|
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 |
} |