Modular Leaf Ordering Methods for Dendrogram Nodes

Description

Modular Leaf Ordering Methods for Dendrogram Nodes

Details

This package includes functions to optimize ordering of nodes in a dendrogram, without affecting the meaning of the dendrogram. A dendrogram can be sorted based on the average distance of subtrees, or based on the smallest distance value. These sorting methods improve readability and interpretability of tree structure, especially for tasks such as comparison of different distance measures or linkage types and identification of tight clusters and outliers. As a result, it also introduces more meaningful reordering for a coupled heatmap visualization.

Author(s)

Ryo Sakai ryo.sakai@esat.kuleuven.be

Recursive function to calculate the length of branches

Description

cal_length is a code modified from plotNode() to calculate the length of lines to draw the branch of a dendrogram. This function was developed to evaluate the use of ink for visualization.

Usage

cal_length(x1, x2, subtree, center, nodePar, edgePar, horiz = FALSE, sum)

Arguments

Value

output The length.

Examples

#generate sample data
set.seed(1234); par(mar=c(0,0,0,0))
x <- rnorm(10, mean=rep(1:5, each=2), sd=0.4)
y <- rnorm(10, mean=rep(c(1,2), each=5), sd=0.4)
dataFrame <- data.frame(x=x, y=y, row.names=c(1:10))
#calculate Euclidian distance
distxy <- dist(dataFrame)
#hierachical clustering "complete" linkage by default
hc <- hclust(distxy)

total_length <- cal_total_length(as.dendrogram(hc))

Calculate the x coordinates given a branch of dendrogram

Description

cal_node_limit is a code modified from plotNodeLimit() to x coordinates of branches given a branch of dendrogram.

Usage

cal_node_limit(x1, x2, subtree, center)

Arguments

Value

output A list of parameters.

Examples

#generate sample data
set.seed(1234); par(mar=c(0,0,0,0))
x <- rnorm(10, mean=rep(1:5, each=2), sd=0.4)
y <- rnorm(10, mean=rep(c(1,2), each=5), sd=0.4)
dataFrame <- data.frame(x=x, y=y, row.names=c(1:10))
#calculate Euclidian distance
distxy <- dist(dataFrame)
#hierachical clustering "complete" linkage by default
hc <- hclust(distxy)

total <- cal_total_length(as.dendrogram(hc))

Calculate the total length of lines to draw the dendrogram

Description

cal_total_length is a code modified from plot.dendrogram() to calculate the total length of lines to draw a dendrogram. This function was developed to evaluate the use of ink for visualization.

Usage

cal_total_length(x)

Arguments

Value

output The total length.

Examples

#generate sample data
set.seed(1234); par(mar=c(0,0,0,0))
x <- rnorm(10, mean=rep(1:5, each=2), sd=0.4)
y <- rnorm(10, mean=rep(c(1,2), each=5), sd=0.4)
dataFrame <- data.frame(x=x, y=y, row.names=c(1:10))
#calculate Euclidian distance
distxy <- dist(dataFrame)
#hierachical clustering "complete" linkage by default
hc <- hclust(distxy)

total_length <- cal_total_length(as.dendrogram(hc))

Sorting and reordering dendrogram nodes

Description

dendsort sorts a dendrogram object which is typically a result of hierarchical clustering (hclust). The subtrees in the resulting dendrogram are sorted based on the average distance of subtrees at every merging point. The tighter cluster, in other words the cluster with smaller average distance, is placed on the left side of branch. When a leaf merge with a cluster, the leaf is placed on the right side.

Usage

dendsort(d, isReverse = FALSE, type = "min")

Arguments

Value

output A sorted dendrogram or hclust.

Examples

#generate sample data
set.seed(1234); par(mar=c(0,0,0,0))
x <- rnorm(10, mean=rep(1:5, each=2), sd=0.4)
y <- rnorm(10, mean=rep(c(1,2), each=5), sd=0.4)
dataFrame <- data.frame(x=x, y=y, row.names=c(1:10))
#calculate Euclidian distance
distxy <- dist(dataFrame)
#hierachical clustering "complete" linkage by default
hc <- hclust(distxy)

#sort dendrogram
dd <- dendsort(as.dendrogram(hc))
hc_sorted  <- as.hclust(dd)

#sort in reverse, you can also pass hclust object
plot(dendsort(hc, isReverse=TRUE))

#sort by average distance
plot(dendsort(hc, type="average"))

#plot the result
par(mfrow = c(1, 3), mai=c(0.8,0.8,2,0.8))
plot(x, y, col="gray", pch=19, cex=2)
text(x, y, labels=as.character(1:10), cex=0.9)
plot(hc,main="before sorting", xlab="", sub="")
plot(hc_sorted, main="after sorting", xlab="", sub="")

Sample data matrix from the integrated pathway analysis of gastric cancer from the Cancer Genome Atlas (TCGA) study

Description

a multivariate table obtained from the integrated pathway analysis of gastric cancer from the Cancer Genome Atlas (TCGA) study. In this data set, each column represents a pathway consisting of a set of genes and each row represents a cohort of samples based on specific clinical or genetic features. For each pair of a pathway and a feature, a continuous value of between 1 and -1 is assigned to score positive or negative association, respectively.

Usage

data(sample_tcga)

Format

A data frame with 215 rows and 117 variables

Details

We would like to thank Sheila Reynolds and Vesteinn Thorsson from the Institute for Systems Biology for sharing this sample data set.

Sorting and reordering dendrogram nodes by average distances

Description

sort_average sorts a dendrogram object based on the average distance of its subtrees, recursively. The tighter cluster, in other words the cluster with smaller average distance, is placed on the left side of branch. When a leaf merge with a cluster, the leaf is placed on the right side.

