--- a
+++ b/man/FindOutliers.Rd
@@ -0,0 +1,73 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/DIscBIO-generic-FindOutliers.R
+\name{FindOutliers}
+\alias{FindOutliers}
+\alias{FindOutliers,DISCBIO-method}
+\title{Inference of outlier cells}
+\usage{
+FindOutliers(
+  object,
+  K,
+  outminc = 5,
+  outlg = 2,
+  probthr = 0.001,
+  thr = 2^-(1:40),
+  outdistquant = 0.75,
+  plot = TRUE,
+  quiet = FALSE
+)
+
+\S4method{FindOutliers}{DISCBIO}(
+  object,
+  K,
+  outminc = 5,
+  outlg = 2,
+  probthr = 0.001,
+  thr = 2^-(1:40),
+  outdistquant = 0.75,
+  plot = TRUE,
+  quiet = FALSE
+)
+}
+\arguments{
+\item{object}{\code{DISCBIO} class object.}
+
+\item{K}{Number of clusters to be used.}
+
+\item{outminc}{minimal transcript count of a gene in a clusters to be tested
+for being an outlier gene. Default is 5.}
+
+\item{outlg}{Minimum number of outlier genes required for being an outlier
+cell. Default is 2.}
+
+\item{probthr}{outlier probability threshold for a minimum of \code{outlg}
+genes to be an outlier cell. This probability is computed from a negative
+binomial background model of expression in a cluster. Default is 0.001.}
+
+\item{thr}{probability values for which the number of outliers is computed in
+order to plot the dependence of the number of outliers on the probability
+threshold. Default is 2**-(1:40).set}
+
+\item{outdistquant}{Real number between zero and one. Outlier cells are
+merged to outlier clusters if their distance smaller than the
+outdistquant-quantile of the distance distribution of  pairs of cells in
+the orginal clusters after outlier removal. Default is 0.75.}
+
+\item{plot}{if `TRUE`, produces a plot of -log10prob per K}
+
+\item{quiet}{if `TRUE`, intermediary output is suppressed}
+}
+\value{
+A named vector of the genes containing outlying cells and the number
+  of cells on each.
+}
+\description{
+This functions performs the outlier identification for k-means
+and model-based clustering
+}
+\examples{
+sc <- DISCBIO(valuesG1msTest)
+sc <- Clustexp(sc, cln = 2) # K-means clustering
+FindOutliers(sc, K = 2)
+
+}