find_subclone: Identify subclones in hierarchical tree.
In ananjysong/SCclust: Single Cell Copy Number Analysis and Clustering

find_subclone

R Documentation

Identify subclones in hierarchical tree.

Description

Based on hierarchical clustering, identify the hard/soft clones.

Usage

find_subclone(hc, pinmat, pins, min_node_size = 6, sim_round = 500,
  lm_max = 0.001, hc_method = "average", base_share = 3,
  fdr_thresh = -2, share_min = 0.9, bymax = TRUE, climb_from_size = 2,
  climb_to_share = 3, graphic = TRUE)

Arguments

`hc`	The hclust objects with clones identified.
`pinmat`	The pinmat.
`pins`	The pins.
`min_node_size`	An integer. Default: 6. The minimum node size for a subclone.
`sim_round`	The number of permutation simulations for subclone identification. Default: 500.
`lm_max`	Numeric value. Default: 0.001. The threshold parameter for the linear fit to identify subclones.
`hc_method`	Default: average
`base_share`	An integer. Default: 3. A balance parameter for controlling minimal number of shared features in a subclone node.
`fdr_thresh`	FDR criterion for subclone nodes. Default: -2.
`share_min`	A feature is considered shared if present in share_min fraction of leaves in a node.Default: 0.90.
`bymax`	Logical. If TRUE (Default), use maximal of mean FDR for the node to find subclones.
`climb_from_size`	An integer. Default: 2.
`climb_to_share`	An integer. Default: 3.
`graphic`	Logical. If TRUE (Default), generate the hierarchical tree plot with hard/soft clones labeled.