% Generated by roxygen2: do not edit by hand

% Please edit documentation in R/DIscBIO-classes.R

\docType{class}

\name{DISCBIO}

\alias{DISCBIO}

\alias{DISCBIO-class,}

\alias{DISCBIO-class}

\title{The DISCBIO Class}

\arguments{

\item{object}{An DISCBIO object.}

}

\description{

The DISCBIO class is the central object storing all information

  generated throughout the pipeline.

}

\details{

DISCBIO

}

\section{Slots}{

\describe{

\item{\code{SingleCellExperiment}}{Representation of the single cell input data,

including both cells from regular and ERCC spike-in samples. Data are

stored in a SingleCellExperiment object.}

\item{\code{expdata}}{The raw expression data matrix with cells as columns and

genes as rows in sparse matrix format. It does not contain ERCC spike-ins.}

\item{\code{expdataAll}}{The raw expression data matrix with cells as columns

and genes as rows in sparse matrix format. It can contain ERCC spike-ins.}

\item{\code{ndata}}{Data with expression normalized to one for each cell.}

\item{\code{fdata}}{Filtered data with expression normalized to one for each

cell.}

\item{\code{distances}}{A distance matrix.}

\item{\code{tsne}}{A data.frame with coordinates of two-dimensional tsne layout

for the K-means clustering.}

\item{\code{background}}{A list storing the polynomial fit for the background

model of gene expression variability. It is used for outlier

identification.}

\item{\code{out}}{A list storing information on outlier cells used for the

prediction of rare cell types.}

\item{\code{cpart}}{A vector containing the final clustering partition computed

by K-means.}

\item{\code{fcol}}{A vector contaning the colour scheme for the clusters.}

\item{\code{filterpar}}{A list containing the parameters used for cell and gene

filtering based on expression.}

\item{\code{clusterpar}}{A list containing the parameters used for the K-means

clustering.}

\item{\code{outlierpar}}{A list containing the parameters used for outlier

identification.}

\item{\code{kmeans}}{A list containing the results of running the Clustexp()

function.}

\item{\code{MBclusters}}{A vector containing the final clustering partition

computed by Model-based clustering.}

\item{\code{kordering}}{A vector containing the Pseudo-time ordering based on

k-means clusters.}

\item{\code{MBordering}}{A vector containing the Pseudo-time ordering based on

Model-based clusters.}

\item{\code{MBtsne}}{A data.frame with coordinates of two-dimensional tsne

layout for the Model-based clustering.}

\item{\code{noiseF}}{A vector containing the gene list resulted from running the

noise filtering.}

\item{\code{FinalGeneList}}{A vector containing the final gene list resulted

from running the noise filtering or/and the expression filtering.}

}}

\examples{

class(valuesG1msTest)

G1_reclassified <- DISCBIO(valuesG1msTest)

class(G1_reclassified)

str(G1_reclassified, max.level = 2)

identical(G1_reclassified@expdataAll, valuesG1msTest)

}