1. 使用
包绘制热图
1.1 基本用法
# 安装并加载所需的R包
# install.packages("pheatmap")
# library(devtools)
# install_github("raivokolde/pheatmap")
library(pheatmap)
library(RColorBrewer)
# 生成示例数据
test = matrix(rnorm(200), 20, 10)
test[1:10, seq(1, 10, 2)] = test[1:10, seq(1, 10, 2)] + 3
test[11:20, seq(2, 10, 2)] = test[11:20, seq(2, 10, 2)] + 2
test[15:20, seq(2, 10, 2)] = test[15:20, seq(2, 10, 2)] + 4
colnames(test) = paste("Test", 1:10, sep = "")
rownames(test) = paste("Gene", 1:20, sep = "")
# 简单绘图
pheatmap(test)
pheatmap-1
1.2 其他参数设置
# 添加行和列的注释信息
annotation_col = data.frame(CellType = factor(rep(c("CT1", "CT2"), 5)), Time = 1:5)
rownames(annotation_col) = paste("Test", 1:10, sep = "")
annotation_row = data.frame(GeneClass = factor(rep(c("Path1", "Path2", "Path3"), c(10, 4, 6))))
rownames(annotation_row) = paste("Gene", 1:20, sep = "")
# 设置注释信息的颜色
ann_colors = list(
Time = c("white", "firebrick"),
CellType = c(CT1 = "#1B9E77", CT2 = "#D95F02"),
GeneClass = c(Path1 = "#7570B3", Path2 = "#E7298A", Path3 = "#66A61E")
)
pheatmap(test,
scale = "row", # 进行均一化的方向，值为 "row", "column" 或者"none"
# kmeans_k = 2, # 用kmean_k把列的特征聚类，2即是聚成2列
clustering_distance_rows = "euclidean", clustering_distance_cols = "euclidean", # 表示行、列聚类使用的度量方法，默认为"euclidean"，也可为 "correlation"即按照Pearson correlation方法进行聚类
clustering_method = "complete", # 选择聚类方法，包括："ward", "ward.D", "ward.D2", "single", "complete", "average", "mcquitty", "median", "centroid"
cluster_rows = T, cluster_cols = T, # 对行或列进行聚类
cutree_rows = NA, cutree_cols = 6, # 若进行了行、列聚类，根据行、列聚类数量分隔热图行
# gaps_row = NULL, #仅在未进行行聚类时使用，表示在行方向上热图的隔断位置
# gaps_col = c(1,2,3,4,5,6)) #仅在未进行列聚类时使用，表示在列方向上热图的隔断位置
treeheight_row = 30, treeheight_col = 30, #对行、列聚类树高度调整
border_color = NA, #热图每个小的单元格边框的颜色，默认为 "grey60"
cellwidth = 30, cellheight = 10, #单个单元格的宽度/高度，默认为 “NA”
# display_numbers = matrix(ifelse(test > 1, "***", ""), nrow = nrow(test)), #使用"display_numbers" 根据热图单元格的数值进行标记,若该单元格原始数值大于1，则为 "***"，否则为 " "；
display_numbers = T, #是否在单元格上显示原始数值或按照特殊条件进行区分标记，
fontsize_number = 6, #热图上显示数字的字体大小
number_format = "%.2f", #热图单元格上显示的数据格式，“%.2f” 表示两位小数,"%.1e"表示科学计数法
number_color = "grey30", #修改热图单元格上显示的数据字体颜色
show_rownames = F, show_colnames = T, #是否显示行名、列名
main = "Example heatmap", #表示热图的标题名字
color = colorRampPalette(c("#2E99B0","#FCD77F","#FF2E4C"))(50), #表示热图颜色,(50)表示50个等级
legend = T, #表示是否显示图例，值为TRUE或FALSE
legend_breaks = NA, #设置图例的范围legend_breaks=c(-2.5,0,2.5)表示图例断点的设置，默认为NA,
legend_labels = NA, #表示图例断点的标签
angle_col = "45", #表示列标签的角度
annotation_col = annotation_col, annotation_row = annotation_row, # 添加行和列的注释信息
annotation_colors = ann_colors # 修改注释的颜色
)
pheatmap-2
2. 使用
包绘制热图
2.1 基本用法
# 安装并加载所需的R包
# if (!requireNamespace("BiocManager", quietly=TRUE))
# install.packages("BiocManager")
# BiocManager::install("ComplexHeatmap")
# library(devtools)
# install_github("jokergoo/ComplexHeatmap")
# 生成示例数据
nr1 = 4; nr2 = 8; nr3 = 6; nr = nr1 + nr2 + nr3
nc1 = 6; nc2 = 8; nc3 = 10; nc = nc1 + nc2 + nc3
