library(cluster)
library(factoextra)
## Loading required package: ggplot2
## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa
data <- scale(USArrests)
dist.res <- dist(data)
hc <- hclust(dist.res, method = "ward.D2")
dend <- as.dendrogram(hc)
plot(dend)
library(dendextend)
##
## ---------------------
## Welcome to dendextend version 1.7.0
## Type citation('dendextend') for how to cite the package.
##
## Type browseVignettes(package = 'dendextend') for the package vignette.
## The github page is: https://github.com/talgalili/dendextend/
##
## Suggestions and bug-reports can be submitted at: https://github.com/talgalili/dendextend/issues
## Or contact: <tal.galili@gmail.com>
##
## To suppress this message use: suppressPackageStartupMessages(library(dendextend))
## ---------------------
##
## Attaching package: 'dendextend'
## The following object is masked from 'package:stats':
##
## cutree
dend <- USArrests[1:5,] %>% # data
scale %>% # Scale the data
dist %>% # calculate a distance matrix,
hclust(method = "ward.D2") %>% # Hierarchical clustering
as.dendrogram # Turn the object into a dendrogram.
plot(dend)
library(dendextend)
# 1. Create a customized dendrogram
mycols <- c("#2E9FDF", "#00AFBB", "#E7B800", "#FC4E07")
dend <- as.dendrogram(hc) %>%
set("branches_lwd", 1) %>% # Branches line width
set("branches_k_color", mycols, k = 4) %>% # Color branches by groups
set("labels_colors", mycols, k = 4) %>% # Color labels by groups
set("labels_cex", 0.5) # Change label size
# 2. Create plot
fviz_dend(dend)
df <- scale(mtcars)
# Default plot
heatmap(df, scale = "none")
col<- colorRampPalette(c("red", "white", "blue"))(256)
library("RColorBrewer")
col <- colorRampPalette(brewer.pal(10, "RdYlBu"))(256)
# Use RColorBrewer color palette names
library("RColorBrewer")
col <- colorRampPalette(brewer.pal(10, "RdYlBu"))(256)
heatmap(df, scale = "none", col = col,
RowSideColors = rep(c("blue", "pink"), each = 16),
ColSideColors = c(rep("purple", 5), rep("orange", 6)))
library("gplots")
##
## Attaching package: 'gplots'
## The following object is masked from 'package:stats':
##
## lowess
heatmap.2(df, scale = "none", col = bluered(100),
trace = "none", density.info = "none")
library("pheatmap")
pheatmap(df, cutree_rows = 4)
library(dendextend)
# order for rows
Rowv <- mtcars %>% scale %>% dist %>% hclust %>% as.dendrogram %>%
set("branches_k_color", k = 3) %>% set("branches_lwd", 1.2) %>%
ladderize
# Order for columns: We must transpose the data
Colv <- mtcars %>% scale %>% t %>% dist %>% hclust %>% as.dendrogram %>%
set("branches_k_color", k = 2, value = c("orange", "blue")) %>%
set("branches_lwd", 1.2) %>%
ladderize
heatmap(scale(mtcars), Rowv = Rowv, Colv = Colv,
scale = "none")
library(gplots)
heatmap.2(scale(mtcars), scale = "none", col = bluered(100),
Rowv = Rowv, Colv = Colv,
trace = "none", density.info = "none")
library(ComplexHeatmap)
## Loading required package: grid
## ========================================
## ComplexHeatmap version 1.17.1
## Bioconductor page: http://bioconductor.org/packages/ComplexHeatmap/
## Github page: https://github.com/jokergoo/ComplexHeatmap
## Documentation: http://bioconductor.org/packages/ComplexHeatmap/
##
## If you use it in published research, please cite:
## Gu, Z. Complex heatmaps reveal patterns and correlations in multidimensional
## genomic data. Bioinformatics 2016.
## ========================================
Heatmap(df,
name = "mtcars", #title of legend
column_title = "Variables", row_title = "Samples",
row_names_gp = gpar(fontsize = 7) # Text size for row names
)
library(circlize)
