Table 1 Algorithms to infer clonal/cluster composition and their properties/assumptions.

From: Engineered in-vitro cell line mixtures and robust evaluation of computational methods for clonal decomposition and longitudinal dynamics in cancer

Algorithm/Property

Input data

Model/approach

CNA

Phylogenetic inference

Multiple samples

Clomial 4

DTS1

non-Bayesian generative Binomial

N2

N

Y3

SciClone 5

DTS

Bayesian Beta mixture

Y

N

Y

PyClone 6

DTS

Dirichlet process, Beta-Binomial

Y

N

Y

PhyloWGS 7

WGS4 and DTS

Tree-stick-breaking process, Binomial

Y

Y

Y

TrAp8

CP5

deterministic search under constraints

I6

Y

N

LICHeE9

VAF7, CP

perfect phylogeny model

I

Y

Y

Rec-BTP10

VAF, CP

binary tree partition

I

Y

N

CITUP11

VAF, CP

combinatorial algorithm

I

Y

Y

SubCloneSeeker12

CP

exaustive tree enumeration

I

Y

Y

PhyloSub13

DTS

predecessor of PhyloWGS without phylogenic correction for CNA

Y

Y

Y

CloneHD14

WGS

HMM8, variational bayes

Y

N

Y

  1. 1Deep-targeted sequencing; 2no; 3yes; 4whole genome sequencing; 5celular prevalence; 6indirectly via CP; 7variant allele frequency; 8hidden Markov model.