 
#          show_rownames = T,
#          show_colnames = T,
#          scale = "row", 
#          color =colorRampPalette(c("#8854d0", "#ffffff","#fa8231"))(100),
#          display_numbers = T, # 显示数值
#          fontsize_number = 8, # 设置字体大小
#          number_color = '#4a4a4a', #设置颜色
#          number_format = '%.2f' # 设置显示格式
# )

pheatmap(exp_ds, #表达数据=======================8
         show_rownames = T,# 显示行名
         show_colnames = T,# 显示列名
         scale = "row", #对行标准化
         color =colorRampPalette(c("#8854d0", "#ffffff","#fa8231"))(100), # 热图基准颜色
         border_color = 'NA',
)

9.字体相关

# 清空暂存数据
rm(list=ls())
# 载入R包
library(pheatmap)
# 设置工作目录
setwd("D:/R")

# 整理数据集====================================
# 参数'./resource/dataset.txt'，表示载入E:/R/WorkSpace/baimoc/visualization/resource/dataset_heatmap.txt
dataset <- read.table('resource/dataset_heatmap.txt',header = TRUE, row.names = 1)
# 截取表达矩阵的一部分数据来绘制热图
exp_ds = dataset[c(1:60),c(1:10)]
# 构建样本分类数据
sample_calss=c(rep('Normal',5),
               rep('Cancer',5))

annotation_c <- data.frame(sample_calss)
rownames(annotation_c) <- colnames(exp_ds)

gene_type=c(rep('Fat',20),
            rep('Blood',20),
            rep('Immunology',20))
annotation_r <- data.frame(gene_type)
rownames(annotation_r) <- rownames(exp_ds)

annotation_colors = list(sample_calss=c(Normal='#F8EFBA', Cancer='#FD7272'),
                         gene_type=c(Fat='#f1f2f6', Blood='#ced6e0', Immunology='#57606f'))
# # 绘制热图=====================================6
# pheatmap(exp_ds, #表达数据
#          cluster_rows = T,#行聚类
#          cluster_cols = T,#列聚类
#          annotation_col =annotation_c, #样本分类数据
#          annotation_row = annotation_r,
#          annotation_colors = annotation_colors,
#          annotation_legend=TRUE, # 显示样本分类
#          show_rownames = T,# 显示行名
#          show_colnames = T,# 显示列名
#          scale = "row", #对行标准化
#          color =colorRampPalette(c("#8854d0", "#ffffff","#fa8231"))(100), # 热图基准颜色
# )

# # 绘制热图=====================================7
# pheatmap(exp_ds, 
#          show_rownames = T,
#          show_colnames = T,
#          scale = "row", 
#          color =colorRampPalette(c("#8854d0", "#ffffff","#fa8231"))(100),
#          display_numbers = T, # 显示数值
#          fontsize_number = 8, # 设置字体大小
#          number_color = '#4a4a4a', #设置颜色
#          number_format = '%.2f' # 设置显示格式
# )

# pheatmap(exp_ds, #表达数据=======================8
#          show_rownames = T,# 显示行名
#          show_colnames = T,# 显示列名
#          scale = "row", #对行标准化
#          color =colorRampPalette(c("#8854d0", "#ffffff","#fa8231"))(100), # 热图基准颜色
#          border_color = 'NA',
# )

pheatmap(exp_ds, #表达数据=========================9
         show_rownames = T,# 显示行名
         show_colnames = T,# 显示列名
         scale = "row", #对行标准化
         color =colorRampPalette(c("#8854d0", "#ffffff","#fa8231"))(100), # 热图基准颜色
         fontsize = 10, # 全局字体大小，会被后边设置所覆盖
         fontsize_row = 8, # 行字体大小
         fontsize_col = 12, # 列字体大小
         angle_col = 45, # 设置列偏转角度，可选 270, 0, 45, 90, 315，
         gaps_row = T
)

10.调整树高

# 清空暂存数据
rm(list=ls())
# 载入R包
library(pheatmap)
# 设置工作目录
setwd("D:/R")

# 整理数据集====================================
# 参数'./resource/dataset.txt'，表示载入E:/R/WorkSpace/baimoc/visualization/resource/dataset_heatmap.txt
dataset <- read.table('resource/dataset_heatmap.txt',header = TRUE, row.names = 1)
# 截取表达矩阵的一部分数据来绘制热图
exp_ds = dataset[c(1:60),c(1:10)]
# 构建样本分类数据
sample_calss=c(rep('Normal',5),
               rep('Cancer',5))

annotation_c <- data.frame(sample_calss)
rownames(annotation_c) <- colnames(exp_ds)

gene_type=c(rep('Fat',20),
            rep('Blood',20),
            rep('Immunology',20))
annotation_r <- data.frame(gene_type)
rownames(annotation_r) <- rownames(exp_ds)

annotation_colors = list(sample_calss=c(Normal='#F8EFBA', Cancer='#FD7272'),
                         gene_type=c(Fat='#f1f2f6', Blood='#ced6e0', Immunology='#57606f'))
# # 绘制热图=====================================6
# pheatmap(exp_ds, #表达数据
#          cluster_rows = T,#行聚类
#          cluster_cols = T,#列聚类
#          annotation_col =annotation_c, #样本分类数据
#          annotation_row = annotation_r,
#          annotation_colors = annotation_colors,
#          annotation_legend=TRUE, # 显示样本分类
#          show_rownames = T,# 显示行名
#          show_colnames = T,# 显示列名
#          scale = "row", #对行标准化
#          color =colorRampPalette(c("#8854d0", "#ffffff","#fa8231"))(100), # 热图基准颜色
# )

