# Clustering

## R

• Partitional Clustering in R: The Essential.
• K-means,
• K-medoids clustering or PAM (Partitioning Around Medoids),
• CLARA (Clustering Large Applications), which is an extension to PAM adapted for large data sets. According to Wikibooks: since CLARA adopts a sampling approach, the quality of its clustering results depends greatly on the size of the sample. When the sample size is small, CLARA’s efficiency in clustering large data sets comes at the cost of clustering quality.

## k-means clustering

### Figures of merit (FOM) plot

maanova::fom(), Validating clustering for gene expression data by Yeung 2001.

## Hierarchical clustering

For the kth cluster, define the Error Sum of Squares as $\displaystyle{ ESS_m = }$ sum of squared deviations (squared Euclidean distance) from the cluster centroid. $\displaystyle{ ESS_m = \sum_{l=1}^{n_m}\sum_{k=1}^p (x_{ml,k} - \bar{x}_{m,k})^2 }$ in which $\displaystyle{ \bar{x}_{m,k} = (1/n_m) \sum_{l=1}^{n_m} x_{ml,k} }$ the mean of the mth cluster for the k-th variable, $\displaystyle{ x_{ml,k} }$ being the score on the kth variable $\displaystyle{ (k=1,\dots,p) }$ for the l-th object $\displaystyle{ (l=1,\dots,n_m) }$ in the mth cluster $\displaystyle{ (m=1,\dots,g) }$.

If there are C clusters, define the Total Error Sum of Squares as Sum of Squares as $\displaystyle{ ESS = \sum_m ESS_m, m=1,\dots,C }$

Consider the union of every possible pair of clusters.

Combine the 2 clusters whose combination combination results in the smallest increase in ESS.

1. The default linkage is "complete" in R.
2. Ward's method tends to join clusters with a small number of observations, and it is strongly biased toward producing clusters with the same shape and with roughly the same number of observations.
3. It is also very sensitive to outliers. See Milligan (1980).

Take pomeroy data (7129 x 90) for an example:

library(gplots)

lr = as.matrix(lr)
method = "average" # method <- "complete"; method <- "ward.D2"; method <- "single"
hclust1 <- function(x) hclust(x, method= method)
heatmap.2(lr, col=bluered(75), hclustfun = hclust1, distfun = dist,
density.info="density", scale = "none",
key=FALSE, symkey=FALSE, trace="none",
main = method)


It seems average method will create a waterfall like dendrogram. Ward method will produce a tight clusters. Complete linkage produces a more 中庸 result.

### K-Means vs hierarchical clustering

K-Means vs hierarchical clustering. Hierarchical clustering is usually preferable, as it is both more flexible and has fewer hidden assumptions about the distribution of the underlying data.

### Correlation distance

# Pairwise correlation between samples (columns)
cols.cor <- cor(mydata, use = "pairwise.complete.obs", method = "pearson")
# Pairwise correlation between rows (genes)
rows.cor <- cor(t(mydata), use = "pairwise.complete.obs", method = "pearson")

## Row- and column-wise clustering using correlation
hclust.col <- hclust(as.dist(1-cols.cor))
hclust.row <- hclust(as.dist(1-rows.cor))

# Plot the heatmap
library("gplots")
heatmap.2(mydata, scale = "row", col = bluered(100),
trace = "none", density.info = "none",
Colv = as.dendrogram(hclust.col),
Rowv = as.dendrogram(hclust.row)
)


### Get the ordering

set.seed(123)
dat <- matrix(rnorm(20), ncol=2)

# perform hierarchical clustering
hc <- hclust(dist(dat))

# plot dendrogram
plot(hc)

# get ordering of leaves
ord <- order.dendrogram(as.dendrogram(hc))
ord
#   8  3  6  5 10  1  9  7  2  4
# Same as seen on the dendrogram nodes


## Dendrogram

### dendextend* package

• Introduction
• Features:
• Adjusting a tree’s graphical parameters: You can use the dendextend package to adjust the color, size, type, and other graphical parameters of a dendrogram’s branches, nodes, and labels1.
• Comparing dendrograms: The dendextend package provides several advanced methods for visually and ** statistically comparing different dendrograms to one another1.
• Manipulating dendrograms: The dendextend package provides utility functions for manipulating dendrogram objects, allowing you to change their color, shape, and content2.
• Paper
• dendextend::plot(, horiz=TRUE) allows to rotate a dendrogram with tips on RHS.
• plot_horiz.dendrogram() allows to rotate a dendrogram with tips on LHS.
• The package has a function tanglegram() to compare two trees of hierarchical clusterings. See this post and its vignette.

Simplified from dendextend's vignette or Label and color leaf dendrogram.

library(dendextend)

set.seed(1234)
iris <- datasets::iris[sample(150, 30), ] # subset for better view
iris2 <- iris[, -5] # data
species_labels <- iris[, 5] # group for coloring

hc_iris <- hclust(dist(iris2), method = "complete")
iris_species <- levels(species_labels)

dend <- as.dendrogram(hc_iris)
colorCodes <- c("red", "green", "blue")
labels_colors(dend) <- colorCodes[as.numeric(species_labels)][order.dendrogram(dend)]

labels(dend) <- paste0(as.character(species_labels)[order.dendrogram(dend)],
"(", labels(dend), ")")
# We hang the dendrogram a bit:
dend <- hang.dendrogram(dend, hang_height=0.1)
dend <- set(dend, "labels_cex", 1.0)

par(mfrow=c(1,2), mar = c(3,3,1,7))
plot(dend, main = "", horiz =  TRUE)
legend("topleft", legend = iris_species, fill = colorCodes)

par(mar=c(3,1,1,5))
plot(as.dendrogram(hc_iris),horiz=TRUE)
dev.off()


### Flip/rotate branches

• rotate() function from dendextend package.
hc <- hclust(dist(USArrests[c(1, 6, 13, 20, 23), ]), "ave")
plot(hc, main = "Original tree")
plot(rotate(hc, c(2:5, 1)), main = "Rotates the left most leaf \n
into the right side of the tree")
# Or
plot(rotate(hc, c("Maryland", "Colorado", "Alabama", "Illinois", "Minnesota")), main="Rotated")

