我使用下面的代码在R中获取ComplexHeatmap。我尝试了两种不同的方法，在代码的注解中指定。两者之间唯一的区别是，在第一种情况下，我依靠ComplexHeatmap来进行聚类，而在第二种情况下，我自己进行聚类，并将hclust对象传递给Heatmap。从理论上讲，我应该得到相同的数据聚类，但我没有。我不知道我误解了什么。我知道热图不应该是相同的，但我相信样本的聚类应该是相同的。我已经在this Github上分享了这方面的数据。PS：聚类相似（但以相反的顺序显示），但不完全相同。

lung = read.csv("all_lung.csv")
lung["subtype_grouped_meso"] = lung["subtype"]
lung[lung["subtype"] == "Not.Otherwise.Specified" | lung["subtype"] == 
       "Epithelioid" | lung["subtype"] == "Sarcomatoid" | 
       lung["subtype"] == "Biphasic", 
     "subtype_grouped_meso"] = "meso"
subtype = lung[["subtype_grouped_meso"]]
rownames(lung) = lung[["X"]]
lung = lung[, chr_keep]

subtype_colors <- c(
  "Adeno" = "red",
  "Squamous" = "green",
  "SCLC" = "blue",
  "meso" = "orange"
)

lung = t(lung)
column_ha <- HeatmapAnnotation(subtype = subtype, 
                               counts = log10(colSums(lung)),
                               col = list(subtype = 
                                            subtype_colors))

# Method 1
Heatmap(lung,
        top_annotation = column_ha,
        clustering_distance_columns = "pearson",
        clustering_method_columns = "ward.D",
        cluster_rows = F,
        show_column_names = F,
        show_row_names = F, 
        show_row_dend = F)

# Method 2 (manually do the clustering) 
cor_matrix <- cor(lung, method = "pearson")
cor_distance <- as.dist(1 - cor_matrix)
hc <- hclust(cor_distance, method = "ward.D")
Heatmap(cor_matrix, 
        top_annotation = column_ha,
        name = "correlation",
        cluster_columns = hc,
        cluster_rows = hc,
        show_column_names = F,
        show_row_names = F,
        show_row_dend = F,
        col = colorRamp2(c(-1, 0, 1), c("blue", "white", "red")))