% Generated by roxygen2: do not edit by hand

% Please edit documentation in R/DIscBIO-generic-ClassVectoringDT.R

\name{ClassVectoringDT}

\alias{ClassVectoringDT}

\alias{ClassVectoringDT,DISCBIO-method}

\title{Generating a class vector to be used for the decision tree analysis.}

\usage{

ClassVectoringDT(

  object,

  Clustering = "K-means",

  K,

  First = "CL1",

  Second = "CL2",

  sigDEG,

  quiet = FALSE

)

\S4method{ClassVectoringDT}{DISCBIO}(

  object,

  Clustering = "K-means",

  K,

  First = "CL1",

  Second = "CL2",

  sigDEG,

  quiet = FALSE

)

}

\arguments{

\item{object}{\code{DISCBIO} class object.}

\item{Clustering}{Clustering has to be one of the following: ["K-means",

"MB"]. Default is "K-means"}

\item{K}{A numeric value of the number of clusters.}

\item{First}{A string vector showing the first target cluster.  Default is

"CL1"}

\item{Second}{A string vector showing the second target cluster.  Default is

"CL2"}

\item{sigDEG}{A data frame of the differentially expressed genes (DEGs)

generated by running "DEGanalysis()" or "DEGanalysisM()".}

\item{quiet}{If `TRUE`, suppresses intermediary output}

}

\value{

A data frame.

}

\description{

This function generates a class vector for the input dataset so

  the decision tree analysis can be implemented afterwards.

}