• https://www.biostars.org/p/279775/

### Color labels

• https://www.r-graph-gallery.com/dendrogram/
• 7+ ways to plot dendrograms in R
• dendrapply(). Cons: 1. do not print the sample ID (solution: dendextend package), 2. not interactive.
library(RColorBrewer)
# matrix contains genomics-style data where columns are samples
#   (if otherwise remove the transposition below)
# labels is a factor variable going along the columns of matrix
# cex: use a smaller number if the number of sample is large
plotHclustColors <- function(matrix,labels, distance="eucl", method="ward.D2", palette="Set1", cex=.3, ...) {
#colnames(matrix) <- labels
if (distance == "eucl") {
d <- dist(t(matrix))
} else if (distance == "corr") {
d <- as.dist(1-cor(matrix))
}
hc <- hclust(d, method = method)
labels <- factor(labels)
if (nlevels(labels) == 2) {
labelColors <- brewer.pal(3, palette)[1:2]
} else {
labelColors <- brewer.pal(nlevels(labels), palette)
}
colLab <- function(n) {
if (is.leaf(n)) {
a <- attributes(n)
labCol <- labelColors[which(levels(labels) == a$label)] attr(n, "nodePar") <- c(a$nodePar, lab.col=labCol)
}
n
}
clusDendro <- dendrapply(as.dendrogram(hc), colLab)
# I change cex because there are lots of samples
op <- par(mar=c(5,3,1,.5)+.1)
plot(clusDendro,...)
par(op)
}

genedata <- matrix(rnorm(100*20), nc=20)
colnames(genedata) <- paste0("S", 1:20)
pheno <- rep(c(1,2), each =10)

plotHclustColors(genedata, pheno, cex=.8)


## Optimal number of clusters

### Silhouette score/width ### Scree/elbow plot

• Cf scree plot for PCA analysis
• K-Means Clustering in R: Step-by-Step Example. ?factoextra::fviz_nbclust (good integration with different clustering methods and evaluation statistic)
• datacamp
# Use map_dbl to run many models with varying value of k (centers)
tot_withinss <- map_dbl(1:10,  function(k){
model <- kmeans(x = lineup, centers = k)
model$tot.withinss }) # Generate a data frame containing both k and tot_withinss elbow_df <- data.frame( k = 1:10, tot_withinss = tot_withinss ) # Plot the elbow plot ggplot(elbow_df, aes(x = k, y = tot_withinss)) + geom_line() + scale_x_continuous(breaks = 1:10)  ### kBET: k-nearest neighbour batch effect test • Buttner, M., Miao, Z., Wolf, F. A., Teichmann, S. A. & Theis, F. J. A test metric for assessing single-cell RNA-seq batch correction. Nat. Methods 16, 43–49 (2019). • https://github.com/theislab/kBET • quantify mixability; how well cells of the same type from different batches were grouped together ### Alignment score • Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018). • quantify mixability; how well cells of the same type from different batches were grouped together ### dynamicTreeCut package dynamicTreeCut: Methods for Detection of Clusters in Hierarchical Clustering Dendrograms. cutreeDynamicTree(). Found in here. ## Compare 2 clustering methods, ARI ## Benchmark clustering algorithms ## Significance analysis ## Power Statistical power for cluster analysis 2022. It includes several take-home message. ## Louvain algorithm: graph-based method ## Mahalanobis distance ## Dendrogram ### as.dendrogram ### Large dendrograms # You probably don't understand heatmaps # Evaluate the effect of centering & scaling ## Different distance measures ## 1-correlation distance Effect of centering and scaling on clustering of genes and samples in terms of distance. 'Yes' means the distance was changed compared to the baseline where no centering or scaling was applied. clustering genes clustering samples centering on each genes No Yes* scaling on each genes No Yes* • Note: Cor(X, Y) = Cor(X + constant scalar, Y). If the constant is not a scalar, the equation won't hold. Or think about plotting data in a 2 dimension space. If the X data has a constant shift for all observations/genes, then the linear correlation won't be changed. ## Euclidean distance clustering genes clustering samples centering on each genes Yes No1 scaling on each genes Yes Yes2 Note 1. $\displaystyle{ \sum(X_i - Y_i)^2 = \sum(X_i-c_i - (Y_i-c_i))^2 }$ 2. $\displaystyle{ \sum(X_i - Y_i)^2 \neq \sum(X_i/c_i - Y_i/c_i)^2 }$ ## Supporting R code 1. 1-Corr distance source("http://www.bioconductor.org/biocLite.R") biocLite("limma"); biocLite("ALL") library(limma); library(ALL) data(ALL) eset <- ALL[, ALL$mol.biol %in% c("BCR/ABL", "ALL1/AF4")]

f <- factor(as.character(eset$mol.biol)) design <- model.matrix(~f) fit <- eBayes(lmFit(eset,design)) selected <- p.adjust(fit$p.value[, 2]) < 0.05
esetSel <- eset [selected, ]   # 165 x 47
heatmap(exprs(esetSel))

esetSel2 <- esetSel[sample(1:nrow(esetSel), 20), sample(1:ncol(esetSel), 10)] # 20 x 10

dist.no <- 1-cor(t(as.matrix(esetSel2)))
dist.mean <- 1-cor(t(sweep(as.matrix(esetSel2), 1L, rowMeans(as.matrix(esetSel2))))) # gene variance has not changed!
dist.median <- 1-cor(t(sweep(as.matrix(esetSel2), 1L, apply(esetSel2, 1, median))))

range(dist.no - dist.mean) #  -1.110223e-16  0.000000e+00
range(dist.no - dist.median) #  -1.110223e-16  0.000000e+00
range(dist.mean - dist.median) #  0 0


So centering (no matter which measure: mean, median, ...) genes won't affect 1-corr distance of genes.

dist.mean <- 1-cor(t(sweep(as.matrix(esetSel2), 1L, rowMeans(as.matrix(esetSel2)), "/")))
dist.median <- 1-cor(t(sweep(as.matrix(esetSel2), 1L, apply(esetSel2, 1, median), "/")))
range(dist.no - dist.mean) #  -8.881784e-16  6.661338e-16
range(dist.no - dist.median) #  -6.661338e-16  6.661338e-16
range(dist.mean - dist.median) #  -1.110223e-15  1.554312e-15


So scaling after centering (no matter what measures: mean, median,...) won't affect 1-corr distance of genes.

