Diff of /man/FindOutliers.Rd [000000] .. [28e211]

Switch to unified view

a b/man/FindOutliers.Rd
1
% Generated by roxygen2: do not edit by hand
2
% Please edit documentation in R/DIscBIO-generic-FindOutliers.R
3
\name{FindOutliers}
4
\alias{FindOutliers}
5
\alias{FindOutliers,DISCBIO-method}
6
\title{Inference of outlier cells}
7
\usage{
8
FindOutliers(
9
  object,
10
  K,
11
  outminc = 5,
12
  outlg = 2,
13
  probthr = 0.001,
14
  thr = 2^-(1:40),
15
  outdistquant = 0.75,
16
  plot = TRUE,
17
  quiet = FALSE
18
)
19
20
\S4method{FindOutliers}{DISCBIO}(
21
  object,
22
  K,
23
  outminc = 5,
24
  outlg = 2,
25
  probthr = 0.001,
26
  thr = 2^-(1:40),
27
  outdistquant = 0.75,
28
  plot = TRUE,
29
  quiet = FALSE
30
)
31
}
32
\arguments{
33
\item{object}{\code{DISCBIO} class object.}
34
35
\item{K}{Number of clusters to be used.}
36
37
\item{outminc}{minimal transcript count of a gene in a clusters to be tested
38
for being an outlier gene. Default is 5.}
39
40
\item{outlg}{Minimum number of outlier genes required for being an outlier
41
cell. Default is 2.}
42
43
\item{probthr}{outlier probability threshold for a minimum of \code{outlg}
44
genes to be an outlier cell. This probability is computed from a negative
45
binomial background model of expression in a cluster. Default is 0.001.}
46
47
\item{thr}{probability values for which the number of outliers is computed in
48
order to plot the dependence of the number of outliers on the probability
49
threshold. Default is 2**-(1:40).set}
50
51
\item{outdistquant}{Real number between zero and one. Outlier cells are
52
merged to outlier clusters if their distance smaller than the
53
outdistquant-quantile of the distance distribution of  pairs of cells in
54
the orginal clusters after outlier removal. Default is 0.75.}
55
56
\item{plot}{if `TRUE`, produces a plot of -log10prob per K}
57
58
\item{quiet}{if `TRUE`, intermediary output is suppressed}
59
}
60
\value{
61
A named vector of the genes containing outlying cells and the number
62
  of cells on each.
63
}
64
\description{
65
This functions performs the outlier identification for k-means
66
and model-based clustering
67
}
68
\examples{
69
sc <- DISCBIO(valuesG1msTest)
70
sc <- Clustexp(sc, cln = 2) # K-means clustering
71
FindOutliers(sc, K = 2)
72
73
}