# # 绘制热图=====================================7
# pheatmap(exp_ds, 
#          show_rownames = T,
#          show_colnames = T,
#          scale = "row", 
#          color =colorRampPalette(c("#8854d0", "#ffffff","#fa8231"))(100),
#          display_numbers = T, # 显示数值
#          fontsize_number = 8, # 设置字体大小
#          number_color = '#4a4a4a', #设置颜色
#          number_format = '%.2f' # 设置显示格式
# )

# pheatmap(exp_ds, #表达数据=======================8
#          show_rownames = T,# 显示行名
#          show_colnames = T,# 显示列名
#          scale = "row", #对行标准化
#          color =colorRampPalette(c("#8854d0", "#ffffff","#fa8231"))(100), # 热图基准颜色
#          border_color = 'NA',
# )
# 
# pheatmap(exp_ds, #表达数据=========================9
#          show_rownames = T,# 显示行名
#          show_colnames = T,# 显示列名
#          scale = "row", #对行标准化
#          color =colorRampPalette(c("#8854d0", "#ffffff","#fa8231"))(100), # 热图基准颜色
#          fontsize = 10, # 全局字体大小，会被后边设置所覆盖
#          fontsize_row = 8, # 行字体大小
#          fontsize_col = 12, # 列字体大小
#          angle_col = 45, # 设置列偏转角度，可选 270, 0, 45, 90, 315，
#          gaps_row = T
# )
                       #============================10
pheatmap(exp_ds, 
         show_rownames = T,
         show_colnames = T,
         scale = "row", 
         color =colorRampPalette(c("#8854d0", "#ffffff","#fa8231"))(100),
         treeheight_row = 50,
         treeheight_col = 30
)

11. 聚类方法选择

# 清空暂存数据
rm(list=ls())
# 载入R包
library(pheatmap)
# 设置工作目录
setwd("D:/R")

# 整理数据集====================================
# 参数'./resource/dataset.txt'，表示载入E:/R/WorkSpace/baimoc/visualization/resource/dataset_heatmap.txt
dataset <- read.table('resource/dataset_heatmap.txt',header = TRUE, row.names = 1)
# 截取表达矩阵的一部分数据来绘制热图
exp_ds = dataset[c(1:60),c(1:10)]
# 构建样本分类数据
sample_calss=c(rep('Normal',5),
               rep('Cancer',5))

annotation_c <- data.frame(sample_calss)
rownames(annotation_c) <- colnames(exp_ds)

gene_type=c(rep('Fat',20),
            rep('Blood',20),
            rep('Immunology',20))
annotation_r <- data.frame(gene_type)
rownames(annotation_r) <- rownames(exp_ds)

annotation_colors = list(sample_calss=c(Normal='#F8EFBA', Cancer='#FD7272'),
                         gene_type=c(Fat='#f1f2f6', Blood='#ced6e0', Immunology='#57606f'))
# # 绘制热图=====================================6
# pheatmap(exp_ds, #表达数据
#          cluster_rows = T,#行聚类
#          cluster_cols = T,#列聚类
#          annotation_col =annotation_c, #样本分类数据
#          annotation_row = annotation_r,
#          annotation_colors = annotation_colors,
#          annotation_legend=TRUE, # 显示样本分类
#          show_rownames = T,# 显示行名
#          show_colnames = T,# 显示列名
#          scale = "row", #对行标准化
#          color =colorRampPalette(c("#8854d0", "#ffffff","#fa8231"))(100), # 热图基准颜色
# )

# # 绘制热图=====================================7
# pheatmap(exp_ds, 
#          show_rownames = T,
#          show_colnames = T,
#          scale = "row", 
#          color =colorRampPalette(c("#8854d0", "#ffffff","#fa8231"))(100),
#          display_numbers = T, # 显示数值
#          fontsize_number = 8, # 设置字体大小
#          number_color = '#4a4a4a', #设置颜色
#          number_format = '%.2f' # 设置显示格式
# )

# pheatmap(exp_ds, #表达数据=======================8
#          show_rownames = T,# 显示行名
#          show_colnames = T,# 显示列名
#          scale = "row", #对行标准化
#          color =colorRampPalette(c("#8854d0", "#ffffff","#fa8231"))(100), # 热图基准颜色
#          border_color = 'NA',
# )
# 
# pheatmap(exp_ds, #表达数据=========================9
#          show_rownames = T,# 显示行名
#          show_colnames = T,# 显示列名
#          scale = "row", #对行标准化
#          color =colorRampPalette(c("#8854d0", "#ffffff","#fa8231"))(100), # 热图基准颜色
#          fontsize = 10, # 全局字体大小，会被后边设置所覆盖
#          fontsize_row = 8, # 行字体大小
#          fontsize_col = 12, # 列字体大小
#          angle_col = 45, # 设置列偏转角度，可选 270, 0, 45, 90, 315，
#          gaps_row = T
# )
#                        #============================10
# pheatmap(exp_ds, 
#          show_rownames = T,
#          show_colnames = T,
#          scale = "row", 
#          color =colorRampPalette(c("#8854d0", "#ffffff","#fa8231"))(100),
#          treeheight_row = 50,
#          treeheight_col = 30
# )
                    #============================11                 
pheatmap(exp_ds, 
         show_rownames = T,
         show_colnames = T,
         scale = "row", 
         color =colorRampPalette(c("#8854d0", "#ffffff","#fa8231"))(100),
         clustering_distance_rows = 'euclidean', # 计算聚类间距的算法，可选'correlation', 'euclidean', 'maximum', 'manhattan', 'canberra', 'binary', 'minkowski'
         clustering_method = 'complete', # 聚类方法, 可选'ward', 'ward.D', 'ward.D2', 'single', 'complete', 'average', 'mcquitty', 'median' or 'centroid'
)

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