mat = cbind(rbind(matrix(rnorm(nr1*nc1, mean = 1, sd = 0.5), nr = nr1),
matrix(rnorm(nr2*nc1, mean = 0, sd = 0.5), nr = nr2),
matrix(rnorm(nr3*nc1, mean = 0, sd = 0.5), nr = nr3)),
rbind(matrix(rnorm(nr1*nc2, mean = 0, sd = 0.5), nr = nr1),
matrix(rnorm(nr2*nc2, mean = 1, sd = 0.5), nr = nr2),
matrix(rnorm(nr3*nc2, mean = 0, sd = 0.5), nr = nr3)),
rbind(matrix(rnorm(nr1*nc3, mean = 0.5, sd = 0.5), nr = nr1),
matrix(rnorm(nr2*nc3, mean = 0.5, sd = 0.5), nr = nr2),
matrix(rnorm(nr3*nc3, mean = 1, sd = 0.5), nr = nr3))
)
mat = mat[sample(nr, nr), sample(nc, nc)] # random shuffle rows and columns
rownames(mat) = paste0("row", seq_len(nr))
colnames(mat) = paste0("column", seq_len(nc))
# 简单绘图
Heatmap(mat)
ComplexHeatmap-1
2.2 单个热图
# 生成颜色映射
library(circlize)
col_fun = colorRamp2(c(-2, 0, 2), c("green", "white", "red"))
# 渲染树状图
library(dendextend)
row_dend = as.dendrogram(hclust(dist(mat)))
row_dend = color_branches(row_dend, k = 2) # `color_branches()` returns a dendrogram object
Heatmap(mat, name = "mat", col = col_fun, # 修改热图配色
border_gp = gpar(col = "black", lty = 2), # 控制热图网格的边框配色及形状
column_title = "I am a column title", row_title = "I am a row title", # 按行或/和按列设置标题
column_title_side = "bottom", row_title_side = "left", # 行/列标题的位置
column_title_gp = gpar(fontsize = 20, fontface = "bold"), # 行/列标题的大小
row_title_rot = 90, column_title_rot = 0, # 标题的旋转
column_dend_height = unit(2, "cm"), row_dend_width = unit(2, "cm"), # 树状图的高度
# row_dend_gp = gpar(col = "red")
cluster_rows = row_dend, # 对树状图进行更自定义的可视化
row_names_side = "left", column_names_side = "bottom", # 行/列名的位置
row_dend_side = "right",column_dend_side = "top", # 树状图的位置
row_names_gp = gpar(fontsize = 10, col = c(rep("red", 10), rep("blue", 8))), # 行/列字体的大小及配色
column_names_rot = 45, #行/列名的旋转
# column_km = 3, # 通过k均值进行行/列拆分
column_split = rep(c("A", "B", "C"), c(6, 8, 10)), # 按分类变量拆分
column_gap = unit(c(4, 2), "mm") # 切片之间的间隙
)
ComplexHeatmap-2
2.3 添加注释
# 使用HeatmapAnnotation函数添加待注释的内容
library(circlize)
col_fun = colorRamp2(c(0, 5, 10), c("green", "white", "red"))
column_ha <- HeatmapAnnotation(foo = runif(24),
foo1 = anno_lines(runif(24), smooth = TRUE), # 添加线条标注
bar1 = anno_barplot(runif(24), bar_width = 1, gp = gpar(fill = 1:24), # 添加条形图注释，设置宽度，调整颜色
seq(-5, 5), baseline = 0, add_numbers = F), # 设置基线，不显示数据
box1 = anno_boxplot(runif(24), height = unit(1, "cm"), gp = gpar(fill = 1:24), # 添加箱线图标注
box_width = 0.9, outline = FALSE), # 调整箱线图宽度，及是否显示离群值
col = list(foo = col_fun), # 对于连续值，调整颜色
simple_anno_size = unit(1, "cm"), # 调整注释的高度
gp = gpar(col = "black") # 调整注释的网格边框
)
row_ha <- HeatmapAnnotation(foo2 = runif(18), bar2 = anno_barplot(runif(18)), which = "row") # 或使用`rowAnnotation()`
Heatmap(mat, top_annotation = column_ha, right_annotation = row_ha,
row_km = 3 # 用km把行分为3列
)
ComplexHeatmap-3
3. 使用
包绘制热图
3.1 基本用法
# 安装并加载所需的R包
# devtools::install_github("DillonHammill/HeatmapR")
library(HeatmapR)