How about centering / scaling genes on array clustering?

dist.no <- 1-cor(as.matrix(esetSel2))
dist.mean <- 1-cor(sweep(as.matrix(esetSel2), 1L, rowMeans(as.matrix(esetSel2)))) # array variance has changed!
dist.median <- 1-cor(sweep(as.matrix(esetSel2), 1L, apply(esetSel2, 1, median)))

range(dist.no - dist.mean) #  -1.547086  0.000000
range(dist.no - dist.median) #  -1.483427  0.000000
range(dist.mean - dist.median) #  -0.5283601  0.6164602

dist.mean <- 1-cor(sweep(as.matrix(esetSel2), 1L, rowMeans(as.matrix(esetSel2)), "/"))
dist.median <- 1-cor(sweep(as.matrix(esetSel2), 1L, apply(esetSel2, 1, median), "/"))
range(dist.no - dist.mean) #  -1.477407  0.000000
range(dist.no - dist.median) #  -1.349419  0.000000
range(dist.mean - dist.median) #  -0.5419534  0.6269875


2. Euclidean distance

dist.no <- dist(as.matrix(esetSel2))
dist.mean <- dist(sweep(as.matrix(esetSel2), 1L, rowMeans(as.matrix(esetSel2))))
dist.median <- dist(sweep(as.matrix(esetSel2), 1L, apply(esetSel2, 1, median)))

range(dist.no - dist.mean) #  7.198864e-05 2.193487e+01
range(dist.no - dist.median) #  -0.3715231 21.9320846
range(dist.mean - dist.median) #  -0.923717629 -0.000088385


Centering does affect the Euclidean distance.

dist.mean <- dist(sweep(as.matrix(esetSel2), 1L, rowMeans(as.matrix(esetSel2)), "/"))
dist.median <- dist(sweep(as.matrix(esetSel2), 1L, apply(esetSel2, 1, median), "/"))

range(dist.no - dist.mean) #   0.7005071 24.0698991
range(dist.no - dist.median) #   0.636749 24.068920
range(dist.mean - dist.median) #  -0.22122869  0.02906131


And scaling affects Euclidean distance too.

How about centering / scaling genes on array clustering?

dist.no <- dist(t(as.matrix(esetSel2)))
dist.mean <- dist(t(sweep(as.matrix(esetSel2), 1L, rowMeans(as.matrix(esetSel2)))))
dist.median <- dist(t(sweep(as.matrix(esetSel2), 1L, apply(esetSel2, 1, median))))

range(dist.no - dist.mean) # 0 0
range(dist.no - dist.median)  # 0 0
range(dist.mean - dist.median) # 0 0

dist.mean <- dist(t(sweep(as.matrix(esetSel2), 1L, rowMeans(as.matrix(esetSel2)), "/")))
dist.median <- dist(t(sweep(as.matrix(esetSel2), 1L, apply(esetSel2, 1, median), "/")))

range(dist.no - dist.mean) #  1.698960 9.383789
range(dist.no - dist.median)  #  1.683028 9.311603
range(dist.mean - dist.median) #  -0.09139173  0.02546394


## Euclidean distance and Pearson correlation relationship

$\displaystyle{ r(X, Y) = 1 - \frac{d^2(X, Y)}{2n} }$

where $\displaystyle{ r(X, Y) }$ is the Pearson correlation of variables X and Y and $\displaystyle{ d^2(X, Y) }$ is the squared Euclidean distance of X and Y.

# Simple image plot using image() function

image(t(x)) is similar to stats::heatmap(x, Rowv = NA, Colv = NA, scale = "none") except heatmap() can show column/row names while image() won't. The default colors are the same too though not pretty.

### Create Matrix plot using colors to fill grid
# Create matrix.   Using random values for this example.
rand <- rnorm(286, 0.8, 0.3)
mat <- matrix(sort(rand), nr=26)
dim(mat)  # Check dimensions

# Create x and y labels
yLabels <- seq(1, 26, 1)
xLabels <- c("a", "b", "c", "d", "e", "f", "g", "h", "i",
"j", "k");

# Set min and max values of rand
min <- min(rand, na.rm=T)
max <- max(rand, na.rm=T)

# Red and green range from 0 to 1 while Blue ranges from 1 to 0
ColorRamp <- rgb(seq(0.95,0.99,length=50),  # Red
seq(0.95,0.05,length=50),  # Green
seq(0.95,0.05,length=50))  # Blue
ColorLevels <- seq(min, max, length=length(ColorRamp))

# Set layout.  We are going to include a colorbar next to plot.
layout(matrix(data=c(1,2), nrow=1, ncol=2), widths=c(4,1),
heights=c(1,1))
#plotting margins.  These seem to work well for me.
par(mar = c(5,5,2.5,1), font = 2)

# Plot it up!
image(1:ncol(mat), 1:nrow(mat), t(mat),
col=ColorRamp, xlab="Variable", ylab="Time",
axes=FALSE, zlim=c(min,max),
main= NA)

# Now annotate the plot
box()
axis(side = 1, at=seq(1,length(xLabels),1), labels=xLabels,
cex.axis=1.0)
axis(side = 2, at=seq(1,length(yLabels),1), labels=yLabels, las= 1,
cex.axis=1)

# Add colorbar to second plot region
par(mar = c(3,2.5,2.5,2))
image(1, ColorLevels,
matrix(data=ColorLevels, ncol=length(ColorLevels),nrow=1),
col=ColorRamp,xlab="",ylab="",xaxt="n", las = 1)


If we define ColorRamp variable using the following way, we will get the 2nd plot.

require(RColorBrewer) # get brewer.pal()
ColorRamp <- colorRampPalette( rev(brewer.pal(9, "RdBu")) )(25)


Note that

• colorRampPalette() is an R's built-in function. It interpolate a set of given colors to create new color palettes. The return is a function that takes an integer argument (the required number of colors) and returns a character vector of colors (see rgb()) interpolating the given sequence (similar to heat.colors() or terrain.colors()).
# An example of showing 50 shades of grey in R
greys <- grep("^grey", colours(), value = TRUE)
length(greys)
#  102
#  "#000000" "#FFFFFF"
# And 50 shades of grey?
mat <- matrix(rep(1:50, each = 50))
image(mat, axes = FALSE, col = fiftyGreys)
box()

• (For dual channel data) brewer.pal(9, "RdBu") creates a diverging palette based on "RdBu" with 9 colors. See help(brewer.pal, package="RColorBrewer") for a list of palette name. The meaning of the palette name can be found on colorbrew2.org website. In genomics, we will add rev() such as rev(brewer.pal(9, "RdBu")).
• (For single channel data) brewer.pal(9, "Blues") is good. See an example.

