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临床数据一般是使用图表汇总Table1的方式进行展示，例如R|tableone 快速绘制文章“表一”-基线特征三线表 或者 gtsummary|巧合-绘制多种数据汇总表“神器” 。
今天介绍一个可视化展示方式，funkyheatmap-R包 ， 可以为基准数据生成热图式可视化的函数，可以使用列和行的注释对其进行微调 。效果如下
一 载入R包，数据
首先安装funkyheatmap 包，
1）先使用mtcars 数据绘制
#devtools::install_github("dynverse/dynbenchmark/package") install.packages("funkyheatmap") library(funkyheatmap) library(dplyr, warn.conflicts = FALSE) library(tibble, warn.conflicts = FALSE) library(tidyverse) data("mtcars") funky_heatmap(mtcars)
使用mtcars类似的数据进行绘制即可，默认情况下绘制每一列的信息，后面就是参数修改以期达到封面图的效果。
下面使用dynbenchmark_data的数据进行详细的调试 绘制。
2）载入dynbenchmark_data数据（2019 NBT ：A comparison of single-cell trajectory inference methods ）
data("dynbenchmark_data") data <- dynbenchmark_data head(data)
二 绘制funky heatmap
想达到封面图的效果，需要一系列的设置。
1 , 设置row_info 和 row_group
row_info选择待展示的行（此处即为id列各种方法），此处为全部展示，可是使用filter筛选想展示的行；
row_groups是行group（此处为各种方法的类型），对应下图的红框部分
row_info <- data %>% select(group, id) row_groups <- data %>% transmute( group, Group = case_when( group == "cycle" ~ "Cyclic methods", TRUE ~ paste0(stringr::str_to_title(group), " methods") ) ) %>% unique() head(row_info) # A tibble: 6 × 2 # group id # <fct> <chr> #1 graph paga #2 graph raceid_stemid #3 graph slicer #4 tree slingshot #5 tree paga_tree #6 tree projected_slingshot head(row_groups) # A tibble: 6 × 2 # group Group # <fct> <chr> #1 graph Graph methods #2 tree Tree methods #3 multifurcation Multifurcation methods #4 bifurcation Bifurcation methods #5 linear Linear methods #6 cycle Cyclic methods
2 ，设置column_info
设置列的信息，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) )
主要有以下几列：
group：列的分组；
id：data中的列名字；
name：图中展示的名字；
geom：集合图形，展示方式 （如果有多个属性（rect 和 text）需要展示则分为多行，如scaling_predtime）；
palette：调色板信息；
3，设置column_groups
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" )
Experiment：group的Experiment信息（下图红框）
Category：group的Category信息（下图绿框）
group：列的分组（同column_info中的group）
palette：group使用何种palette
4，设置 palettes
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)) )
5，绘制funky heatmap
完成上述设置后终于可以绘制funky heatmap了
g <- funky_heatmap( 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 ) g #保存输出 #ggsave("path_to_plot.pdf", g, device = cairo_pdf, width = g$width, height = g$height)
OK，这样每个患者（id）的临床信息，组学信息，分组信息，就都可以可视化展示了！