# 使用heat_map_save()导出图像
heat_map_save("HeatmapR-1.png", height = 9.5, width = 5.7, res = 500)
heat_map(mtcars,
cell_shape = "rect", # 修改单元格形状，可设置为“rect”（默认）、“circle”或“diamond”
cell_size = TRUE, # 根据输入矩阵中的值缩放每个单元格的大小
legend = TRUE, # 控制要显示的图例类型,包括"colour","shape"或"both"
legend_size = 1.5, # 调整图例可用的空间
legend_col_scale_size = 1.2, # 控制色标图例的大小
legend_title = c("colour", "size"), # 显示的标题和文本
title = "Legends"
)
heat_map_complete()
HeatmapR-1
3.2 其他参数设置
heat_map_save("HeatmapR-2.png", height = 9.5, width = 6.7, res = 500)
heat_map(
mtcars,
cell_border_line_type = 3, cell_border_line_width = 2, cell_border_line_col = "red", cell_border_line_col_alpha = 1, # 调节单元格的边框
cell_text = TRUE, cell_text_size = 0.6, cell_text_font = 2, # 显示单元格数据，并调整字体（1 by default）及大小（1 by default）
cell_text_col = "black", cell_text_col_alpha = 1, # 调整单元格数据的颜色（"white" by default），及透明度
title = "mtcars", # 设置标题
scale = "column", # 缩放行或列的数据，以看出较小的差距
scale_method = "range", # 选择缩放的方法："range" (by default), "mean" or "zscore"
bar_values_x = 1:ncol(mtcars), bar_size_x = 0.8, bar_fill_x = rainbow(ncol(mtcars)), bar_line_col_x = "black", # 在热图中添加条形图
bar_values_y = 1:nrow(mtcars), bar_size_y = 0.8, bar_fill_y = rainbow(ncol(mtcars)), bar_line_col_y = "black",
dist_method = "euclidean", clust_method = "complete",
tree_x = TRUE, tree_size_x = 0.4, # 将树状图添加到x轴
tree_cut_x = 4, tree_split_x = 1, # 选择性割任x轴上的树状图
tree_y = TRUE, tree_size_y = 0.8, # 将树状图添加到y轴
tree_cut_y = 5, tree_split_y = 1, # 选择性割任y轴上的树状图
)
heat_map_complete()
HeatmapR-2
4. 使用
包绘制热图
4.1 基本用法
# 安装并加载所需的R包
# install.packages("funkyheatmap")
library(funkyheatmap)
library(dplyr, warn.conflicts = FALSE)
library(tibble, warn.conflicts = FALSE)
funky_heatmap(mtcars)
funkyheatmap-1
4.2 其他参数设置
data("dynbenchmark_data") # 使用dynbenchmark_data的数据
data <- dynbenchmark_data
row_info <- data$data %>% select(group, id) # 选择待展示的行（此处即为id列各种方法）
row_groups <- data$data %>% transmute(group, Group = case_when(
group == "cycle" ~ "Cyclic methods", TRUE ~ paste0(stringr::str_to_title(group), " methods"))) %>%
unique()
# 定义需要展示的列以及对应的一些属性信息
column_info <- tribble( # tribble_start
~group, ~id, ~name, ~geom, ~palette, ~options,
"method_characteristic", "method_name", "", "text", NA, list(hjust = 0, width = 6),
"method_characteristic", "method_platform", "Platform", "text", NA, list(width = 2),
"method_characteristic", "method_topology_inference", "Topology inference", "text", NA, list(width = 2),
"score_overall", "summary_overall_overall", "Overall", "bar", "overall", list(width = 4, legend = FALSE),
"score_overall", "benchmark_overall_overall", "Accuracy", "bar", "benchmark", list(width = 4, legend = FALSE),
"score_overall", "qc_overall_overall", "Usability", "bar", "qc", list(width = 4, legend = FALSE),
"score_overall", "control_label", "", "text", NA, list(overlay = TRUE),
"benchmark_metric", "benchmark_overall_norm_him", "Topology", "funkyrect", "benchmark", lst(),
