Making a heatmap in R with the pheatmap package

Last updated: 2020-11-27

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File	Version	Author	Date	Message
Rmd	4da96c0	Dave Tang	2020-11-27	More clusters
html	2db13ea	davetang	2020-11-10	Build site.
Rmd	268312f	davetang	2020-11-10	wflow_publish(files = c(“analysis/index.Rmd”, “analysis/google_trends.Rmd”,
html	586b91f	Dave Tang	2020-07-12	Build site.
Rmd	b1d6edd	Dave Tang	2020-07-12	pheatmap

Making a heatmap using the pheatmap package.

example_file <- "https://davetang.org/file/TagSeqExample.tab"
data <- read.delim(example_file, header = TRUE, row.names = "gene")
data_subset <- as.matrix(data[rowSums(data)>50000,])

Default heatmap using pheatmap.

pheatmap(data_subset)

Version	Author	Date
586b91f	Dave Tang	2020-07-12

cal_z_score <- function(x){
  (x - mean(x)) / sd(x)
}

data_subset_norm <- t(apply(data_subset, 1, cal_z_score))
pheatmap(data_subset_norm)

Version	Author	Date
586b91f	Dave Tang	2020-07-12

Using scale produces the same heatmap as using cal_z_score.

pheatmap(data_subset, scale = "row")

Version	Author	Date
2db13ea	davetang	2020-11-10

Two heatmaps.

one <- pheatmap(data_subset, silent = TRUE)
two <- pheatmap(data_subset, silent = TRUE)

grid.arrange(grobs = list(one[[4]], two[[4]]))

Version	Author	Date
586b91f	Dave Tang	2020-07-12

Reproduce the gene dendrogram.

par(mar = c(3.1, 2.1, 1.1, 5.1))

my_hclust_gene <- hclust(dist(data_subset), method = "complete")
my_hclust_gene$height

 [1]  2502.208  3771.244  4252.402  4366.211  4700.444  5069.851  5208.367
 [8]  6439.545  6474.863  6938.482  7983.369  8141.632  9198.185  9849.175
[15] 10818.256 10868.066 11127.621 11168.654 12699.557 12871.187 13511.763
[22] 13549.622 14483.876 14856.478 14860.904 15033.046 16304.877 16574.315
[29] 16935.384 17713.534 18798.131 18904.899 20250.185 22302.634 22512.593
[36] 24345.199 29826.722 30846.374 31530.137 31849.145 40048.202 43714.148
[43] 47029.264 48908.962 56038.953 67891.667 74124.247 95015.400

as.dendrogram(my_hclust_gene) %>%
  plot(horiz = TRUE)

Version	Author	Date
586b91f	Dave Tang	2020-07-12

Reproduce the sample dendrogram.

my_hclust_sample <- hclust(dist(t(data_subset)), method = "complete")

as.dendrogram(my_hclust_sample) %>%
  plot()

Version	Author	Date
586b91f	Dave Tang	2020-07-12

Add annotations.

my_gene_col <- cutree(tree = as.dendrogram(my_hclust_gene), k = 2)
my_gene_col <- data.frame(cluster = ifelse(test = my_gene_col == 1, yes = "cluster 1", no = "cluster 2"))

my_sample_col <- data.frame(sample = rep(c("tumour", "normal"), c(4,2)))
row.names(my_sample_col) <- colnames(data_subset)

set.seed(1984)
my_random <- as.factor(sample(x = 1:2, size = nrow(my_gene_col), replace = TRUE))
my_gene_col$random <- my_random

pheatmap(data_subset, annotation_row = my_gene_col, annotation_col = my_sample_col)

Version	Author	Date
586b91f	Dave Tang	2020-07-12

More clusters.

my_gene_col <- cutree(tree = as.dendrogram(my_hclust_gene), k = 6)
my_gene_col <- data.frame(cluster = paste0("cluster ", my_gene_col), row.names = names(my_gene_col))

my_sample_col <- data.frame(sample = rep(c("tumour", "normal"), c(4,2)))
row.names(my_sample_col) <- colnames(data_subset)

set.seed(1984)
my_random <- as.factor(sample(x = 1:2, size = nrow(my_gene_col), replace = TRUE))
my_gene_col$random <- my_random

pheatmap(data_subset, annotation_row = my_gene_col, annotation_col = my_sample_col)

