NeatMap (version 0.1)

heatmap1: Make a non-clustered heatmap

Description

Makes a heatmap without need for cluster analysis

Usage

heatmap1(profiles, row.order = NULL, column.order = NULL, row.cluster = NULL,
column.cluster = NULL, column.labels = NULL, row.labels = NULL, 
column.label.size = 3, row.label.size = 3,row.normalize=F)
make.heatmap1(profiles, row.method = "nMDS", column.method = "none",
row.metric = "pearson", column.metric = "pearson", row.cluster.method = "average",
column.cluster.method = "average", column.labels = NULL, row.labels = NULL,
row.label.size = 3, column.label.size = 3,row.normalize=F)

Arguments

profiles

matrix: containing the data to be plotted.

row.order

vector containing order of rows such as produced by order

column.order

vector containing order of columns such as produced by order

row.cluster

hierarchical clustering result for rows of type hclust

column.cluster

hierarchical clustering result for columns of type hclust

column.labels

vector of labels for columns

row.labels

vector of labels for rows

column.label.size

size for column label text

row.label.size

size for row label text

row.normalize

logical: If true the rows are normalized to zero mean and unit variance

row.method

dimension reduction method used by make.heatmap1 to order rows. One of "none","nMDS","PCA","complete.linkage" and "average.linkage".

column.method

dimension reduction method used by make.heatmap1 to order columns. One of "none","nMDS","PCA","complete.linkage" and "average.linkage".

row.metric

Distance metric used by row.method either "pearson" or "euclidean"

column.metric

Distance metric used by column.method either "pearson" or "euclidean"

row.cluster.method

Clustering algorithm used for clustering rows. Either "average.linkage" or "complete.linkage". If NULL, no row cluster results are shown

column.cluster.method

Clustering algorithm used for clustering columns. Either "average.linkage" or "complete.linkage". If NULL, no column cluster results are shown

Value

• A ggplot2 plot of class ggplot.

Details

The traditional heatmap uses clustering to order rows and columns. These functions allow us to use alternate schemes for this ordering. They use the same format as the traditional heatmap, and are therefore similar to image and heatmap. heatmap1 assumes the user has already ordered the rows and columns according to the scheme of their choice. make.heatmap1 is a convenience wrapper which performs ordering using "nMDS", "PCA", or hierarchical clustering. This ordering is the passed on to heatmap1 for plotting. If "nMDS" or "PCA" are chosen as the ordering method, then it is assumed that their 2D embedding is annular in shape. This is often observed if PCA or nMDS (with euclidean distance) is applied to normalized data or Pearson correlation is used with nMDS. Angles measured at the centre of mass are then used for ordering. The user is therefore advised to confirm that such an annular structure is indeed present. Note that the two opposite ends of the ordering are typically separated by 360 degrees and are therefore very similar. To avoid artifacts produced by this, the user could consider using circularmap instead. The metric specified by row.metric and column.metric are also used by the clustering algorithms.

See Also

image,heatmap,circularmap.

Examples

make.heatmap1(mtcars,row.method="PCA",column.method="average.linkage")

#is equivalent to
mtcars.PCA<-prcomp(mtcars)
mtcars.PCA.order<-order(apply(mtcars.PCA$x[,1:2],1,function(x){atan2(x[1],x[2])}))
mtcars.column.cluster<-hclust(as.dist(1-cor(mtcars)),method="average")
mtcars.row.cluster<-hclust(as.dist(1-cor(t(mtcars))),method="average")
heatmap1(mtcars,row.order=mtcars.PCA.order,column.order=mtcars.column.cluster$order,
row.cluster=mtcars.row.cluster,column.cluster=mtcars.column.cluster)

#Changing Color Scheme
make.heatmap1(mtcars,row.method="PCA",column.method="average.linkage")+
scale_fill_gradient2(low="yellow",high="blue",mid="black",midpoint=200)

#Adding labels (the scale function ensures that labels are not clipped)
make.heatmap1(mtcars,row.method="PCA",column.method="average.linkage",
row.labels=rownames(mtcars),column.labels=colnames(mtcars))+
scale_x_continuous(lim=c(-1,15))