使用 pheatmap 进行绘图

Question

我的目标是根据

Scissor.select

对列进行聚类和排序，使用规定的注释颜色，并删除列标签（即图底部的标签）。 但是，我的代码没有对列进行排序，也没有使用注释调色板。

library(pheatmap)
annotation_colors <- list("Scissor- cells"='#ffa600',"Background Cells"='#c1c1c1',"Scissor+ cells"='#003f5c')

pdf("Plots/gsva_scissor_singlecell_KIRP.pdf", width=10, height=10)
pheatmap(top20.genesets, 
         cluster_cols = F,  
         cluster_rows = F,
         labels_row = NULL,
         annotation_colors=annotation_colors,
         clustering_distance_rows = "euclidean",  # You can choose a different distance metric
         clustering_distance_cols = "euclidean",  # You can choose a different distance metric
         main = "Genesets of Scissor.select",
         annotation_col = Scissor.select)
dev.off()

输入：

> dput(top20.genesets[1:20,1:20])
structure(c(0.490698593595098, -0.104087580459566, -0.104087580459566, 
-0.104087580459566, -0.104087580459566, -0.104087580459566, -0.104087580459566, 
-0.104087580459566, -0.104087580459566, -0.104087580459566, -0.104087580459566, 
-0.104087580459566, -0.104087580459566, -0.104087580459566, -0.104087580459566, 
-0.104087580459566, -0.104087580459566, -0.104087580459566, -0.104087580459566, 
-0.104087580459566, 0.421306873288721, 0.371741358784165, 0.371741358784165, 
0.371741358784165, 0.371741358784165, 0.371741358784165, 0.371741358784165, 
0.371741358784165, 0.371741358784165, 0.371741358784165, 0.371741358784165, 
0.371741358784165, 0.371741358784165, 0.371741358784165, 0.371741358784165, 
0.371741358784165, 0.371741358784165, 0.371741358784165, 0.371741358784165, 
0.371741358784165, 0.361828255883254, 0.292436535576877, 0.292436535576877, 
0.292436535576877, 0.292436535576877, 0.292436535576877, 0.292436535576877, 
0.292436535576877, 0.292436535576877, 0.292436535576877, 0.292436535576877, 
0.292436535576877, 0.292436535576877, 0.292436535576877, 0.292436535576877, 
0.292436535576877, 0.292436535576877, 0.292436535576877, 0.292436535576877, 
0.292436535576877, 0.431219976189632, 0.183392403666855, 0.183392403666855, 
0.183392403666855, 0.183392403666855, 0.183392403666855, 0.183392403666855, 
0.183392403666855, 0.183392403666855, 0.183392403666855, 0.183392403666855, 
0.183392403666855, 0.183392403666855, 0.183392403666855, 0.183392403666855, 
0.183392403666855, 0.183392403666855, 0.183392403666855, 0.183392403666855, 
0.183392403666855, 0.470872387793276, 0.421306873288721, 0.421306873288721, 
0.421306873288721, 0.421306873288721, 0.421306873288721, 0.421306873288721, 
0.421306873288721, 0.421306873288721, 0.421306873288721, 0.421306873288721, 
0.421306873288721, 0.421306873288721, 0.421306873288721, 0.421306873288721, 
0.421306873288721, 0.421306873288721, 0.421306873288721, 0.421306873288721, 
0.421306873288721, 0.470872387793276, 0.421306873288721, 0.421306873288721, 
0.421306873288721, 0.421306873288721, 0.421306873288721, 0.421306873288721, 
0.421306873288721, 0.421306873288721, 0.421306873288721, 0.421306873288721, 
0.421306873288721, 0.421306873288721, 0.421306873288721, 0.421306873288721, 
0.421306873288721, 0.421306873288721, 0.421306873288721, 0.421306873288721, 
0.421306873288721, 0.460959284892365, 0.351915152982343, 0.351915152982343, 
0.351915152982343, 0.351915152982343, 0.351915152982343, 0.351915152982343, 
0.351915152982343, 0.351915152982343, 0.351915152982343, 0.351915152982343, 