# stats::heatmap()

• ?heatmap. It includes parameters for settings
• margins (margins )
• font size (cexRow, cexCol),
• row/column orders (Rowv, Colv)
• scale = c("row", "column", "none").
• Source code of heatmap()
• Hierarchical Clustering in R: The Essentials. Note stats::heatmap() can add color side bars too.
• If we run the heatmap() function line-by-line, we see the side bars were drawn by using par(mar) & image(, axes = FALSE).
• Default par()$mar is (5,4,4,1)+.5 • layout(lmat, widths = lwid, heights = lhei, respect = TRUE) > lmat [,1] [,2] [,3] [1,] 0 0 5 [2,] 0 0 2 [3,] 4 1 3 # 1 = RowSideColors # 2 = ColSideColors # 3 = heatmap # 4 = Row dendrogram # 5 = Column dendrogram > lwid # lhei is the same  1.0 0.2 4.0  • When it is drawing RowSideColors, par()$mar is changed to (5, 0, 0, .5)
• When it is drawing ColSideColors, par()$mar is changed to (.5, 0, 0, 5) • When it is drawing the heatmap, par()$mar is changed to (5, 0, 0, 5)
• image() was called 3 times if RowSideColors and ColSideColors are TRUE.
• Bottom & right texts on x-axis & y-axis are drawn by axis()
• When it is drawing the row dendrogram, par()$mar is changed to (5, 0, 0, 0) • When it is drawing the column dendrogram, par()$mar is changed to (0, 0, 0, 5)

## Rowv, Colv: reorder of rows and columns

• ?heatmap, ?heatmap.2.
• Rowv/Colv. Either a dendrogram or a vector of values used to reorder the row dendrogram or NA to suppress any row dendrogram (and reordering) or by default, NULL.
• If either is a vector (of ‘weights’) then the appropriate dendrogram is reordered according to the supplied values subject to the constraints imposed by the dendrogram, by reorder(dd, Rowv), in the row case. If either is missing, as by default, then the ordering of the corresponding dendrogram is by the mean value of the rows/columns, i.e., in the case of rows, Rowv <- rowMeans(x, na.rm = na.rm).
• ?hclust The algorithm used in hclust is to order the subtree so that the tighter cluster is on the left (the last, i.e., most recent, merge of the left subtree is at a lower value than the last merge of the right subtree). Single observations are the tightest clusters possible, and merges involving two observations place them in order by their observation sequence number. (Not clear about the ordering of two single observations?)
• ?reorder.dendrogram. At each node, the branches are ordered in increasing weights where the weight of a branch is defined as f(wj) where f is agglo.FUN and wj is the weight of the j-th sub branch.
reorder(x, wts, agglo.FUN = sum, …)

• Order Rows & Columns of Heatmap in R (2 Examples), How does R heatmap order rows by default?
set.seed(3255434)                                   # Set seed for reproducibility
my_mat <- matrix(rnorm(25, 0, 10), nrow = 5)        # Create example matrix
colnames(my_mat) <- paste0("col", 1:5)              # Specify column names
rownames(my_mat) <- paste0("row", 1:5)              # Specify row names
my_mat
apply(my_mat, 1, mean) |> round(2)
# row1  row2  row3  row4  row5
# 1.24  0.37  5.77 -3.70 -2.74
apply(my_mat, 2, mean) |> round(2)
# col1  col2  col3  col4  col5
# -2.64  2.98 -1.21  5.64 -3.83

heatmap(my_mat)
# col order is col1 col3 col2 col5 col4
#       +-----------+
#       |           |
#   +------+        |
#   |      |      +----+
#   |    +---+    |    |
#   |    |   |    |    |
#   1    3   2    5    4
# -2.6 -1.2 2.9 -3.8 5.6
# heatmap() has applied reorder() by default internally

# To obtain the same ordering of hclust():
hclust_rows <- as.dendrogram(hclust(dist(my_mat)))  # Calculate hclust dendrograms
hclust_cols <- as.dendrogram(hclust(dist(t(my_mat))))
heatmap(my_mat,                                     # Draw heatmap with hclust dendrograms
Rowv = hclust_rows,
Colv = hclust_cols)$colInd # 4 5 1 2 3 plot(hclust(dist(t(my_mat)))) # col order is col4 col5 col1 col2 col3 # +---------+ # | | # | +-----+ # +----+ | | # | | | +---+ # | | | | | # 4 5 1 2 3 # 5.6 -3.8 -2.6 2.9 -1.2 # order by the tightness # # To obtain the same dendrogram of heatmap(): Colv <- colMeans(my_mat, na.rm = T) plot(reorder(hclust_cols, Colv))  • Heatmap in R: Static and Interactive Visualization ## scale parameter The scale parameter in heatmap() or heatmap.2() only affects the coloring. It does not affect the clustering. In stats::heatmap(, scale="row") by default, but in gplots::heatmap.2(, scale = "none") by default. When we check the heatmap.2() source code, we see it runs hclust() before calling sweep() if scale = "row". The scaled x was then used to display the carpet by using the image() function. It looks like many people misunderstand the meaning; see this post Row scaling from ComplexHeatmap. The scale parameter in tidyHeatmap also did the scaling before clustering. However, we can still do that by following this post Can we scale data and trim data for better presentation by specifying our own clustering results in cluster_rows and cluster_columns parameters. library(gplots) nr <- 5; nc <- 20 set.seed(1) x <- matrix(rnorm(nr*nc), nr=nr) x[1,] <- x[1,]-min(x[1,]) # in order to see the effect of 'scale' # the following 2 lines prove the scale parameter does not affect clustering o1 <- heatmap.2(x, scale = "row", main = 'row', trace ='none', col=bluered(75)) # colors are balanced per row, but not column o2 <- heatmap.2(x, scale = "none", main = 'none', trace ='none', col=bluered(75)) # colors are imbalanced identical(o1$colInd, o2$colInd) #  TRUE identical(o1$rowInd, o2$rowInd) #  TRUE # the following line proves we'll get a different result if we input a z-score matrix o3 <- heatmap.2(t(o1$carpet), scale = "none", main = 'o1$carpet', trace ='none', col=bluered(75)) # totally different  ## Is it important to scale data before clustering Is it important to scale data before clustering?. So if we are using the correlation as the distance, we don't need to use z-score transformation. ## dev.hold(), dev.flush() # gplots package and heatmap.2() The following example is extracted from DESeq2 package. ## ----loadDESeq2, echo=FALSE---------------------------------------------- # in order to print version number below library("DESeq2") ## ----loadExonsByGene, echo=FALSE----------------------------------------- library("parathyroidSE") library("GenomicFeatures") data(exonsByGene) ## ----locateFiles, echo=FALSE--------------------------------------------- bamDir <- system.file("extdata",package="parathyroidSE",mustWork=TRUE) fls <- list.files(bamDir, pattern="bam$",full=TRUE)

## ----bamfilepaired-------------------------------------------------------
library( "Rsamtools" )
bamLst <- BamFileList( fls, yieldSize=100000 )

## ----sumOver-------------------------------------------------------------
library( "GenomicAlignments" )
se <- summarizeOverlaps( exonsByGene, bamLst,
mode="Union",
singleEnd=FALSE,
ignore.strand=TRUE,
fragments=TRUE )