"benchmark_metric", "benchmark_overall_norm_F1_branches", "Branch assignment", "funkyrect", "benchmark", lst(),
"benchmark_metric", "benchmark_overall_norm_correlation", "Cell positions", "funkyrect", "benchmark", lst(),
"benchmark_metric", "benchmark_overall_norm_featureimp_wcor", "Features", "funkyrect", "benchmark", lst(),
"benchmark_source", "benchmark_source_real_gold", "Gold", "funkyrect", "benchmark", lst(),
"benchmark_source", "benchmark_source_real_silver", "Silver", "funkyrect", "benchmark", lst(),
"benchmark_source", "benchmark_source_synthetic_dyngen", "dyngen", "funkyrect", "benchmark", lst(),
"benchmark_source", "benchmark_source_synthetic_dyntoy", "dyntoy", "funkyrect", "benchmark", lst(),
"benchmark_source", "benchmark_source_synthetic_prosstt", "PROSSTT", "funkyrect", "benchmark", lst(),
"benchmark_source", "benchmark_source_synthetic_splatter", "Splatter", "funkyrect", "benchmark", lst(),
"benchmark_execution", "benchmark_overall_pct_errored_str", "% Errored", "text", NA, lst(hjust = 1),
"benchmark_execution", "benchmark_overall_error_reasons", "Reason", "pie", "error_reasons", lst(),
"scaling_predtime", "scaling_pred_scoretime_cells1m_features100", "1m \u00D7 100", "rect", "scaling", lst(scale = FALSE),
"scaling_predtime", "scaling_pred_scoretime_cells1m_features100", "", "text", "white6black4", lst(label = "scaling_pred_timestr_cells1m_features100", overlay = TRUE, size = 3, scale = FALSE),
"scaling_predtime", "scaling_pred_scoretime_cells100k_features1k", "100k \u00D7 1k", "rect", "scaling", lst(scale = FALSE),
"scaling_predtime", "scaling_pred_scoretime_cells100k_features1k", "", "text", "white6black4", lst(label = "scaling_pred_timestr_cells100k_features1k", overlay = TRUE, size = 3, scale = FALSE),
"scaling_predtime", "scaling_pred_scoretime_cells10k_features10k", "10k \u00D7 10k", "rect", "scaling", lst(scale = FALSE),
"scaling_predtime", "scaling_pred_scoretime_cells10k_features10k", "", "text", "white6black4", lst(label = "scaling_pred_timestr_cells10k_features10k", overlay = TRUE, size = 3, scale = FALSE),
"scaling_predtime", "scaling_pred_scoretime_cells1k_features100k", "1k \u00D7 100k", "rect", "scaling", lst(scale = FALSE),
"scaling_predtime", "scaling_pred_scoretime_cells1k_features100k", "", "text", "white6black4", lst(label = "scaling_pred_timestr_cells1k_features100k", overlay = TRUE, size = 3, scale = FALSE),
"scaling_predtime", "scaling_pred_scoretime_cells100_features1m", "100 \u00D7 1m", "rect", "scaling", lst(scale = FALSE),
"scaling_predtime", "scaling_pred_scoretime_cells100_features1m", "", "text", "white6black4", lst(label = "scaling_pred_timestr_cells100_features1m", overlay = TRUE, size = 3, scale = FALSE),
"scaling_predtime", "benchmark_overall_time_predcor_str", "Cor. pred. vs. real", "text", NA, lst(size = 3),
"stability", "stability_him", "Topology", "funkyrect", "stability", lst(),
