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ADE Heatmap illustration

ADE Heatmap illustration

February 17, 2025

Introduciton

This R Markdown document describes how to merge multiple values from different assays to create heatmaps to visualize the features of each antibody-variant pair.

The following steps present how to read data from excel files, merge them together and use them to make a pretty heatmap.

Prepare R packages

I need readxl and writexl to input/output data, dplyr and scales to process data, and pheatmap, ggplot2, tidyverse to create graphs:

> library(readxl)
> library(writexl)
> library(scales)
> library(pheatmap)
> library(ggplot2)
> library(tidyverse)

Process data

set input file or folder path 

> input_file_path = "./serum_dataset/vaccine serum samples-wt delta omi.xlsx"
> input_folder_path = "./serum_dataset"
> # replace it with your file or folder path

Usually, I’d like to export the graphs to the same folder as the input files :

> outputpath = paste0(dirname(input_file_path),"\\heatmap.pdf")

This outputpath will be named as “heatmap.pdf” in the same folder as the input file:

[1] "./serum_dataset\\heatmap.pdf"

If you chose folder path as input, the outputpath should be as same as the input_folder_path:

> outputpath = input_folder_path
> outputpath

read data

Importantly, make sure the data in your excel files looks like this:

sample WT-1 WT-2 WT-3 WT-4 WT-5 WT-6 WT-7 WT-8 WT-9 Delta-1 Delta-2 Delta-3 Delta-4 Delta-5 Delta-6 Delta-7 Delta-8 Delta-9 Omicron-1 Omicron-2 Omicron-3 Omicron-4 Omicron-5 Omicron-6 Omicron-7 Omicron-8 Omicron-9
249 3490 3566 3701 3823 3796 3779 4106 6278 11691 2318 2265 2270 2361 2399 2625 4045 19085 42810 2688 2691 2608 2743 2742 2697 2756 3168 10036
NA 3236 3265 3310 3453 3100 2996 3179 10101 17397 2160 2154 2158 2899 2047 2424 6343 10983 41618 2550 2473 2521 2474 2317 2331 2356 3451 7322
230 2384 2528 2675 2620 2673 2764 4490 10318 18427 1879 2515 1857 1938 2726 4257 12521 23052 57013 2255 2264 2126 2276 2062 2187 2262 2363 5507
NA 2394 2296 2315 2243 2376 3073 5018 14496 18438 1852 1869 1849 1792 1907 4469 16023 23121 29738 2121 2088 2464 2183 2036 1983 1931 2320 4665
244 1958 1936 2026 2005 1976 2474 2137 4656 11394 1698 2007 1678 1796 1647 1795 3571 9030 30330 2038 1889 2044 1917 1911 2243 2243 2969 6628
NA 1832 1921 1920 1790 1854 1996 2062 3999 10572 1703 1662 1543 1523 1683 3228 3715 7058 20046 1858 1881 1799 1760 1749 1736 1984 2797 8719

The Sample column will be used as the key to merge data together. If your data format matches the example above, we can read it by these following commands:

  • If your data is stored in different excel files which contains only one sheet:
> file_names <- list.files(input_folder_path, pattern = "\\.xlsx$|\\.xls$", full.names = TRUE)
> merged.data = list()
> for (i in 1:length(file_names)) {
+     # read data in excel files
+     data <- read_excel(file_names[i])
+     colnames(data)[1] = "sample"
+     # merge data together 
+     merged.data[[i]] = data
+ }
> merged_df = merged.data[[1]]

Let’s take a look at the output merged_df:

It’s a dataframe:

