【ComplexHeatmap】Nature的突变热图还是太全面了

# === Step 1: 模拟数据 ===
set.seed()
samples <- paste0("S",:)
genes <-c("TP53","PIK3CA","ESR1","GATA3","CDH1","PTEN","RB1","NF1","AKT1","MAP3K1")
alterations <-c("Hotspot","Missense","Amplification","Stop_codon","Splice_site","Frameshift","Homozygous_deletion","Insertion_deletion")
mutation_matrix <- matrix(
  sample(c(alterations,NA),*, replace =TRUE, prob =c(rep(0.1,),0.2)),
  nrow =,dimnames=list(genes, samples)
)
mutation_list <- as.matrix(mutation_matrix)
mutation_list[is.na(mutation_list)]<-""# 必须是 "" 而不是 NA

# === Step 2: 模拟临床注释 ===
clinical <- data.frame(
  Sample = samples,
  Subtype = sample(c("HR+/HER2-","HER2+","TNBC","UN"),, replace =TRUE),
  Biopsy = sample(c("Breast","Lung","Liver","Lymph node","Skin","Other"),, replace =TRUE),
  ChemoLines = sample(c("0","1","2",">3"),, replace =TRUE),
  HormoneTx = sample(c("Yes","No"),, replace =TRUE),
  CDK4i = sample(c("Yes","No"),, replace =TRUE),
  MTORi = sample(c("Yes","No"),, replace =TRUE),
  Delay = sample(c("0-12m","12-24m",">24m"),, replace =TRUE)
)

# === Step 3: 设置突变颜色 ===
col =c(
"Hotspot"="#E41A1C","Missense"="#377EB8","Amplification"="#4DAF4A",
"Stop_codon"="#984EA3","Splice_site"="#FF7F00","Frameshift"="#FFFF33",
"Homozygous_deletion"="#A65628","Insertion_deletion"="#F781BF"
)

alter_fun =list(
  background =function(x, y, w, h){
    grid.rect(x, y, w, h, gp = gpar(fill ="#F0F0F0", col =NA))
}
)

for(mut innames(col)){
  alter_fun[[mut]]<- local({
    mycol <- col[[mut]]
function(x, y, w, h){
      grid.rect(x, y, w *0.9, h *0.9, gp = gpar(fill = mycol, col =NA))
}
})
}

=== Step : 设置颜色映射 ===
subtype_col <-c("HR+/HER2-"="#66C2A5","HER2+"="#FC8D62","TNBC"="#8DA0CB","UN"="grey60")
biopsy_col <-c("Breast"="orange","Lung"="skyblue","Liver"="red","Lymph node"="green","Skin"="purple","Other"="gray")

# === Step 5: 构建顶部注释 ===
# 顶部柱状图（每列突变数）
mutation_count <- colSums(mutation_list !="")

top_anno <- HeatmapAnnotation(
  MutCount = anno_barplot(
    mutation_count,
    gp = gpar(fill = subtype_col[clinical$Subtype]),# 按亚型着色
    border =FALSE
),
  Subtype = clinical$Subtype,
  Biopsy = clinical$Biopsy,
  Chemo = clinical$ChemoLines,
  HormoneTx = clinical$HormoneTx,
  CDK4i = clinical$CDK4i,
  MTORi = clinical$MTORi,
  Delay = clinical$Delay,
  col =list(
    Subtype = subtype_col,
    Biopsy = biopsy_col,
    Chemo =c("0"="white","1"="lightblue","2"="blue",">3"="darkblue"),
    HormoneTx =c("Yes"="pink","No"="gray"),
    CDK4i =c("Yes"="gold","No"="gray"),
    MTORi =c("Yes"="green","No"="gray"),
    Delay =c("0-12m"="#FFFFB3","12-24m"="#FDB462",">24m"="#B3DE69")
),
  annotation_height = unit(c(,rep(,)),"mm"),
  annotation_name_gp = gpar(fontsize =),# 设置注释标签字体
  gp = gpar(fontsize =)# 设置分类注释的字体
)

# 计算二值矩阵 (TRUE/FALSE)
mut_binary_mat <- matrix(FALSE, nrow =length(genes), ncol =length(subtypes),
dimnames=list(genes, subtypes))

# 生成颜色矩阵，TRUE用对应亚型颜色，FALSE用白色
col_mat <- matrix("white", nrow = nrow(mut_binary_mat), ncol = ncol(mut_binary_mat),
dimnames=dimnames(mut_binary_mat))
for(j inseq_len(ncol(mut_binary_mat))){
  col_mat[mut_binary_mat[, j], j]<- subtype_col[colnames(mut_binary_mat)[j]]
}

col_mat <- matrix("white", nrow = nrow(mut_binary_mat), ncol = ncol(mut_binary_mat),
dimnames=dimnames(mut_binary_mat))

for(j inseq_len(ncol(mut_binary_mat))){
  col_mat[mut_binary_mat[, j], j]<- subtype_col[colnames(mut_binary_mat)[j]]
}

# Heatmap() 支持绘制颜色矩阵，设置 col = structure(unique_colors, names=unique_colors)
unique_cols <- unique(as.vector(col_mat))
names(unique_cols)<- unique_cols

ht_left <- Heatmap(col_mat,
        col = unique_cols,
        cluster_rows =FALSE,
        cluster_columns =FALSE,
        show_row_names =FALSE,
        show_column_names =TRUE,
        show_heatmap_legend =FALSE)

ht_main <- oncoPrint(
  mutation_list,
  alter_fun = alter_fun,
  col = col,
  show_heatmap_legend =FALSE,
  top_annotation = top_anno,
  show_column_names =FALSE,
  column_order = order(clinical$Subtype),
  remove_empty_columns =TRUE,
  row_names_gp = gpar(fontsize =),
)

draw(ht_left + ht_main, merge_legend =TRUE)