Performance

library(ComplexHeatmap)
#> Loading required package: grid
#> ========================================
#> ComplexHeatmap version 2.20.0
#> Bioconductor page: http://bioconductor.org/packages/ComplexHeatmap/
#> Github page: https://github.com/jokergoo/ComplexHeatmap
#> Documentation: http://jokergoo.github.io/ComplexHeatmap-reference
#> 
#> If you use it in published research, please cite either one:
#> - Gu, Z. Complex Heatmap Visualization. iMeta 2022.
#> - Gu, Z. Complex heatmaps reveal patterns and correlations in multidimensional 
#>     genomic data. Bioinformatics 2016.
#> 
#> 
#> The new InteractiveComplexHeatmap package can directly export static 
#> complex heatmaps into an interactive Shiny app with zero effort. Have a try!
#> 
#> This message can be suppressed by:
#>   suppressPackageStartupMessages(library(ComplexHeatmap))
#> ========================================
library(pheatmap)
#> 
#> Attaching package: 'pheatmap'
#> The following object is masked from 'package:ComplexHeatmap':
#> 
#>     pheatmap
library(gplots)
#> 
#> Attaching package: 'gplots'
#> The following object is masked from 'package:stats':
#> 
#>     lowess
library(ggalign)
#> Loading required package: ggplot2

set.seed(123)
n <- 1000
mat <- matrix(rnorm(n * n), nrow = n)

A simple heatmap.

bench::mark(
    "heatmap()" = {
        pdf(NULL)
        heatmap(mat, Rowv = NA, Colv = NA)
        dev.off()
        NULL
    },
    "heatmap.2()" = {
        pdf(NULL)
        heatmap.2(mat, dendrogram = "none", trace = "none")
        dev.off()
        NULL
    },
    "Heatmap()" = {
        pdf(NULL)
        draw(Heatmap(mat, cluster_rows = FALSE, cluster_columns = FALSE))
        dev.off()
        NULL
    },
    "pheatmap()" = {
        pdf(NULL)
        pheatmap(mat, cluster_rows = FALSE, cluster_cols = FALSE)
        dev.off()
        NULL
    },
    "ggalign()" = {
        pdf(NULL)
        print(ggheatmap(mat))
        dev.off()
        NULL
    }
)
#> → heatmap built with `geom_raster()`
#> → heatmap built with `geom_raster()`
#> Warning: Some expressions had a GC in every iteration; so filtering is
#> disabled.
#> # A tibble: 5 × 6
#>   expression       min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr>  <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 heatmap()   154.03ms  159.3ms     6.16    139.1MB     7.71
#> 2 heatmap.2()    2.43s    2.43s     0.412   224.2MB     1.65
#> 3 Heatmap()      3.38s    3.38s     0.295     785MB     3.25
#> 4 pheatmap()  690.62ms 690.62ms     1.45    124.1MB     2.90
#> 5 ggalign()      2.57s    2.57s     0.390     2.5GB     9.35

For heatmap with dendrogram

bench::mark(
    "heatmap()" = {
        pdf(NULL)
        heatmap(mat)
        dev.off()
        NULL
    },
    "heatmap.2()" = {
        pdf(NULL)
        heatmap.2(mat, trace = "none")
        dev.off()
        NULL
    },
    "Heatmap()" = {
        pdf(NULL)
        draw(Heatmap(mat, row_dend_reorder = FALSE, column_dend_reorder = FALSE))
        dev.off()
        NULL
    },
    "pheatmap()" = {
        pdf(NULL)
        pheatmap(mat)
        dev.off()
        NULL
    },
    "ggalign()" = {
        pdf(NULL)
        print(ggheatmap(mat) +
            hmanno("t") + align_dendro() +
            hmanno("r") + align_dendro())
        dev.off()
        NULL
    }
)
#> → heatmap built with `geom_raster()`
#> → heatmap built with `geom_raster()`
#> Warning: Some expressions had a GC in every iteration; so filtering is
#> disabled.
#> # A tibble: 5 × 6
#>   expression       min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr>  <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 heatmap()      2.72s    2.72s     0.367  173.72MB    1.47 
#> 2 heatmap.2()    3.15s    3.15s     0.318  223.41MB    0.954
#> 3 Heatmap()      4.49s    4.49s     0.223    1.51GB    2.23 
#> 4 pheatmap()     2.31s    2.31s     0.432  177.53MB    0.432
#> 5 ggalign()      5.19s    5.19s     0.193    2.57GB    4.62