plot.ORdensity: plot.ORdensity

In jmartinezot/findDE: Differentially Expressed Genes Discovery using ORdensity Method

Description

This function plots the silhouette values of successive clusterings.

Usage

plot.ORdensity(object, k = object@bestK, ...)

Arguments

object	An object of ORdensity class
K	Integer value to indicate the maximum number of partitions to check along the successive clusterings. By default K=10.

Details

Once the potential DE genes are identified, the real DE genes and the not real DE genes or false positives must be distinguished and it is carried out clustering all the potential DE genes (see findDEgenes). The clustering algorithm is pam and by default the number of clusters in the partition is obtained by silhouette. Nevertheless, silhouetteAnalysis allows to check whether there is a clear best number for the partition or the average silhouettes values are rather similar. #' @references Rousseeuw, P. J. (1987). Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of computational and applied mathematics, 20, 53-65.

Value

Displays a plot of the average silhouette width along with the number of clusters in the partition. If assigned to an object, it returns a vector of length K with the average silhouette values.

Examples

# Read data from 2 experimental conditions
x <- simexpr[, 3:32]
y <- simexpr[, 33:62]
EXC.1 <- as.matrix(x)
EXC.2 <- as.matrix(y)
myORdensity <- new("ORdensity", Exp_cond_1 = EXC.1, Exp_cond_2 = EXC.2)
silhouetteAnalysis(myORdensity)

jmartinezot/findDE documentation built on May 24, 2019, 8:54 a.m.