Change annotation colours and ordering.

my_gene_col <- cutree(tree = as.dendrogram(my_hclust_gene), k = 2)
my_gene_col <- data.frame(cluster = ifelse(test = my_gene_col == 1, yes = "cluster1", no = "cluster2"))

my_sample_col <- data.frame(sample = rep(c("tumour", "normal"), c(4,2)))
row.names(my_sample_col) <- colnames(data_subset)

# change order
my_sample_col$sample <- factor(my_sample_col$sample, levels = c("normal", "tumour"))

set.seed(1984)
my_random <- as.factor(sample(x = c("random1", "random2"), size = nrow(my_gene_col), replace = TRUE))
my_gene_col$random <- my_random

my_colour = list(
    sample = c(normal = "#5977ff", tumour = "#f74747"),
    random = c(random1 = "#82ed82", random2 = "#9e82ed"),
    cluster = c(cluster1 = "#e89829", cluster2 = "#cc4ee0")
)

p <- pheatmap(data_subset,
              annotation_colors = my_colour,
              annotation_row = my_gene_col,
              annotation_col = my_sample_col,
              cellheight = 7,
              cellwidth = 18)

Version	Author	Date
586b91f	Dave Tang	2020-07-12

save_pheatmap_png <- function(x, filename, width=1200, height=1000, res = 150) {
  png(filename, width = width, height = height, res = res)
  grid::grid.newpage()
  grid::grid.draw(x$gtable)
  dev.off()
}

# not run
# save_pheatmap_png(p, "heatmap_colour.png")

Introduce breaks by cutting the dendrogram.

pheatmap(data_subset,
         annotation_row = my_gene_col,
         annotation_col = my_sample_col,
         cutree_rows = 2,
         cutree_cols = 2)

Version	Author	Date
586b91f	Dave Tang	2020-07-12

Dendrogram results from pheatmap().

par(mar = c(3.1, 2.1, 1.1, 5.1))

my_heatmap <- pheatmap(data_subset, silent = TRUE)
names(my_heatmap)

[1] "tree_row" "tree_col" "kmeans"   "gtable"

my_heatmap$tree_row %>%
  as.dendrogram() %>%
  plot(horiz = TRUE)

Version	Author	Date
586b91f	Dave Tang	2020-07-12

sessionInfo()

R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.1 LTS

Matrix products: default
BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.8.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=C             
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] gridExtra_2.3     dendextend_1.14.0 pheatmap_1.0.12   workflowr_1.6.2  

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.5         pillar_1.4.7       compiler_4.0.3     later_1.1.0.1     
 [5] RColorBrewer_1.1-2 git2r_0.27.1       viridis_0.5.1      tools_4.0.3       
 [9] digest_0.6.27      viridisLite_0.3.0  evaluate_0.14      lifecycle_0.2.0   
[13] tibble_3.0.4       gtable_0.3.0       pkgconfig_2.0.3    rlang_0.4.8       
[17] rstudioapi_0.13    yaml_2.2.1         xfun_0.19          dplyr_1.0.2       
[21] stringr_1.4.0      knitr_1.30         generics_0.1.0     fs_1.5.0          
[25] vctrs_0.3.5        tidyselect_1.1.0   rprojroot_2.0.2    grid_4.0.3        
[29] glue_1.4.2         R6_2.5.0           rmarkdown_2.5      farver_2.0.3      
[33] purrr_0.3.4        ggplot2_3.3.2      magrittr_2.0.1     whisker_0.4       
[37] scales_1.1.1       promises_1.1.1     ellipsis_0.3.1     htmltools_0.5.0   
[41] colorspace_2.0-0   httpuv_1.5.4       stringi_1.5.3      munsell_0.5.0     
[45] crayon_1.3.4