0.351915152982343, 0.351915152982343, 0.351915152982343, 0.351915152982343, 
0.351915152982343, 0.351915152982343, 0.351915152982343, 0.351915152982343, 
0.351915152982343, 0.480785490694187, 0.361828255883254, 0.361828255883254, 
0.361828255883254, 0.361828255883254, 0.361828255883254, 0.361828255883254, 
0.361828255883254, 0.361828255883254, 0.361828255883254, 0.361828255883254, 
0.361828255883254, 0.361828255883254, 0.361828255883254, 0.361828255883254, 
0.361828255883254, 0.361828255883254, 0.361828255883254, 0.361828255883254, 
0.361828255883254, 0.470872387793276, 0.401480667486898, 0.401480667486898, 
0.401480667486898, 0.401480667486898, 0.401480667486898, 0.401480667486898, 
0.401480667486898, 0.401480667486898, 0.401480667486898, 0.401480667486898, 
0.401480667486898, 0.401480667486898, 0.401480667486898, 0.401480667486898, 
0.401480667486898, 0.401480667486898, 0.401480667486898, 0.401480667486898, 
0.401480667486898, 0.381654461685076, 0.470872387793276, 0.470872387793276, 
0.470872387793276, 0.470872387793276, 0.470872387793276, 0.470872387793276, 
0.470872387793276, 0.470872387793276, 0.470872387793276, 0.470872387793276, 
0.470872387793276, 0.470872387793276, 0.470872387793276, 0.470872387793276, 
0.470872387793276, 0.470872387793276, 0.470872387793276, 0.470872387793276, 
0.470872387793276, 0.332088947180521, 0.451046181991454, 0.451046181991454, 
0.451046181991454, 0.451046181991454, 0.451046181991454, 0.451046181991454, 
0.451046181991454, 0.451046181991454, 0.451046181991454, 0.451046181991454, 
0.451046181991454, 0.451046181991454, 0.451046181991454, 0.451046181991454, 
0.451046181991454, 0.451046181991454, 0.451046181991454, 0.451046181991454, 
0.451046181991454, 0.451046181991454, 0.391567564585987, 0.391567564585987, 
0.391567564585987, 0.391567564585987, 0.391567564585987, 0.391567564585987, 
0.391567564585987, 0.391567564585987, 0.391567564585987, 0.391567564585987, 
0.391567564585987, 0.391567564585987, 0.391567564585987, 0.391567564585987, 
0.391567564585987, 0.391567564585987, 0.391567564585987, 0.391567564585987, 
0.391567564585987, 0.342002050081432, 0.441133079090543, 0.441133079090543, 
0.441133079090543, 0.441133079090543, 0.441133079090543, 0.441133079090543, 
0.441133079090543, 0.441133079090543, 0.441133079090543, 0.441133079090543, 
0.441133079090543, 0.441133079090543, 0.441133079090543, 0.441133079090543, 
0.441133079090543, 0.441133079090543, 0.441133079090543, 0.441133079090543, 
0.441133079090543, 0.490698593595098, 0.242871021072321, 0.242871021072321, 
0.242871021072321, 0.242871021072321, 0.242871021072321, 0.242871021072321, 
0.242871021072321, 0.242871021072321, 0.242871021072321, 0.242871021072321, 
0.242871021072321, 0.242871021072321, 0.242871021072321, 0.242871021072321, 
0.242871021072321, 0.242871021072321, 0.242871021072321, 0.242871021072321, 
0.242871021072321, 0.381654461685076, 0.441133079090543, 0.441133079090543, 
0.441133079090543, 0.441133079090543, 0.441133079090543, 0.441133079090543, 
0.441133079090543, 0.441133079090543, 0.441133079090543, 0.441133079090543, 
0.441133079090543, 0.441133079090543, 0.441133079090543, 0.441133079090543, 
0.441133079090543, 0.441133079090543, 0.441133079090543, 0.441133079090543, 
0.441133079090543, -0.00495655145045554, 0.451046181991454, 0.451046181991454, 
0.451046181991454, 0.451046181991454, 0.451046181991454, 0.451046181991454, 
