Making a heatmap in R with the pheatmap package
2020-11-10
Last updated: 2020-11-10
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Knit directory: muse/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| 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 <- system.file ("extra/TagSeqExample.tab", package="DESeq")
data <- read.delim(example_file, header=T, 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")
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$height [1] 2502.208 3771.244 4252.402 4366.211 4700.444 5069.851 5208.367
[8] 6439.545 6474.863 6938.482 7983.369 8141.632 9198.185 9849.175
[15] 10818.256 10868.066 11127.621 11168.654 12699.557 12871.187 13511.763
[22] 13549.622 14483.876 14856.478 14860.904 15033.046 16304.877 16574.315
[29] 16935.384 17713.534 18798.131 18904.899 20250.185 22302.634 22512.593
[36] 24345.199 29826.722 30846.374 31530.137 31849.145 40048.202 43714.148
[43] 47029.264 48908.962 56038.953 67891.667 74124.247 95015.400as.dendrogram(my_hclust_gene) %>%
plot(horiz = TRUE)
| Version | Author | Date |
|---|---|---|
| 586b91f | Dave Tang | 2020-07-12 |
Reproduce the sample dendrogram.
my_hclust_sample <- hclust(dist(t(data_subset)), method = "complete")
as.dendrogram(my_hclust_sample) %>%
plot()
| Version | Author | Date |
|---|---|---|
| 586b91f | Dave Tang | 2020-07-12 |
Add annotations.
my_gene_col <- cutree(tree = as.dendrogram(my_hclust_gene), k = 2)
my_gene_col <- data.frame(cluster = ifelse(test = my_gene_col == 1, yes = "cluster 1", no = "cluster 2"))
my_sample_col <- data.frame(sample = rep(c("tumour", "normal"), c(4,2)))
row.names(my_sample_col) <- colnames(data_subset)
set.seed(1984)
my_random <- as.factor(sample(x = 1:2, size = nrow(my_gene_col), replace = TRUE))
my_gene_col$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 |
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 4.0.2 (2020-06-22)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.7
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
locale:
[1] en_AU.UTF-8/en_AU.UTF-8/en_AU.UTF-8/C/en_AU.UTF-8/en_AU.UTF-8
attached base packages:
[1] parallel stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] gridExtra_2.3 dendextend_1.14.0 DESeq_1.39.0
[4] lattice_0.20-41 locfit_1.5-9.4 Biobase_2.48.0
[7] BiocGenerics_0.34.0 pheatmap_1.0.12 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rcpp_1.0.5 rprojroot_1.3-2 digest_0.6.25
[4] R6_2.4.1 backports_1.1.9 stats4_4.0.2
[7] RSQLite_2.2.0 evaluate_0.14 ggplot2_3.3.2
[10] pillar_1.4.6 rlang_0.4.7 rstudioapi_0.11
[13] annotate_1.66.0 whisker_0.4 blob_1.2.1
[16] S4Vectors_0.26.1 Matrix_1.2-18 rmarkdown_2.3
[19] splines_4.0.2 geneplotter_1.66.0 stringr_1.4.0
[22] RCurl_1.98-1.2 bit_4.0.4 munsell_0.5.0
[25] compiler_4.0.2 httpuv_1.5.4 xfun_0.16
[28] pkgconfig_2.0.3 htmltools_0.5.0 tidyselect_1.1.0
[31] tibble_3.0.3 IRanges_2.22.2 XML_3.99-0.5
[34] viridisLite_0.3.0 crayon_1.3.4 dplyr_1.0.2
[37] later_1.1.0.1 bitops_1.0-6 grid_4.0.2
[40] xtable_1.8-4 gtable_0.3.0 lifecycle_0.2.0
[43] DBI_1.1.0 git2r_0.27.1 magrittr_1.5
[46] scales_1.1.1 stringi_1.4.6 farver_2.0.3
[49] genefilter_1.70.0 viridis_0.5.1 fs_1.5.0
[52] promises_1.1.1 ellipsis_0.3.1 vctrs_0.3.4
[55] generics_0.0.2 RColorBrewer_1.1-2 tools_4.0.2
[58] bit64_4.0.5 glue_1.4.2 purrr_0.3.4
[61] survival_3.2-3 yaml_2.2.1 AnnotationDbi_1.50.3
[64] colorspace_1.4-1 memoise_1.1.0 knitr_1.29