Usage

sort_average(d)

Arguments

Value

output A sorted dendrogram object.

Examples

#generate sample data
set.seed(1234); par(mar=c(0,0,0,0))
x <- rnorm(10, mean=rep(1:5, each=2), sd=0.4)
y <- rnorm(10, mean=rep(c(1,2), each=5), sd=0.4)
dataFrame <- data.frame(x=x, y=y, row.names=c(1:10))
#calculate Euclidian distance
distxy <- dist(dataFrame)
#hierachical clustering "complete" linkage by default
hc <- hclust(distxy)

#sort dendrogram
dd <- dendsort(as.dendrogram(hc))
hc_sorted  <- as.hclust(dd)

#sort in reverse, you can also pass hclust object
plot(dendsort(hc, isReverse=TRUE))

#sort by average distance
plot(dendsort(hc, type="average"))

#plot the result
par(mfrow = c(1, 3), mai=c(0.8,0.8,2,0.8))
plot(x, y, col="gray", pch=19, cex=2)
text(x, y, labels=as.character(1:10), cex=0.9)
plot(hc,main="before sorting", xlab="", sub="")
plot(hc_sorted, main="after sorting", xlab="", sub="")

Sorting and reordering dendrogram nodes by average distances in reverse

Description

sort_average_r sorts a dendrogram object in reverse based on the average distance of its subtrees, recursively. The tighter cluster, in other words the cluster with smaller average distance, is placed on the right side of branch. When a leaf merge with a cluster, the leaf is placed on the left side.

Usage

sort_average_r(d)

Arguments

Value

output A sorted dendrogram object.

Examples

#generate sample data
set.seed(1234); par(mar=c(0,0,0,0))
x <- rnorm(10, mean=rep(1:5, each=2), sd=0.4)
y <- rnorm(10, mean=rep(c(1,2), each=5), sd=0.4)
dataFrame <- data.frame(x=x, y=y, row.names=c(1:10))
#calculate Euclidian distance
distxy <- dist(dataFrame)
#hierachical clustering "complete" linkage by default
hc <- hclust(distxy)

#sort dendrogram
dd <- dendsort(as.dendrogram(hc))
hc_sorted  <- as.hclust(dd)

#sort in reverse, you can also pass hclust object
plot(dendsort(hc, isReverse=TRUE))

#sort by average distance
plot(dendsort(hc, type="average"))

#plot the result
par(mfrow = c(1, 3), mai=c(0.8,0.8,2,0.8))
plot(x, y, col="gray", pch=19, cex=2)
text(x, y, labels=as.character(1:10), cex=0.9)
plot(hc,main="before sorting", xlab="", sub="")
plot(hc_sorted, main="after sorting", xlab="", sub="")

Sorting and reordering dendrogram nodes by the smallest value

Description

sort_smallest sorts a dendrogram object based on the smallest distance in its subtrees, recursively. The cluster with the smallest distance is placed on the left side of branch.When a leaf merge with a cluster, the leaf is placed on the right side.

Usage

sort_smallest(d)

Arguments

Value

output A sorted dendrogram object.

Examples

#generate sample data
set.seed(1234); par(mar=c(0,0,0,0))
x <- rnorm(10, mean=rep(1:5, each=2), sd=0.4)
y <- rnorm(10, mean=rep(c(1,2), each=5), sd=0.4)
dataFrame <- data.frame(x=x, y=y, row.names=c(1:10))
#calculate Euclidian distance
distxy <- dist(dataFrame)
#hierachical clustering "complete" linkage by default
hc <- hclust(distxy)

#sort dendrogram
dd <- dendsort(as.dendrogram(hc))
hc_sorted  <- as.hclust(dd)

#sort in reverse, you can also pass hclust object
plot(dendsort(hc, isReverse=TRUE))

#sort by average distance
plot(dendsort(hc, type="average"))

#plot the result
par(mfrow = c(1, 3), mai=c(0.8,0.8,2,0.8))
plot(x, y, col="gray", pch=19, cex=2)
text(x, y, labels=as.character(1:10), cex=0.9)
plot(hc,main="before sorting", xlab="", sub="")
plot(hc_sorted, main="after sorting", xlab="", sub="")

Sorting and reordering dendrogram nodes by the smallest value in reverse

Description

sort_smallest_r sorts a dendrogram object in reverse based on the smallest distance in its subtrees, recursively. The cluster with the smallest distance is placed on the right side of branch.When a leaf merge with a cluster, the leaf is placed on the left side.

Usage

sort_smallest_r(d)

Arguments

Value

output A sorted dendrogram object.

Examples

#generate sample data
set.seed(1234); par(mar=c(0,0,0,0))
x <- rnorm(10, mean=rep(1:5, each=2), sd=0.4)
y <- rnorm(10, mean=rep(c(1,2), each=5), sd=0.4)
dataFrame <- data.frame(x=x, y=y, row.names=c(1:10))
#calculate Euclidian distance
distxy <- dist(dataFrame)
#hierachical clustering "complete" linkage by default
hc <- hclust(distxy)

#sort dendrogram
dd <- dendsort(as.dendrogram(hc))
hc_sorted  <- as.hclust(dd)

#sort in reverse, you can also pass hclust object
plot(dendsort(hc, isReverse=TRUE))

#sort by average distance
plot(dendsort(hc, type="average"))

#plot the result
par(mfrow = c(1, 3), mai=c(0.8,0.8,2,0.8))
plot(x, y, col="gray", pch=19, cex=2)
text(x, y, labels=as.character(1:10), cex=0.9)
plot(hc,main="before sorting", xlab="", sub="")
plot(hc_sorted, main="after sorting", xlab="", sub="")