gausianMixture: Gaussian Mixture Model for CNA clustering

In FeifeiXiaoUSC/FLCNA: Simultaneous CNV Detection And Subclone Clustering With Single Cell Sequencing Data

Gaussian Mixture Model for CNA clustering

Description

Gaussian Mixture Model is applied to assign each segment to the most likely cluster/state.

Usage

gausianMixture(x, cp, priors, L, st)

Arguments

x	The vector of the estimated mean of markers.
cp	The vector of the marker index of the identified change-points.
priors	Given initial parameters for the EM algorithm.
L	Repeat times in the EM algorithm. Defaults to 100.
st	Number of assumed states in the EM algorithm.

Value

The return is the clustered CNA segments with the start position and end position using CNA marker index, and the copy number states. It also returns a vector of final candidates of change-points.

p.final	Probability of falling into each state for each CNA segment after convergence.
mu.final	Segment means of each state after convergence.
cp.final	List of change-points after EM algorithm.
index.final	The index of change-points.
state.new	Assigned copy number state for each CNA.

FeifeiXiaoUSC/FLCNA documentation built on March 29, 2025, 10:48 p.m.