Hierarchical Clustering Dendrogram (hclustplot)

This function computes the sample-wise correlation coefficients using the stats::cor() function from the transformed expression values. After transformation to a distance matrix, hierarchical clustering is performed with the stats::hclust() function, and the result is plotted as a dendrogram.

hclustplot(
  exploredds,
  method = "spearman",
  plotly = FALSE,
  savePlot = FALSE,
  filePlot = NULL
)

Arguments

exploredds	object of class DESeq2::DESeqTransform().
method	a character string indicating which correlation coefficient is to be computed, based on the stats::cor() function. Options are: c("pearson" "kendall", "spearman").
plotly	logical: when FALSE (default), the ggplot2 plot will be returned. TRUE option returns the plotly version of the plot.
savePlot	logical: when FALSE (default), the plot will not be saved. If TRUE the plot will be saved, and requires the filePlot argument.
filePlot	file name where the plot will be saved. For more information, please consult the ggplot2::ggsave() function.

Value

returns an object of ggplot or plotly class.

Examples

## Targets file
targetspath <- system.file("extdata", "targets.txt", 
package = "systemPipeR")
targets <- read.delim(targetspath, comment = "#")
cmp <- systemPipeR::readComp(file = targetspath, 
format = "matrix", delim = "-")
## Count table file
countMatrixPath <- system.file("extdata", "countDFeByg.xls",
 package = "systemPipeR")
countMatrix <- read.delim(countMatrixPath, row.names = 1)
## Plot
exploredds <- exploreDDS(countMatrix, targets,
    cmp = cmp[[1]],
    preFilter = NULL, transformationMethod = "rlog"
)
hclustplot(exploredds, method = "spearman")