Table 1.

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

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

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