## ----libraries-----------------------------------------------------------
library( "DESeq2" )
library( "parathyroidSE" )

data( "parathyroidGenesSE" )
se <- parathyroidGenesSE
colnames(se) <- se$run ## ----fromSE-------------------------------------------------------------- ddsFull <- DESeqDataSet( se, design = ~ patient + treatment ) ## ----collapse------------------------------------------------------------ ddsCollapsed <- collapseReplicates( ddsFull, groupby = ddsFull$sample,
run = ddsFull$run ) ## ----subsetCols---------------------------------------------------------- dds <- ddsCollapsed[ , ddsCollapsed$time == "48h" ]

## ----subsetRows, echo=FALSE----------------------------------------------
idx <- which(rowSums(counts(dds)) > 0)[1:4000]
dds <- dds[idx,]

## ----runDESeq, cache=TRUE------------------------------------------------
dds <- DESeq(dds)

rld <- rlog( dds)

library( "genefilter" )
topVarGenes <- head( order( rowVars( assay(rld) ), decreasing=TRUE ), 35 )

## ----beginner_geneHeatmap, fig.width=9, fig.height=9---------------------
library(RColorBrewer)
library(gplots)
heatmap.2( assay(rld)[ topVarGenes, ], scale="row",
trace="none", dendrogram="column",
density.info="density",
key.title = "Expression",
key.xlab = "Row Z-score",
col = colorRampPalette( rev(brewer.pal(9, "RdBu")) )(255))


## heatmap.2() vs heatmap()

It looks the main difference is heatmap.2() can produce color key on the top-left corner. See Heatmap in R: Static and Interactive Visualization.

heatmap.2(..., hclustfun = function(x) hclust(x,method = 'ward.D2'), ...)


## Rowv, Colv: reorder of rows and columns

Same as the case in heatmap().

## Change breaks in scale

Con: it'll be difficult to interpret the heatmap

## Font size, rotation

See the help page

cexCol=.8   # reduce the label size from 1 to .8
offsetCol=0 # reduce the offset space from .5 to 0
# 2-element vector giving the (left-right, top-bottom) justification of row/column labels
adjCol=c(1,0)  # align to top; only meaningful if we rotate the labels
adjCol=c(0,1)  # align to bottom; some long text may go inside the figure
adjCol=c(1,1)  # how to explain it?
srtCol=45   # Rotate 45 degrees

keysize=2    # increase the keysize from the default 1.5
key = TRUE   # default
key.xlab=NA  # default is NULL
key.title=NA


## Color labels and side bars

https://stackoverflow.com/questions/13206335/color-labels-text-in-r-heatmap. See the options in an example in ?heatmap.2.

• colRow, colCol
• RowSideColors, ColSideColors
## Color the labels to match RowSideColors and ColSideColors
hv <- heatmap.2(x, col=cm.colors(255), scale="column",
RowSideColors=rc, ColSideColors=cc, margin=c(5, 10),
xlab="specification variables", ylab= "Car Models",
main="heatmap(<Mtcars data>, ..., scale=\"column\")",
tracecol="green", density="density", colRow=rc, colCol=cc,


## Dendrogram width and height

# Default
lhei <- c(1.5, 4)
lwid <- c(1.5, 4)


Note these are relative. Recall heatmap.2() makes a 2x2 grid: color key, dendrograms (left & top) and the heatmap (right & bottom).

## Modify the margins for column/row names

# Default
margins <- c(5, 5) # (column, row)


Note par(mar) does not work.

## White strips (artifacts)

On my Linux (Dell Precision T3500, NVIDIA GF108GL, Quadro 600, 1920x1080), the heatmap shows several white strips when I set a resolution 800x800 (see the plot of 10000 genes shown below). Note if I set a higher resolution 1920x1920, the problem is gone but the color key map will be quite large and the text font will be small.

On MacBook Pro (integrated Intel Iris Pro, 2880x1800), there is no artifact even with 800x800 resolution.

How about saving the plots using

• a different format (eg tiff) or even the lossless compression option - not help
• Cairo package - works. Note that the default background is transparent.

## RowSideColors and ColSideColors options

### Legend/annotation

legend("topright",
legend = unique(dat$GO), col = unique(as.numeric(dat$GO)),
lty= 1,
lwd = 5,
cex=.7)

# In practice
par(xpd = FALSE) # default
heatmap.2(, ColSideColors=cc) # add sample dendrogram
par(xpd = NA)
legend(0, .5, ...) # legend is on the LHS
# the coordinate is device dependent


Another example from video which makes use of an archived package heatmap.plus.

legend(0.8,1,
legend=paste(treatment_times,"weeks"),
fill=treatment_color_options,
cex=0.5)
legend(0.8,0.9,
legend=c("Control","Treatment"),
fill=c('#808080','#FFC0CB'),
cex=0.5)


### heatmap.plus()

How to Make an R Heatmap with Annotations and Legend. ColSideColors can be a matrix (n x 2). So it is possible to draw two side colors on the heatmap. Unfortunately the package was removed from CRAN in 2021-04. The package was used by TCGAbiolinks but now this package uses ComplexHeatmap instead.

devtools::install_version("heatmap.plus", "1.3")


# ggplot2 package

## ggplot2::geom_tile()

# Suppose dat=[x, y1, y2, y3] is a wide matrix
# and we want to make a long matrix like dat=[x, y, val]
library(tidyr)
dat <- dat %>% pivot_longer(!x, names_to = 'y', values_to='val')
ggplot(dat, aes(x, y)) +
geom_tile(aes(fill = val), colour = "white") +
scale_fill_gradient2(low = "blue", mid = "white", high = "red") +
labs(y="Cell Line", fill= "Log GI50")
# white is the border color
# grey = NA by default
# labs(fill) is to change the title
# labs(y) is to change the y-axis label


# NMF package

aheatmap() function.

