optimTree.plot {compdiagTools} | R Documentation |
Function is based on original clusterplot
code by Mark Wilkinson. For a given expression matrix, hierarchical clustering with average linkage is performed on Euclidean distances. The samples (colums) are rearranged using optimTree
.
optimTree.plot(x, optimize = TRUE, row.scale = TRUE, zlim = 2, max.samp = 15, barplot = FALSE, col = "color1")
x |
Matrix of expression values with genes in rows and samples in columns OR an exprSet . |
optimize |
Logical value. If TRUE , optimTree is used in succession with hclust . |
row.scale |
Logical value. If TRUE , genes are scaled before plotting to derive nicer colors. |
zlim |
Limit of plotted values. All values exceeding +-zlim are encoded by the brightest colors. Maybe increase the level if row.scale=FALSE . |
max.samp |
Maximal sample size. If there are more samples, no optimization is done. Note that computation time increases with sample size. |
barplot |
Logical value. If TRUE , the encoding color bar is plotted instead of a clusterplot. |
col |
Color schemes color1 to color3 . |
Graphical output.
Stefanie Scheid <stefanie.scheid@molgen.mpg.de>, clusterplot
code by Mark Wilkinson
optimTree
,optimTree.dendrogram
a <- matrix(rnorm(1000),ncol=10) optimTree.plot(a) optimTree.plot(a,barplot=TRUE)