> class(merged_df)
[1] "tbl_df"     "tbl"        "data.frame"

data processing

This merged_df will be used as the input to undergo further processing, including merge batches, omit NA value and normalization

merge batches and omit NA value 

 [1] "sample"    "WT-1"      "WT-2"      "WT-3"      "WT-4"      "WT-5"     
 [7] "WT-6"      "WT-7"      "WT-8"      "WT-9"      "Delta-1"   "Delta-2"  
[13] "Delta-3"   "Delta-4"   "Delta-5"   "Delta-6"   "Delta-7"   "Delta-8"  
[19] "Delta-9"   "Omicron-1" "Omicron-2" "Omicron-3" "Omicron-4" "Omicron-5"
[25] "Omicron-6" "Omicron-7" "Omicron-8" "Omicron-9"

normalization using Z-score

Z-score is usually used to perform standardization. Here is a detailed explanation about its definition. Briefly, we here use (x-μ)/σ to calculate the z-score of Binding, Neutralization, ADE and Competition ELISA assays respectively.

> output.data = merged_df %>% na.omit()
> rownames(output.data) = output.data$sample
> output.data$sample = NULL

Take a look at the outout.data:

WT-1 WT-2 WT-3 WT-4 WT-5 WT-6 WT-7 WT-8 WT-9
3490 3566 3701 3823 3796 3779 4106 6278 11691
2384 2528 2675 2620 2673 2764 4490 10318 18427

Create Heatmap

Now you have already gotten your data for make heatmap, but there are still some more things to do.

annotate columns and rows

Note that the column annotations must match the order in your output.data.
So you may want to double-check how your output.data arranges:

> colnames(output.data)
 [1] "WT-1"      "WT-2"      "WT-3"      "WT-4"      "WT-5"      "WT-6"     
 [7] "WT-7"      "WT-8"      "WT-9"      "Delta-1"   "Delta-2"   "Delta-3"  
[13] "Delta-4"   "Delta-5"   "Delta-6"   "Delta-7"   "Delta-8"   "Delta-9"  
[19] "Omicron-1" "Omicron-2" "Omicron-3" "Omicron-4" "Omicron-5" "Omicron-6"
[25] "Omicron-7" "Omicron-8" "Omicron-9"

Now you are ready to set the annotation following the commands below:

>   annotation_col= data.frame(Datatype = factor(rep(c("WT","Delta","omi"),c(9,9,9))))
>   rownames(annotation_col) = colnames(output.data)
>   ann_colors = list(Datatype = c(WT="#3567E5",Delta="#B0E65E",omi="#91726B"))

set legend breaks

The break argument is quite important for the output format of your heatmap, as it determines the representative color for your data at certain scale. Here we use a bk factor to set it.

>  bk = seq(-2,5,by = 1)

draw your heatmap

Finally, we reach here.

>  #heatmap
>   p1 <- pheatmap(output.data,  # your prepared data
+                  color = c(colorRampPalette(c('#C6DAF1','white','red'),bias = 1.7)(6)), 
+                  # set colors, here I make 6 colors between light blue and red
+                  border_color = "black",  #set border color，NA means no border
+                  scale= "none", 
+                  # "column" indicates z-score standardization for each column,
+                  # "row" indicates z-score standardization for each row,
+                  # "none" indicates no standardization, 
+                  # we chose "none" here because we have done it in previous steps.
+                  cluster_rows = TRUE, # whether cluster by rows
+                  cluster_cols = FALSE, # whether cluster by columns
+                  legend = TRUE, # display legends or not
+                  breaks = bk, # set breaks here 
+                  legend_breaks = c(-2:5), # set legend breaks here 
+                  legend_labels = c(-2:5), # set labels at legend breaks
+                  show_rownames = FALSE, 
+                  show_colnames = FALSE, 
+                  display_numbers = FALSE,
+                  fontsize = 10, # set the font size
+                  annotation_col =annotation_col,
+                  annotation_colors = ann_colors,
+                  angle_col = 0,
+                  cutree_rows=4, # break heatmap by clustering result
+   )

We did it! Now enjoy your heatmap!

save your heatmap 

>  ggsave(file = outputpath, plot = p1,width = 11,height = 10,dpi = 300)
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AI disclosure: This article was written by the author with assistance from Claude (Anthropic) for drafting, bilingual translation, and formatting. The author has reviewed all content and takes full responsibility for its accuracy.
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