# ComplexHeatmap

Pros

## Clustering

• Whether to cluster rows or not
Heatmap(mat, cluster_rows = F)

• Whether to show the dendrogram or not
Heatmap(mat, show_column_dend = F)

• Change the default distance method
Heatmap(mat, clustering_distance_rows = function(m) dist(m))
Heatmap(mat, clustering_distance_rows = function(x, y) 1-cor(x, y))

• Change the default agglomeration/linkage method
Heatmap(mat, clustering_method_rows = "complete")

• Change the clustering method in rows or columns
Heatmap(mat, cluster_rows = diana(mat),
cluster_columns = agnes(t(mat)))
# 小心
# ** if cluster_columns is set as a function, you don't need to transpose the matrix **
Heatmap(mat, cluster_rows = diana,
cluster_columns = agnes)
# the above is the same as the following
# Note, when cluster_rows is set as a function, the argument m is the input mat itself,
# while for cluster_columns, m is the transpose of mat.
Heatmap(mat, cluster_rows = function(m) as.dendrogram(diana(m)),
cluster_columns = function(m) as.dendrogram(agnes(m)))
fh = function(x) fastcluster::hclust(dist(x))
Heatmap(mat, cluster_rows = fh, cluster_columns = fh)

• Run clustering in each of subgroup
# you might already have a subgroup classification for the matrix rows or columns,
# and you only want to perform clustering for the features in the same subgroup.
group = kmeans(t(mat), centers = 3)$cluster Heatmap(mat, cluster_columns = cluster_within_group(mat, group))  ### Render dendrograms We can add colors to branches of the dendrogram after we cut the tree. See 2.3.3 Render dendrograms ### Reorder/rotate branches in dendrograms • In the Heatmap() function, dendrograms are reordered to make features with larger difference more separated from each others (see reorder.dendrogram()). • See an interesting example which makes use of the dendsort package. Not really useful. • 2.3.4 Reorder dendrograms • Inconsistent clustering with ComplexHeatmap?. Good explanation! • row_dend_reorder/column_dend_reorder with default value TRUE. • If we set "row_dend_reorder/column_dend_reorder" to be FALSE, then the orders obtained from hclust() & Heatmap() will be the same. More specifically, the order will be the same for columns and the order will be reversed for rows. • By default, Heatmap() will create a different order than hclust(). If we like to get the same order as hclust(), we can do: Heatmap(my_mat, column_dend_reorder = F, row_dend_reorder = F) # OR hclust_rows <- as.dendrogram(hclust(dist(my_mat))) hclust_cols <- as.dendrogram(hclust(dist(t(my_mat))) Heatmap(my_mat, cluster_columns = hclust_cols, column_dend_reorder = F, cluster_rows = hclust_rows, row_dend_reorder = F, name = 'my_mat')  • By default, Heatmap() can create the same order as heatmap()/heatmap.2() function for columns but the row orders are reversed (but when I try another data, the statement does not hold). Heatmap(my_mat) # OR Colv <- colMeans(my_mat, na.rm = T) hclust_cols2 <- reorder(hclust_cols, Colv) Rowv <- rowMeans(my_mat, na.rm = T) hclust_rows2 <- reorder(hclust_rows, Rowv) Heatmap(my_mat, cluster_columns = hclust_cols2, column_dend_reorder = F, cluster_rows = hclust_rows2, row_dend_reorder = F, name = 'my_mat2') # PS. columns order is the same as heatmap(), # but row order is the "reverse" of the order of heatmap()  • The order of rows and columns in a heatmap produced by the heatmap function can be different from the order produced by the hclust function because the heatmap function uses additional steps to reorder the dendrogram based on row/column means (Order of rows in heatmap?). This is done through the reorderfun parameter, which takes a function that reorders the dendrogram as much as possible based on row/column means. If you want to use the same order produced by the hclust function in your heatmap, you can extract the dendrogram from the hclust object and pass it to the Rowv or Colv arguments of the heatmap function. You can also set the reorderfun parameter to a function that does not reorder the dendrogram. • Use dendextend package (see the next section). The 1st plot shows the original heatmap. The 2nd plot shows how to use the result of hclust() in the Heatmap() function. The 3rd plot shows how to rotate branches using the dendextend package. ### dendextend package • See clustering section. • Examples. See the plots given in the last section for how to use rotate() function to rotate branches. For rows, if we want to use numerical numbers instead of labels in order parameter, we need to count from top to bottom. For columns, we can count from left to right. # create a dendrogram hc <- hclust(dist(USArrests), "ave") dend <- as.dendrogram(hc) # manipulate the dendrogram using the dendextend package dend2 <- color_branches(dend, k = 3) # create a heatmap using the ComplexHeatmap package Heatmap(USArrests, name = "USArrests", cluster_rows = dend2)  ## Get the rows/columns order Use row_order()/column_order(). See 4.12 Get orders and dendrograms set.seed(123) dat <- matrix(rnorm(20), ncol=2) hc <- hclust(dist(dat)) plot(hc) # get ordering of leaves ord <- order.dendrogram(as.dendrogram(hc)) ord #  8 3 6 5 10 1 9 7 2 4 rownames(dat) <- 1:10 Heatmap(dat) row_order(draw(Heatmap(dat)) ) #  6 3 7 4 2 1 9 5 10 8 # Same order if I read the labels from top to down # Differ from hclust() b/c reordering  ## Set the rows/columns order manually Heatmap(mat, name = "mat", row_order = order(as.numeric(gsub("row", "", rownames(mat)))), column_order = order(as.numeric(gsub("column", "", colnames(mat)))), column_title = "reorder matrix")  ## Rotate labels Heatmap(mat, name = "mat", column_names_rot = 45)  ## Heatmap split ## Colors and legend • How to make continuous legend symmetric? #82, 2020 To exactly control the break values on the legend, you can set heatmap_legend_param argument in Heatmap() function. • Use circlize::colorRamp2() to change the color limit including the color specification. PS: NO need to use library()/require() to load the circlize package. • ComplexHeatmap break values appear different in the plots #361, 2019. pretty(range(x), n=3) Heatmap( xm, col = colorRamp2(c(min(xm), 0, max(xm)), c("#0404B4", "white", "#B18904")), show_row_names = F, km = 2, column_names_gp = gpar(fontsize = 7), name="Tumors", heatmap_legend_param = list(at = c(min(xm), 0, max(xm)))) pretty(seq(-3, 3, length = 3),n=4) #  -4 -2 0 2 4 pretty(seq(-3, 3, length = 3),n=5) # default n=5 #  -3 -2 -1 0 1 2 3  • One legend for a list of heatmaps #391, 2019 col_fun = colorRamp2(...) Heatmap(mat1, col = col_fun, ...) + Heatmap(mat2, col = col_fun, show_heatmap_legend = FALSE, ...) + Heatmap(mat3, col = col_fun, show_heatmap_legend = FALSE, ...) +  • Breaks in Color Scales are Wrong #659, 2020. col = colorRamp2(seq(-3, 3, length = 3), c("blue", "#EEEEEE", "red")) does not mean -3, 0, 3 should be the breaks on the legend (although you can manually control it). The color mapping function only defines the colors, while the default break values on the legends are calculated from the input matrix with 3 to 5 break values. In your code, you see 4 and -4 are the border of the legend, actually, all values between 3~4 are mapped to red and all the values between -3~-4 are mapped to blue. In other words, if I use colorRamp2(c(-3, 1, 3), c('blue', 'white', 'red')), it will uniformly distribute data in (-3,1) to c('blue', 'white') and (1,3) to c('white', 'red'). • Hex code #EEEEEE represents bright gray • Setting a default color schema #834, 2021 • Changing the default background color #698, 2021 ### cutoffs in circlize::colorRamp2() ## Row standardization/normalization ## Customize the heatmap body We can add numbers to each/certain cells. See 2.9 Customize the heatmap body ## Save images to files See png(file="newfile.png", width=8, height=6, units="in", res=300) ht <- Heatmap(...) draw(ht) dev.off()  ### svg and pdf For some reason, when I save the image to a file in svg or pdf format I will see borders of each cell. When I try use_raster = TRUE option, it seems to fix the problem on the body heatmap but the column annotation part still has borders. ## Extract orders and dendrograms See Section 2.12 ## Heatmap annotation 3 Heatmap Annotations. Using operators + and %v%' is easier so we can simplify the call to Heatmap(). Heatmap(...) + rowAnnotation() + ... # add to right Heatmap(...) %v% HeatmapAnnotation(...) %v% ... # add to bottom ha = HeatmapAnnotation(...) Heatmap(..., top_annotation = ha) ha = rowAnnotation(...) Heatmap(..., right_annotation = ha)  Example 1: top/bottom annotation and HeatmapAnnotation() library(ComplexHeatmap); library(circlize) set.seed(123) mat = matrix(rnorm(80, 2), 8, 10) rownames(mat) = paste0("R", 1:8) colnames(mat) = paste0("C", 1:10) col_anno = HeatmapAnnotation( df = data.frame(anno1 = 1:10, anno2 = sample(letters[1:3], 10, replace = TRUE)), col = list(anno2 = c("a" = "red", "b" = "blue", "c" = "green"))) Heatmap(mat, col = colorRamp2(c(-1, 0, 1), c("green", "white", "red")), top_annotation = col_anno, name = "mat", # legend for the color of the main heatmap column_title = "Heatmap") # top of the whole plot, default is ''  Example 2: left/right annotation and rowAnnotation() row_anno_df <- data.frame(anno1 = 1:8, anno2 = sample(letters[1:3], 8, replace = TRUE)) row_anno_col <- list(anno2 = c("a" = "red", "b" = "blue", "c" = "green")) row_anno <- rowAnnotation( df = row_anno_df, col = row_anno_col) Heatmap(mat, col = colorRamp2(c(-1, 0, 1), c("green", "white", "red")), , right_annotation = row_anno, name = "mat", row_title = "Heatmap") Heatmap(mat, col = colorRamp2(c(-1, 0, 1), c("green", "white", "red")), , name = "mat", row_title = "Heatmap") + row_anno # row labels disappear?  Example 3: use colorRamp2() to control colors on continuous variables in annotations # Same definition of row_anno_df row_anno_col <- list(anno1 = colorRamp2(c(min(row_anno_df$anno1), max(row_anno_df$anno1)), c("blue", "red")), anno2 = c("a" = "red", "b" = "blue", "c" = "green")) row_anno = rowAnnotation(df = row_anno_df, col = row_anno_col) Heatmap(mat, col = colorRamp2(c(-1, 0, 1), c("green", "white", "red")), right_annotation = row_anno, name = "mat", row_title = "Heatmap")  ## Correlation matrix The following code will guarantee the heatmap has a diagonal in the direction of top-left and bottom-right. The row dendrogram will be flipped automatically. There is no need to use cluster_rows = rev(mydend). mcor <- cor(t(lograt)) colnames(mcor) <- rownames(mcor) <- 1:ncol(mcor) mydend <- as.dendrogram(hclust(as.dist(1-mcor))) Heatmap( mcor, cluster_columns = mydend, cluster_rows = mydend, row_dend_reorder = FALSE, column_dend_reorder = FALSE, row_names_gp = gpar(fontsize = 6), column_names_gp = gpar(fontsize = 6), column_title = "", name = "value")  ## OncoPrint ## InteractiveComplexHeatmap ## tidyHeatmap tidyHeatmap. This is a tidy implementation for heatmap. At the moment it is based on the (great) package 'ComplexHeatmap'. Note: that ComplexHeatmap is on Bioconductor but tidyHeatmap is on CRAN. By default, .scale = "row". See ?heatmap. add_tile() to add a column or row (depending on the data) annotation. cluster_rows=FALSE if we don't want to cluster rows. BiocManager::install('tidyHeatmap') library(tidyHeatmap) library(tidyr) mtcars_tidy <- mtcars |> as_tibble(rownames="Car name") |> # Scale mutate_at(vars(-Car name, -hp, -vs), scale) |> # tidyfy pivot_longer(cols = -c(Car name, hp, vs), names_to = "Property", values_to = "Value") # create another variable which will be added next to 'hp' mtcars_tidy <- mtcars_tidy%>% mutate(type = Car name) mtcars_tidy$type <- substr(mtcars_tidy\$type, 1, 1)
mtcars_tidy

