Cook R生物信息学与算法数据科学与R语言

【r<-包】ComplexHeatmap(5):热图和注释

2019-03-24  本文已影响20人  王诗翔

Author: Zuguang Gu ( z.gu@dkfz.de )
翻译:诗翔
Date: 2018-10-30


热图的图例是由彩色条块、标签和标题组成。 ComplexHeatmap 包根据输入矩阵和注释自动生成图例,也提供了自定义和添加新图例的灵活性。

基本设置

所有热图和行注释的图例放在一起,列注释的图例放在一起。

library(ComplexHeatmap)
library(circlize)

set.seed(123)
mat = matrix(rnorm(80, 2), 8, 10)
mat = rbind(mat, matrix(rnorm(40, -2), 4, 10))
rownames(mat) = paste0("R", 1:12)
colnames(mat) = paste0("C", 1:10)

ha_column = HeatmapAnnotation(df = data.frame(type1 = c(rep("a", 5), rep("b", 5))),
    col = list(type1 = c("a" =  "red", "b" = "blue")))
ha_row = rowAnnotation(df = data.frame(type2 = c(rep("A", 6), rep("B", 6))),
    col = list(type2 = c("A" =  "green", "B" = "orange")), width = unit(1, "cm"))

ht1 = Heatmap(mat, name = "ht1", column_title = "Heatmap 1", top_annotation = ha_column)
ht2 = Heatmap(mat, name = "ht2", column_title = "Heatmap 2")
ht_list = ht1 + ht2 + ha_row

draw(ht_list)

图例放置在哪边可以通过heatmap_legend_sideannotation_legend_side设置。

draw(ht_list, heatmap_legend_side = "left", annotation_legend_side = "bottom")

show_heatmap_legendshow_annotation_legendshow_legend则设置图例是否可见。

draw(ht_list, show_heatmap_legend = FALSE, show_annotation_legend = FALSE)
ha_column = HeatmapAnnotation(df = data.frame(type1 = c(rep("a", 5), rep("b", 5))),
    col = list(type1 = c("a" =  "red", "b" = "blue")), show_legend = FALSE)
ha_row = rowAnnotation(df = data.frame(type2 = c(rep("A", 6), rep("B", 6))),
    col = list(type2 = c("A" =  "green", "B" = "orange")), show_legend = FALSE, width = unit(1, "cm"))

ht1 = Heatmap(mat, name = "ht1", column_title = "Heatmap 1", top_annotation = ha_column)
ht2 = Heatmap(mat, name = "ht2", column_title = "Heatmap 2", show_heatmap_legend = FALSE)
ht1 + ht2 + ha_row

图例自定义

图例本身也可以自定义。通过将自定义选项传入heatmap_legend_param(热图),annotation_legend_param(注释)即可实现,下面是一些可选参数。

下面是例子:

df = data.frame(type = c(rep("a", 5), rep("b", 5)))
ha = HeatmapAnnotation(df = df, col = list(type = c("a" =  "red", "b" = "blue")),
    annotation_legend_param = list(type = list(title = "TYPE", title_gp = gpar(fontsize = 14),
                                               labels_gp = gpar(fontsize = 8))))
ht1 = Heatmap(mat, name = "ht1", column_title = "Heatmap 1", top_annotation = ha)
ht2 = Heatmap(mat, name = "ht2", column_title = "Heatmap 2",
    heatmap_legend_param = list(title = "Heatmap2", title_gp = gpar(fontsize = 8),
        labels_gp = gpar(fontsize = 14)))
ht1 + ht2
ha = HeatmapAnnotation(df = df, col = list(type = c("a" =  "red", "b" = "blue")),
    annotation_legend_param = list(type = list(title = "TYPE", title_gp = gpar(fontsize = 14),
        labels_gp = gpar(fontsize = 8), at = c("a", "b"), labels = c("A", "B"))))
ht1 = Heatmap(mat, name = "ht1", column_title = "Heatmap 1", top_annotation = ha,
    heatmap_legend_param = list(at = c(-3, 0, 3), labels = c("-three", "zero", "+three")))
ht1 + ht2

如果注释水平太多,可以排列为行列。

ha_chr = rowAnnotation(chr = sample(paste0("chr", 1:20), nrow(mat), replace = TRUE),
    annotation_legend_param = list(chr = list(ncol = 2, title = "chromosome", title_position = "topcenter")),
    width = unit(5, "mm"))
ht1 = Heatmap(mat, name = "ht1")
ht1 + ha_chr

或者放在底部

ha_chr = rowAnnotation(chr = sample(paste0("chr", 1:20), nrow(mat), replace = TRUE),
    annotation_legend_param = list(chr = list(nrow = 2, title = "chr", title_position = "leftcenter")),
    width = unit(5, "mm"))
ht1 = Heatmap(mat, name = "ht1", show_heatmap_legend = FALSE)
draw(ht1 + ha_chr, heatmap_legend_side = "bottom")

按列组织:

ha_chr = rowAnnotation(chr = sample(paste0("chr", 1:20), nrow(mat), replace = TRUE),
    annotation_legend_param = list(chr = list(nrow = 2, title = "chr", title_position = "leftcenter", legend_direction = "vertical")),
    width = unit(5, "mm"))
ht1 = Heatmap(mat, name = "ht1", show_heatmap_legend = FALSE)
draw(ht1 + ha_chr, heatmap_legend_side = "bottom")

