title: “gapmap” author: “Ryo Sakai” date: “2 September 2014”

output: html_document

This document explains basic functions of the gapmap package to draw a gapped cluster heatmap. The plot is generated using the ggplot2 package. Let's load the library first.


We will simulate a simple dataset.

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 distance matrix. default is Euclidean distance
distxy <- dist(dataFrame)
#perform hierarchical clustering. default is complete linkage.
hc <- hclust(distxy)
dend <- as.dendrogram(hc)

To make a gapped cluster heatmap, you need to pass a matrix object for heatmap, and dendrogram class objects for drawing dendrograms and ordering.

grey_scale =c("#333333", "#5C5C5C", "#757575", "#8A8A8A", "#9B9B9B", "#AAAAAA", "#B8B8B8", "#C5C5C5", "#D0D0D0", "#DBDBDB", "#E6E6E6")
gapmap(m = as.matrix(distxy), d_row= rev(dend), d_col=dend, col = grey_scale)

plot of chunk unnamed-chunk-3

The default of gapmap function is in the quantitative mode and uses exponential mapping. First, you can choose either modes: quantitative or threshold.

The following example uses the linear mapping. This mapping generate more gaps, whereas the previous example of exponential mapping emphasize on the large gaps.

gapmap(m = as.matrix(distxy), d_row= rev(dend), d_col=dend,  mode = "quantitative", mapping="linear", col = grey_scale)

plot of chunk unnamed-chunk-4

The following example illustrate the difference of two mapping schemes. For the exponential mapping, the scale log base is set to 0.5. plot of chunk unnamed-chunk-5

The variation of scale log base settings is illustrated in the following plot. The value of scale is annotated on the plot. plot of chunk unnamed-chunk-6

Besides the quantitative mode, there is linear mode to introduce gap by a threshold. In the following example, the dendrograms for rows and columns are cut at the threshold distance of 2 and gaps of the same size are introduced between clusters.

gapmap(m = as.matrix(distxy), d_row= rev(dend), d_col=dend,  mode = "threshold", row_threshold = 2, col_threshold = 2, col = grey_scale)

plot of chunk unnamed-chunk-7

In addition, this package works well with our dendrogram sorting package, called dendsort. For the details on dendsort, please check our paper.

gapmap(m = as.matrix(distxy), d_row= rev(dendsort(dend)), d_col=dendsort(dend),  mode = "quantitative", col = grey_scale)

plot of chunk unnamed-chunk-8