# NA case. Consider the cell on the top-right corner
mtcars_tidy %>% filter(Car name == 'Volvo 142E' & Property == 'am')
mtcars_tidy <- mtcars_tidy %>%                               # Replacing values
mutate(Value = replace(Value,
Car name == 'Volvo 142E' & Property == 'am',
NA))
mtcars_tidy %>% filter(Car name == 'Volvo 142E' & Property == 'am')
# Re-draw data with missing value
mtcars_tidy |>
heatmap(Car name, Property, Value,
palette_value = circlize::colorRamp2(
seq(-2, 2, length.out = 11),
rev(RColorBrewer::brewer.pal(11, "RdBu")))) |>

# two tiles on rows
mtcars_heatmap <-
mtcars_tidy |>
heatmap(Car name, Property, Value,
palette_value = circlize::colorRamp2(
seq(-2, 2, length.out = 11),
rev(RColorBrewer::brewer.pal(11, "RdBu")))) |>
mtcars_heatmap
# Other useful parameters
# heatmap(, cluster_rows = FALSE)
# heatmap(, .scale = F)

# Note add_tile(var) can decide whether the 'var' should go to
# columns or rows - interesting!
# one tile goes to columns and one tile goes to rows.
tidyHeatmap::pasilla |>
# group_by(location, type) |>
heatmap(
.column = sample,
.row = symbol,
.value = count normalised adjusted
) |>


# Correlation heatmap

## corrplot

This package is used for visualization of correlation matrix. See its vignette and Visualize correlation matrix using correlogram.

# pheatmap

What is special?

