Load Packages
library(devtools)
## Loading required package: usethis
library(ComplexHeatmap)
## Loading required package: grid
## ========================================
## ComplexHeatmap version 2.12.1
## 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 heatmaps reveal patterns and correlations in multidimensional
## genomic data. Bioinformatics 2016.
## - Gu, Z. Complex Heatmap Visualization. iMeta 2022.
##
##
## 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(circlize)
## ========================================
## circlize version 0.4.15
## CRAN page: https://cran.r-project.org/package=circlize
## Github page: https://github.com/jokergoo/circlize
## Documentation: https://jokergoo.github.io/circlize_book/book/
##
## If you use it in published research, please cite:
## Gu, Z. circlize implements and enhances circular visualization
## in R. Bioinformatics 2014.
##
## This message can be suppressed by:
## suppressPackageStartupMessages(library(circlize))
## ========================================
library(hopach)
## Loading required package: cluster
## Loading required package: Biobase
## Loading required package: BiocGenerics
##
## Attaching package: 'BiocGenerics'
## The following objects are masked from 'package:stats':
##
## IQR, mad, sd, var, xtabs
## The following objects are masked from 'package:base':
##
## anyDuplicated, append, as.data.frame, basename, cbind, colnames,
## dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,
## grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,
## order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
## rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,
## union, unique, unsplit, which.max, which.min
## Welcome to Bioconductor
##
## Vignettes contain introductory material; view with
## 'browseVignettes()'. To cite Bioconductor, see
## 'citation("Biobase")', and for packages 'citation("pkgname")'.
Visualize the mtcars dataset using hierarchical cluster
analysis heatmaps
head(mtcars)
## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
data<-mtcars[,c(1:7)]
data.scale<-scale(data)
head(data.scale)
## mpg cyl disp hp drat
## Mazda RX4 0.1508848 -0.1049878 -0.57061982 -0.5350928 0.5675137
## Mazda RX4 Wag 0.1508848 -0.1049878 -0.57061982 -0.5350928 0.5675137
## Datsun 710 0.4495434 -1.2248578 -0.99018209 -0.7830405 0.4739996
## Hornet 4 Drive 0.2172534 -0.1049878 0.22009369 -0.5350928 -0.9661175
## Hornet Sportabout -0.2307345 1.0148821 1.04308123 0.4129422 -0.8351978
## Valiant -0.3302874 -0.1049878 -0.04616698 -0.6080186 -1.5646078
## wt qsec
## Mazda RX4 -0.610399567 -0.7771651
## Mazda RX4 Wag -0.349785269 -0.4637808
## Datsun 710 -0.917004624 0.4260068
## Hornet 4 Drive -0.002299538 0.8904872
## Hornet Sportabout 0.227654255 -0.4637808
## Valiant 0.248094592 1.3269868
Heatmap(data.scale, name="mtcar heatmap",
cluster_rows=FALSE, cluster_columns=FALSE)
Heatmap(data.scale, name = "mtcars heatmap",
cluster_columns = FALSE)
Heatmap(data.scale,name="mtcars heatmap")
uncenter.dist<-function(m){
as.dist(as.matrix(distancematrix(m,d="cosangle")))
}
row.clus<-hclust(uncenter.dist(data.scale),method="ave")
col.clus<-hclust(uncenter.dist(t(data.scale)),method="ave")
Heatmap(data.scale, name = "mtcars heatmap",
cluster_rows=row.clus, cluster_columns=col.clus)
ht_inline<-Heatmap(mtcars[, c(8)],
name = "Cylinder Config",
col=colorRamp2(c(0,1),c("white", "black")),
heatmap_legend_param = list(at = c(0,1),
labels = c("V-shaped","Inline")),
width = unit(0.5,"cm"))
ht_manual<-Heatmap(mtcars[,c(9)], name = "Transmission",
col = colorRamp2(c(0,1),c("white", "black")),
heatmap_legend_param = list(at = c(0,1),
labels = c("Automatic", "Manual")),
width = unit(0.5,"cm"))
ht_main = Heatmap(data.scale, name = "cluster rows",
cluster_rows=row.clus, cluster_columns = col.clus)
draw(ht_main+ht_manual+ht_inline, auto_adjust = FALSE)
draw(ht_main+ht_manual+ht_inline)
Use Complex Heatmap to visualize the mtcars
dataset.
head(mtcars)
## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
data<-mtcars[,c(1:7)]
data.scale<-scale(data)
head(data.scale)
## mpg cyl disp hp drat
## Mazda RX4 0.1508848 -0.1049878 -0.57061982 -0.5350928 0.5675137
## Mazda RX4 Wag 0.1508848 -0.1049878 -0.57061982 -0.5350928 0.5675137
## Datsun 710 0.4495434 -1.2248578 -0.99018209 -0.7830405 0.4739996
## Hornet 4 Drive 0.2172534 -0.1049878 0.22009369 -0.5350928 -0.9661175
## Hornet Sportabout -0.2307345 1.0148821 1.04308123 0.4129422 -0.8351978
## Valiant -0.3302874 -0.1049878 -0.04616698 -0.6080186 -1.5646078
## wt qsec
## Mazda RX4 -0.610399567 -0.7771651
## Mazda RX4 Wag -0.349785269 -0.4637808
## Datsun 710 -0.917004624 0.4260068
## Hornet 4 Drive -0.002299538 0.8904872
## Hornet Sportabout 0.227654255 -0.4637808
## Valiant 0.248094592 1.3269868
col_fun = colorRamp2(c(-4, 4), c("yellow", "blue"))
col_fun(seq(-3, 3))
## [1] "#F5E153FF" "#EAC476FF" "#DBA792FF" "#CA8AAAFF" "#B46DC0FF" "#9850D5FF"
## [7] "#7030EAFF"
Heatmap(data.scale, name = "Car Heatmap",col = col_fun)
Heatmap(data.scale, name = "Car Heatmap", rect_gp = gpar(col = "white", lwd = 2))
col_fun = colorRamp2(c(-4, 4), c("yellow", "blue"))
col_fun(seq(-3, 3))
## [1] "#F5E153FF" "#EAC476FF" "#DBA792FF" "#CA8AAAFF" "#B46DC0FF" "#9850D5FF"
## [7] "#7030EAFF"
Heatmap(data.scale, name = "Car Heatmap",
row_km = 3, row_gap = unit(c(2, 4), "mm"),
column_km = 3, column_gap = unit(c(2, 4), "mm"))
col_fun = colorRamp2(c(-4, 4), c("yellow", "blue"))
col_fun(seq(-3, 3))
## [1] "#F5E153FF" "#EAC476FF" "#DBA792FF" "#CA8AAAFF" "#B46DC0FF" "#9850D5FF"
## [7] "#7030EAFF"
Heatmap(data.scale, name = "Car Heatmap",
row_km = 2, column_km = 3, border = TRUE)