0.451046181991454, 0.451046181991454, 0.451046181991454, 0.451046181991454, 
0.451046181991454, 0.451046181991454, 0.451046181991454, 0.451046181991454, 
0.451046181991454, 0.451046181991454, 0.451046181991454, 0.451046181991454, 
0.451046181991454, 0.480785490694187, 0.451046181991454, 0.451046181991454, 
0.451046181991454, 0.451046181991454, 0.451046181991454, 0.451046181991454, 
0.451046181991454, 0.451046181991454, 0.451046181991454, 0.451046181991454, 
0.451046181991454, 0.451046181991454, 0.451046181991454, 0.451046181991454, 
0.451046181991454, 0.451046181991454, 0.451046181991454, 0.451046181991454, 
0.451046181991454, 0.480785490694187, 0.431219976189632, 0.431219976189632, 
0.431219976189632, 0.431219976189632, 0.431219976189632, 0.431219976189632, 
0.431219976189632, 0.431219976189632, 0.431219976189632, 0.431219976189632, 
0.431219976189632, 0.431219976189632, 0.431219976189632, 0.431219976189632, 
0.431219976189632, 0.431219976189632, 0.431219976189632, 0.431219976189632, 
0.431219976189632, 0.431219976189632, 0.480785490694187, 0.480785490694187, 
0.480785490694187, 0.480785490694187, 0.480785490694187, 0.480785490694187, 
0.480785490694187, 0.480785490694187, 0.480785490694187, 0.480785490694187, 
0.480785490694187, 0.480785490694187, 0.480785490694187, 0.480785490694187, 
0.480785490694187, 0.480785490694187, 0.480785490694187, 0.480785490694187, 
0.480785490694187, 0.470872387793276, 0.342002050081432, 0.342002050081432, 
0.342002050081432, 0.342002050081432, 0.342002050081432, 0.342002050081432, 
0.342002050081432, 0.342002050081432, 0.342002050081432, 0.342002050081432, 
0.342002050081432, 0.342002050081432, 0.342002050081432, 0.342002050081432, 
0.342002050081432, 0.342002050081432, 0.342002050081432, 0.342002050081432, 
0.342002050081432), dim = c(20L, 20L), dimnames = list(c("REACTOME_GLUTAMATE_AND_GLUTAMINE_METABOLISM", 
"REACTOME_CONSTITUTIVE_SIGNALING_BY_ABERRANT_PI3K_IN_CANCER", 
"REACTOME_CONSTITUTIVE_SIGNALING_BY_OVEREXPRESSED_ERBB2", "REACTOME_DOWNREGULATION_OF_ERBB2_ERBB3_SIGNALING", 
"REACTOME_DOWNREGULATION_OF_ERBB2_SIGNALING", "REACTOME_ERBB2_ACTIVATES_PTK6_SIGNALING", 
"REACTOME_ERBB2_REGULATES_CELL_MOTILITY", "REACTOME_GRB2_EVENTS_IN_ERBB2_SIGNALING", 
"REACTOME_GRB7_EVENTS_IN_ERBB2_SIGNALING", "REACTOME_PI3K_AKT_SIGNALING_IN_CANCER", 
"REACTOME_PI3K_EVENTS_IN_ERBB2_SIGNALING", "REACTOME_SEMA4D_IN_SEMAPHORIN_SIGNALING", 
"REACTOME_SEMA4D_INDUCED_CELL_MIGRATION_AND_GROWTH_CONE_COLLAPSE", 
"REACTOME_SHC1_EVENTS_IN_ERBB2_SIGNALING", "REACTOME_SIGNALING_BY_ERBB2", 
"REACTOME_SIGNALING_BY_ERBB2_ECD_MUTANTS", "REACTOME_SIGNALING_BY_ERBB2_IN_CANCER", 
"REACTOME_SIGNALING_BY_PTK6", "REACTOME_TFAP2_AP_2_FAMILY_REGULATES_TRANSCRIPTION_OF_GROWTH_FACTORS_AND_THEIR_RECEPTORS", 
"REACTOME_TRANSCRIPTIONAL_REGULATION_BY_THE_AP_2_TFAP2_FAMILY_OF_TRANSCRIPTION_FACTORS"
), c("AAACCTGAGCGTTTAC.p906", "AAACCTGAGTGCAAGC.p906", "AAACCTGTCATCACCC.p906", 
"AAACGGGCAGTTAACC.p906", "AAACGGGGTGCTAGCC.p906", "AAACGGGTCACAAACC.p906", 
"AAAGATGAGCGTTGCC.p906", "AAAGATGCAAGGTTTC.p906", "AAAGATGGTCTCTCTG.p906", 
"AAAGATGTCGCGGATC.p906", "AAAGCAAAGACCTAGG.p906", "AAAGCAAGTAAAGGAG.p906", 
"AAAGTAGAGAGTCTGG.p906", "AAATGCCGTGTGCCTG.p906", "AACACGTAGAGACGAA.p906", 
"AACACGTTCCCATTTA.p906", "AACCATGGTATAATGG.p906", "AACCATGTCGTTGACA.p906", 
"AACCGCGTCATAGCAC.p906", "AACGTTGCAACTGCGC.p906")))