离散的色块可以用于连续值:

ha = HeatmapAnnotation(df = data.frame(value = runif(10)),
    col = list(value = colorRamp2(c(0, 1), c("white", "blue"))),
    annotation_legend_param = list(color_bar = "discrete", at = c(0, 0.5, 1)))
Heatmap(mat, name = "ht1", top_annotation = ha, heatmap_legend_param = list(color_bar = "discrete"))

一些用户倾向于把图例放在底部:

ht = Heatmap(mat, name = "ht1", heatmap_legend_param = list(legend_direction = "horizontal",
    legend_width = unit(5, "cm"), title_position = "lefttop"))
draw(ht, heatmap_legend_side = "bottom")

相似地,我们可以调整图例的高度

Heatmap(mat, name = "ht1", heatmap_legend_param = list(legend_height = unit(5, "cm")))

如果你想要为所有的热图更改默认设置,使用全局选项。

ht_global_opt(heatmap_legend_title_gp = gpar(fontsize = 16), annotation_legend_labels_gp = gpar(fontface = "italic"))
ha = HeatmapAnnotation(df = data.frame(value = runif(10)),
    col = list(value = colorRamp2(c(0, 1), c("white", "blue"))))
ht1 = Heatmap(mat, name = "ht1", column_title = "Heatmap 1", top_annotation = ha)
ht2 = Heatmap(mat, name = "ht2", column_title = "Heatmap 2", heatmap_legend_param = list(title_gp = gpar(fontsize = 8)))
ht1 + ht2
ht_global_opt(RESET = TRUE)

添加新图例

自定义图例使用一个grob对象列表传入heatmap_legend_listannotation_legend_list参数中。

对于高级用户,可以使用frameGrob()placeGrob()构造图例。

ha = HeatmapAnnotation(points = anno_points(rnorm(10)))
ht2 = Heatmap(mat, name = "ht2", column_title = "Heatmap 2", top_annotation = ha, show_heatmap_legend = FALSE)
lgd = legendGrob(c("dots"), pch = 16)
draw(ht1 + ht2, annotation_legend_list = list(lgd))

现在,该包提供Legend()函数提供grob格式的图例。在下面的例子中,我有有含点的列注释,我们也想要为它们展示图例。

ha = HeatmapAnnotation(points = anno_points(rnorm(10), gp = gpar(col = rep(2:3, each = 5))))
ht = Heatmap(mat, name = "ht2", column_title = "Heatmap 2", top_annotation = ha)
lgd = Legend(at = c("class1", "class2"), title = "points", type = "points", legend_gp = gpar(col = 2:3))
draw(ht, annotation_legend_list = list(lgd))

阅读 this blog link 查看更多说明。

会话信息

sessionInfo()
## R version 3.5.1 Patched (2018-07-12 r74967)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 16.04.5 LTS
## 
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.8-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.8-bioc/R/lib/libRlapack.so
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8       
##  [4] LC_COLLATE=C               LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                  LC_ADDRESS=C              
## [10] LC_TELEPHONE=C             LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
##  [1] stats4    parallel  grid      stats     graphics  grDevices utils     datasets  methods  
## [10] base     
## 
## other attached packages:
##  [1] dendextend_1.9.0      dendsort_0.3.3        cluster_2.0.7-1       IRanges_2.16.0       
##  [5] S4Vectors_0.20.0      BiocGenerics_0.28.0   HilbertCurve_1.12.0   circlize_0.4.4       
##  [9] ComplexHeatmap_1.20.0 knitr_1.20            markdown_0.8         
## 
## loaded via a namespace (and not attached):
##  [1] mclust_5.4.1           Rcpp_0.12.19           mvtnorm_1.0-8          lattice_0.20-35       
##  [5] png_0.1-7              class_7.3-14           assertthat_0.2.0       mime_0.6              
##  [9] R6_2.3.0               GenomeInfoDb_1.18.0    plyr_1.8.4             evaluate_0.12         
## [13] ggplot2_3.1.0          highr_0.7              pillar_1.3.0           GlobalOptions_0.1.0   
## [17] zlibbioc_1.28.0        rlang_0.3.0.1          lazyeval_0.2.1         diptest_0.75-7        
## [21] kernlab_0.9-27         whisker_0.3-2          GetoptLong_0.1.7       stringr_1.3.1         
## [25] RCurl_1.95-4.11        munsell_0.5.0          compiler_3.5.1         pkgconfig_2.0.2       
## [29] shape_1.4.4            nnet_7.3-12            tidyselect_0.2.5       gridExtra_2.3         
## [33] tibble_1.4.2           GenomeInfoDbData_1.2.0 viridisLite_0.3.0      crayon_1.3.4          
## [37] dplyr_0.7.7            MASS_7.3-51            bitops_1.0-6           gtable_0.2.0          
## [41] magrittr_1.5           scales_1.0.0           stringi_1.2.4          XVector_0.22.0        
## [45] viridis_0.5.1          flexmix_2.3-14         bindrcpp_0.2.2         robustbase_0.93-3     
## [49] fastcluster_1.1.25     HilbertVis_1.40.0      rjson_0.2.20           RColorBrewer_1.1-2    
## [53] tools_3.5.1            fpc_2.1-11.1           glue_1.3.0             trimcluster_0.1-2.1   
## [57] DEoptimR_1.0-8         purrr_0.2.5            colorspace_1.3-2       GenomicRanges_1.34.0  
## [61] prabclus_2.2-6         bindr_0.1.1            modeltools_0.2-22
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