FindOutliers: Inference of outlier cells

In DIscBIO: A User-Friendly Pipeline for Biomarker Discovery in Single-Cell Transcriptomics

Inference of outlier cells

Description

This functions performs the outlier identification for k-means and model-based clustering

Usage

FindOutliers(
  object,
  K,
  outminc = 5,
  outlg = 2,
  probthr = 0.001,
  thr = 2^-(1:40),
  outdistquant = 0.75,
  plot = TRUE,
  quiet = FALSE
)

## S4 method for signature 'DISCBIO'
FindOutliers(
  object,
  K,
  outminc = 5,
  outlg = 2,
  probthr = 0.001,
  thr = 2^-(1:40),
  outdistquant = 0.75,
  plot = TRUE,
  quiet = FALSE
)

Arguments

object	DISCBIO class object.
K	Number of clusters to be used.
outminc	minimal transcript count of a gene in a clusters to be tested for being an outlier gene. Default is 5.
outlg	Minimum number of outlier genes required for being an outlier cell. Default is 2.
probthr	outlier probability threshold for a minimum of 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.
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
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.
plot	if 'TRUE', produces a plot of -log10prob per K
quiet	if 'TRUE', intermediary output is suppressed

Value

A named vector of the genes containing outlying cells and the number of cells on each.

Examples

sc <- DISCBIO(valuesG1msTest)
sc <- Clustexp(sc, cln = 2) # K-means clustering
FindOutliers(sc, K = 2)

DIscBIO documentation built on Nov. 6, 2023, 5:08 p.m.