> dput(Scissor.select[1:20,])
c("Scissor- cells", "Background Cells", "Scissor- cells", "Scissor- cells", 
"Scissor+ cells", "Scissor+ cells", "Scissor- cells", "Scissor- cells", 
"Scissor- cells", "Scissor+ cells", "Scissor+ cells", "Scissor- cells", 
"Scissor+ cells", "Scissor- cells", "Scissor- cells", "Scissor+ cells", 
"Scissor+ cells", "Scissor+ cells", "Scissor+ cells", "Scissor+ cells"
)

电流输出：

所需格式：

Answer 1

如果您查看 pheatmap 文档，他们有一些很好的示例。 您可以通过将 Scissor.select 从列表更改为数据框并将行名称设置为矩阵的列名称来实现图表。 您还想为注释颜色创建一个命名列表，以便 pheatmap 知道将颜色与您的注释相关联。

Scissor.select <- data.frame(CellType = c("Scissor- cells", "Background Cells", "Scissor- cells", "Scissor- cells", 
  "Scissor+ cells", "Scissor+ cells", "Scissor- cells", "Scissor- cells", 
  "Scissor- cells", "Scissor+ cells", "Scissor+ cells", "Scissor- cells", 
  "Scissor+ cells", "Scissor- cells", "Scissor- cells", "Scissor+ cells", 
  "Scissor+ cells", "Scissor+ cells", "Scissor+ cells", "Scissor+ cells"
))
rownames(Scissor.select) <- colnames(top20.genesets)

annotation_colors <- list(CellType = c("Scissor- cells"='#ffa600',"Background Cells"='#c1c1c1',"Scissor+ cells"='#003f5c'))

pdf("Plots/gsva_scissor_singlecell_KIRP.pdf", width=10, height=10)
pheatmap::pheatmap(top20.genesets, 
         cluster_cols = T,  
         cluster_rows = F,
         show_colnames = F,
         annotation_col = Scissor.select,
         annotation_colors=annotation_colors,
         clustering_distance_rows = "euclidean",  # You can choose a different distance metric
         clustering_distance_cols = "euclidean",  # You can choose a different distance metric
         main = "Genesets of Scissor.select"
)
dev.off()