## ========================================
## circlize version 0.4.3
## CRAN page: https://cran.r-project.org/package=circlize
## Github page: https://github.com/jokergoo/circlize
## Documentation: http://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.
## ========================================
mycols <- colorRamp2(breaks = c(-2, 0, 2),
colors = c("green", "white", "red"))
Heatmap(df, name = "mtcars", col = mycols)
library("circlize")
library("RColorBrewer")
Heatmap(df, name = "mtcars",
col = colorRamp2(c(-2, 0, 2), brewer.pal(n=3, name="RdBu")))
library(dendextend)
row_dend = hclust(dist(df)) # row clustering
col_dend = hclust(dist(t(df))) # column clustering
Heatmap(df, name = "mtcars",
row_names_gp = gpar(fontsize = 6.5),
cluster_rows = color_branches(row_dend, k = 4),
cluster_columns = color_branches(col_dend, k = 2))
# split by a vector specifying rowgroups
Heatmap(df, name = "mtcars", split = mtcars$cyl,
row_names_gp = gpar(fontsize = 7))
# Split by combining multiple variables
Heatmap(df, name ="mtcars",
split = data.frame(cyl = mtcars$cyl, am = mtcars$am))
# Annotation data frame
annot_df <- data.frame(cyl = mtcars$cyl, am = mtcars$am,
mpg = mtcars$mpg)
# Define colors for each levels of qualitative variables
# Define gradient color for continuous variable (mpg)
col = list(cyl = c("4" = "green", "6" = "gray", "8" = "darkred"),
am = c("0" = "yellow", "1" = "orange"),
mpg = circlize::colorRamp2(c(17, 25),
c("lightblue", "purple")) )
# Create the heatmap annotation
ha <- HeatmapAnnotation(annot_df, col = col)
# Combine the heatmap and the annotation
Heatmap(df, name = "mtcars",
top_annotation = ha)
ha <- HeatmapAnnotation(annot_df, col = col, show_legend = FALSE)
Heatmap(df, name = "mtcars", top_annotation = ha)
# Define some graphics to display the distribution of columns
.hist = anno_histogram(df, gp = gpar(fill = "lightblue"))
.density = anno_density(df, type = "line", gp = gpar(col = "blue"))
ha_mix_top = HeatmapAnnotation(hist = .hist, density = .density)
# Define some graphics to display the distribution of rows
.violin = anno_density(df, type = "violin",
gp = gpar(fill = "lightblue"), which = "row")
.boxplot = anno_boxplot(df, which = "row")
ha_mix_right = HeatmapAnnotation(violin = .violin, bxplt = .boxplot,
which = "row", width = unit(4, "cm"))
# Combine annotation with heatmap
Heatmap(df, name = "mtcars",
column_names_gp = gpar(fontsize = 8),
top_annotation = ha_mix_top,
top_annotation_height = unit(3.8, "cm")) + ha_mix_right
# Heatmap 1
ht1 = Heatmap(df, name = "ht1", km = 2,
column_names_gp = gpar(fontsize = 9))
# Heatmap 2
ht2 = Heatmap(df, name = "ht2",
col = circlize::colorRamp2(c(-2, 0, 2), c("green", "white", "red")),
column_names_gp = gpar(fontsize = 9))
# Combine the two heatmaps
ht1 + ht2
draw(ht1 + ht2,
row_title = "Two heatmaps, row title",
row_title_gp = gpar(col = "red"),
column_title = "Two heatmaps, column title",
column_title_side = "bottom",
# Gap between heatmaps
gap = unit(0.5, "cm"))
expr <- readRDS(paste0(system.file(package = "ComplexHeatmap"),
"/extdata/gene_expression.rds"))
mat <- as.matrix(expr[, grep("cell", colnames(expr))])
type <- gsub("s\\d+_", "", colnames(mat))
ha = HeatmapAnnotation(df = data.frame(type = type))
Heatmap(mat, name = "expression", km = 5, top_annotation = ha,
top_annotation_height = unit(4, "mm"),
show_row_names = FALSE, show_column_names = FALSE) +
Heatmap(expr$length, name = "length", width = unit(5, "mm"),
col = circlize::colorRamp2(c(0, 100000), c("white", "orange"))) +
Heatmap(expr$type, name = "type", width = unit(5, "mm")) +
Heatmap(expr$chr, name = "chr", width = unit(5, "mm"),
col = circlize::rand_color(length(unique(expr$chr))))
