CNA.out.eachcell: CNA output for each cell

In FeiQin92/FLCNA: Simultaneous CNV Detection And Subclone Clustering With Single Cell Sequencing Data

CNA output for each cell

Description

This function clusters the identified change-points to make final CNA calling for each cell. The potential CNA segments between two neighbor candidate change-points are assigned to different copy number states according to the estimated mean matrix from FLCNA R function and log2R data for each cell. We use three clusters including duplication, normal state and deletion. A Gaussisan Mixture Model based clustering strategy was applied to assign each segment to the most likely cluster/state.

Usage

CNA.out.eachcell(mean.matrix, log2R.NRC, cluster.index, cutoff = 0.5, L = 100)

Arguments

mean.matrix	The cluster mean matrix estimated from FLCNA R function.
log2R.NRC	Log2R data from normalization of original read counts.
cluster.index	Cluster index for all the cells.
cutoff	Cutoff value to further control the number of CNAs, the larger value of cutoff, the smaller number of CNAs. The default is 0.35.
L	Repeat times in the EM algorithm, defaults to 100.

Value

The return is the clustered CNA segments by presenting the start position and end position using CNA marker index, and the copy number states.

state	The CNA states assigned.
start	The start point for CNAs.
end	The end point for CNAs.
width	The width for CNAs.
sample	Sample index.

FeiQin92/FLCNA documentation built on Nov. 27, 2024, 3:36 a.m.