1. the color scheme is grey-blue to orange-red. pheatmap() default options, colorRampPalette in R
2. able to include class labels in samples and/or genes
3. the color key is thin and placed on the RHS (prettier for publications though it could miss information)
4. borders for each cell are included (not necessary)

(archived)

# heatmap3

CRAN. An improved heatmap package. Completely compatible with the original R function 'heatmap', and provides more powerful and convenient features.

# funkyheatmap

funkyheatmap - Generating Funky Heatmaps for Data Frames

# rasterImage

library(jpeg)

# need to create a plot first and rasterImage() will overlay it with another image
# bottom-left = (100, 300), top-right = (250, 450)
plot(c(100, 250), c(300, 450), type = "n", xlab = "", ylab = "")

args(rasterImage)
# function (image, xleft, ybottom, xright, ytop, angle = 0, interpolate = TRUE,  ...)
rasterImage(img, 100, 300, 250, 450)

text(100, 350, "ABCD", cex=2, col="yellow",adj=0) # left-bottom align


# Interactive heatmaps

## d3heatmap

This package has been removed from CRAN and archived on 2020-05-24.

A package generates interactive heatmaps using d3.js and htmlwidgets.

The package let you

• Highlight rows/columns by clicking axis labels
• Click and drag over colormap to zoom in (click on colormap to zoom out)
• Optional clustering and dendrograms, courtesy of base::heatmap

The following screenshots shows 3 features.

• Shows the row/column/value under the mouse cursor
• Zoom in a region (click on the zoom-in image will bring back the original heatmap)
• Highlight a row or a column (click the label of another row will highlight another row. Click the same label again will bring back the original image)

## heatmaply

This package extends the plotly engine to heatmaps, allowing you to inspect certain values of the data matrix by hovering the mouse over a cell. You can also zoom into a region of the heatmap by drawing a rectangle over an area of your choice.

Installing this package requires to compile some dependent package.

The return object is heatmaply is 'plotly' and 'htmlwidget'. It does not return the ordering of rows/columns. It can not control whether to do clustering (d3heatmap package is better at this).

## shinyHeatmaply

shinyHeatmaply: Deploy 'heatmaply' using 'shiny'

# Colors

## Fold change

data <- pms / pms[, "12.50"] # # fold change vs "12.50" sample
data <- ifelse(data>1, data, -1/data)
heatmap.2(data, ...)


## Generate sequential colors & grDevices::colorRampPalette()

?colorRampPalette. See an example Building heatmap with R.

mat <- matrix(1:100) # 100 x 1
image(t(mat), axes = FALSE, col = colorRampPalette( c("blue", "white", "red") )(100))

require(RColorBrewer)  # brewer.pal
ColorRamp <- colorRampPalette( brewer.pal(9, "Blues") )(25)
# The above will generate 25 colors by interpolating the colors
#   defined by brewer.pal(9, "Blues")
heatmap.2(..., col = ColorRamp)


## RColorBrewer::brewer.pal() function

• RColorBrewer can be used in both base plots and ggplot2.
• barplot(c(2,5,7), col = brewer.pal(n = 3, name = "RdBu"))
• ggplot() + geom_XXX + scale_fill_brewer(palette = "Dark2"). ggplot() + geom_point + + scale_color_brewer(palette = "Dark2")
• ColorBrewer contains 3 types of color palettes (The above page contains a list of all names):
• sequential,
• diverging,
• qualitative.
• display.brewer.pal() can display the colors on a graphical device.
• brewer.pal() will return colors (in hex) for a certain palette,
library(RColorBrewer)

display.brewer.all()                          # visualize colors
display.brewer.all(colorblindFriendly = TRUE) # draw a plot

display.brewer.pal(11, "BrBG") # Alternative colors used in correlation matrix

display.brewer.pal(9, "Set1") # Up to 9 classes are allowed

brewer.pal(n, name)
brewer.pal(n = 8, name = "Dark2")  # Qualitative palettes
##  "#1B9E77" "#D95F02" "#7570B3" "#E7298A" "#66A61E" "#E6AB02" "#A6761D"
##  "#666666"
brewer.pal(n = 8, name = "Greens") # Sequential palettes

brewer.pal(n = 8, name = "RdYlBu") # Diverging palettes


### RdYlBu

It means Red-Yellow-Blue. See a full list of names and colors for RColorBrewer palettes Colors in R.

See some examples (R base heatmap, d3heatmap) Heatmap in R: Static and Interactive Visualization using RdYlBu color.

library(RColorBrewer)
display.brewer.pal(5, "RdYlBu")
brewer.pal(n=5, name = "RdYlBu")
#  "#D7191C" "#FDAE61" "#FFFFBF" "#ABD9E9" "#2C7BB6"

# If we set n=3, the colors won't be right
display.brewer.pal(3, "RdYlBu")
brewer.pal(n=3, name = "RdYlBu")
#  "#FC8D59" "#FFFFBF" "#91BFDB"


pheatmap seems to use a palette very close to RdYlBu. To specify the palette explicitly, see R语言: 从pheatmap无缝迁移至ComplexHeatmap.

Note ComplexHeatmap requires the color to be a function instead of color palette.

library(ComplexHeatmap)
df <- scale(mtcars)
range(df)
#  -1.874010  3.211677  # NOT symmetric

col_fun <- circlize::colorRamp2(quantile(df, c(0, .25, .5, .75, 1)),
rev(RColorBrewer::brewer.pal(n=5, name = "RdYlBu")))

# Treat the data as symmetric
col_fun <- circlize::colorRamp2(c(-2, qnorm(c(.25, .5, .75)), 2),
rev(RColorBrewer::brewer.pal(n=5, name = "RdYlBu")))
Heatmap(df,
col = col_fun,
name = "mtcars", #title of legend
column_title = "Variables", row_title = "Samples",
row_names_gp = gpar(fontsize = 7) # Text size for row names
)


## scales package

• https://scales.r-lib.org/. Scales colour palettes are used to power the scales in ggplot2, but you can use them in any plotting system include base R plots.
show_col(hue_pal()(4))      # visualize colors
show_col(viridis_pal()(16)) # visualize colors

viridis_pal()(4)
#>  "#440154FF" "#31688EFF" "#35B779FF" "#FDE725FF"

# use in combination with baseR palette() to set new defaults
palette(brewer_pal(palette = "Set2")(4))

• Emulate ggplot2 default color palette

## Missing data

In the dynamic heatmap tool of BRB-ArrayTools, the missing data is represented by the gray color.

plot(c(100, 200), c(300, 450), type= "n", xlab = "", ylab = "")
rect(110, 300, 175, 350, col = "navy")
rect(110, 360, 175, 400, col = "powderblue")
rect(110, 410, 175, 450, col = "#4B9CD3")


# Papers

## Double dipping 