"stability", "stability_F1_branches", "Branch assignment", "funkyrect", "stability", lst(),
"stability", "stability_correlation", "Cell positions", "funkyrect", "stability", lst(),
"stability", "stability_featureimp_wcor", "Features", "funkyrect", "stability", lst(),
"qc_category", "qc_cat_availability", "Availability", "funkyrect", "qc", lst(),
"qc_category", "qc_cat_behaviour", "Behaviour", "funkyrect", "qc", lst(),
"qc_category", "qc_cat_code_assurance", "Code assurance", "funkyrect", "qc", lst(),
"qc_category", "qc_cat_code_quality", "Code quality", "funkyrect", "qc", lst(),
"qc_category", "qc_cat_documentation", "Documentation", "funkyrect", "qc", lst(),
"qc_category", "qc_cat_paper", "Paper", "funkyrect", "qc", lst(),
"qc_category", "control_label", "", "text", NA, list(overlay = TRUE, width = -6)
)
# 将上述column_info的列，根据对应的group ，设置group的“Category”和大一级的Experiment信息
column_groups <- tribble(
~Experiment, ~Category, ~group, ~palette,
"Method", "\n", "method_characteristic", "overall",
"Summary", "Aggregated scores per experiment", "score_overall", "overall",
"Accuracy", "Per metric", "benchmark_metric", "benchmark",
"Accuracy", "Per dataset source", "benchmark_source", "benchmark",
"Accuracy", "Errors", "benchmark_execution", "benchmark",
"Scalability", "Predicted time\n(#cells \u00D7 #features)", "scaling_predtime", "scaling",
"Stability", "Similarity\nbetween runs", "stability", "stability",
"Usability", "Quality of\nsoftware and paper", "qc_category", "qc"
)
# 设置不同palette的颜色
error_reasons <- tibble(
name = c("pct_memory_limit", "pct_time_limit", "pct_execution_error", "pct_method_error"),
label = c("Memory limit exceeded", "Time limit exceeded", "Execution error", "Method error"),
colour = RColorBrewer::brewer.pal(length(name), "Set3")
)
palettes <- tribble(
~palette, ~colours,
"overall", grDevices::colorRampPalette(rev(RColorBrewer::brewer.pal(9, "Greys")[-1]))(101),
"benchmark", grDevices::colorRampPalette(rev(RColorBrewer::brewer.pal(9, "Blues") %>% c("#011636")))(101),
"scaling", grDevices::colorRampPalette(rev(RColorBrewer::brewer.pal(9, "Reds")[-8:-9]))(101),
"stability", grDevices::colorRampPalette(rev(RColorBrewer::brewer.pal(9, "YlOrBr")[-7:-9]))(101),
"qc", grDevices::colorRampPalette(rev(RColorBrewer::brewer.pal(9, "Greens")[-1] %>% c("#00250f")))(101),
"error_reasons", error_reasons %>% select(label, colour) %>% deframe(),
"white6black4", c(rep("white", 3), rep("black", 7))
)
g <- funky_heatmap(
data = data$data,
column_info = column_info,
column_groups = column_groups,
row_info = row_info,
row_groups = row_groups,
palettes = palettes,
col_annot_offset = 3.2
)
ggsave("/Users/shumin/Desktop/funkyheatmap-2.png", g, device = png, width = g$width, height = g$height)
funkyheatmap-2
参考：
- https://github.com/jokergoo/ComplexHeatmap
- https://github.com/DillonHammill/HeatmapR
- funkyheatmap |临床+组学+分组数据可视化“神器”，时髦的热图（http://www.360doc.com/content/23/0110/08/65403234_1063150291.shtml）
网友评论