# code onlyfordemonstrationsplit=data.frame(cutree(hclust(dist(mat)),k=2),rep(c("A","B"),9))Heatmap(mat,name="mat",row_split=split) 2.7.4 切片顺序 当row_split/column_split或row_km/column_km设置为分类变量(向量或数据框)时,默认情况下,会对切片的均值应用额外的聚类以显示切片级别的层次...
Heatmap(matrix=mat,name='mat',column_split=factor(rep(c('A','B'),7),'A'),row_split=factor(rep(c('A','B'),7),'B')) 而基于数据框: Heatmap(matrix=mat,name='mat',column_split=data.frame(c(rep(c('A','B'),7),'A'),c(rep(c('C','D'),each=7),'D'))) 其实也很...
Heatmap(mat, name = "mat", row_split = factor(rep(c("A", "B"), 9)), column_split = factor(rep(c("C", "D"), 12))) row_km/column_km与row_split和column_split混合使用。 Heatmap(mat, name = "mat", row_split = rep(c("A", "B"), 9), row_km = 2) 如果对默认的k-...
split = c( rep(c("A","B"),10) , rep("C",4) )ha = HeatmapAnnotation(foo = anno_block(gp = gpar(fill = 2:6), labels = c("AA","BB","CC") ))col_fun = colorRamp2(c(0, 5, 10, 20), c("white", "cornflowerblue", "yellow", "red")) 使用column_split 函数即可按照...
Heatmap(mat, name = "mat", column_km = 3) Heatmap(mat, name = "mat", row_km = 2, column_km = 3) 2.7.2 通过离散型变量分割 # split by a vector Heatmap(mat, name = "mat", row_split = rep(c("A", "B"), 9), column_split = rep(c("C", "D"), 12)) ...
split=c(rep(c("A","B"),10),rep("C",4))ha=HeatmapAnnotation(foo=anno_block(gp=gpar(fill=2:6),labels=c("AA","BB","CC")))col_fun=colorRamp2(c(0,5,10,20),c("white","cornflowerblue","yellow","red")) 使用column_split 函数即可按照指定拆分 ...
Heatmap(mat, name ="mat", column_km =3) plot of chunk unnamed-chunk-43 Heatmap(mat, name ="mat", row_km =2, column_km =3) plot of chunk unnamed-chunk-44 2.7.2 通过离散型变量分割 # split by a vector Heatmap(mat, name ="mat", ...
column_split = annotation_col$CellType) ComplexHeatmap::pheatmap()返回一个Heatmap对象,因此它可以与其他Heatmap/HeatmapAnnotation对象连接。换句话说,你可以使用炫酷的+或者%v%对多个pheatmap水平连接或者垂直连接。 p1 = pheatmap(test, name ="mat1") ...
使用column_split 函数即可按照指定拆分 Heatmap(mat, name = "mat_cluster", column_split = split, top_annotation = ha, cluster_rows = T, cluster_columns = F, #rect_gp = gpar(col="white"), #添加白色格子线 column_title = NULL)
heatmap(mat, name = "mat_cluster", column_split = split, top_annotation = ha, cluster_rows = t, cluster_columns = f, #rect_gp = gpar(col="white"), #添加白色格子线 column_title = null) 3.3 根据富集结果添加...