Table 1
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SNV-driven tools for subclonal inference

NameYearFeature|$^{\ast }$|Model|$^{\dagger }$|Evolutionary constraintMulti-sample analysis|$^{\ddagger }$|Description§ImplementationRef
PurBayes2013ACBinNNBayesian finite mixture model evaluated with penalized expected devianceR[21]
TrAp2013CVAFN/AYYEnumerates possible tree structures by iteratively merging first-generation treesJava[22]
PyClone2014ACBin, BeBinNYBayesian nonparametric clustering using Dirichlet processPython[23]
SciClone2014AC, VAFBeta, Gaussian, BinNYVariational Bayesian mixture model that prunes marginal clusters to obtain optimal number of clustersR[24]
PhyloSub2014ACBinYYBayesian nonparametric model using tree-structured stick-breaking processC++, Python[25]
EXPANDS2014VAFN/ANNHeuristic clustering of variants based on KL-divergence.R[26]
TITAN2014ACBinNNFactorial HMM with joint emission model for allele counts and RDsR[27]
Clomial2014ACBinNYConstrained matrix factorization using multi-region sequencing dataR[28]
Rec-BTP2014CVAFN/AYNRecursive approximation algorithm for binary tree partitionMATLAB, Python[29]
PhyloWGS2015ACBinYYExtends PhyloSub by incorporating SCNA information from WGSC++, Python[30]
AncesTree2015ACBinYYSolves VAF factorization problem with ILPC++[31]
LICHeE2015VAFGaussianYYMulti-sample tumor phylogeny reconstruction by solving a set cover problemJava[32]
Bayclone2015ACBinNYBayesian nonparametric clustering using categorial Indian buffet processR[33]
Cloe2016ACBeBinYYPhylogenetic latent feature model that reflects hierarchical relationship between subclonal featuresR[34]
Canopy2016ACBinYYMulti-sample tumor phylogeny reconstruction by joint modeling of SNV and SCNAR[35]
ddClone2017ACBin, BeBinNNBayesian nonparametric clustering using distance-dependent Chinese restaurant processR[36]
ClonEvol2017CVAFN/AYYUses bootstrapping to get CIs of subclonal prevalences generated from other methods, then enumerates and evaluates possible subclonal orderingsR[37]
NameYearFeature|$^{\ast }$|Model|$^{\dagger }$|Evolutionary constraintMulti-sample analysis|$^{\ddagger }$|Description§ImplementationRef
PurBayes2013ACBinNNBayesian finite mixture model evaluated with penalized expected devianceR[21]
TrAp2013CVAFN/AYYEnumerates possible tree structures by iteratively merging first-generation treesJava[22]
PyClone2014ACBin, BeBinNYBayesian nonparametric clustering using Dirichlet processPython[23]
SciClone2014AC, VAFBeta, Gaussian, BinNYVariational Bayesian mixture model that prunes marginal clusters to obtain optimal number of clustersR[24]
PhyloSub2014ACBinYYBayesian nonparametric model using tree-structured stick-breaking processC++, Python[25]
EXPANDS2014VAFN/ANNHeuristic clustering of variants based on KL-divergence.R[26]
TITAN2014ACBinNNFactorial HMM with joint emission model for allele counts and RDsR[27]
Clomial2014ACBinNYConstrained matrix factorization using multi-region sequencing dataR[28]
Rec-BTP2014CVAFN/AYNRecursive approximation algorithm for binary tree partitionMATLAB, Python[29]
PhyloWGS2015ACBinYYExtends PhyloSub by incorporating SCNA information from WGSC++, Python[30]
AncesTree2015ACBinYYSolves VAF factorization problem with ILPC++[31]
LICHeE2015VAFGaussianYYMulti-sample tumor phylogeny reconstruction by solving a set cover problemJava[32]
Bayclone2015ACBinNYBayesian nonparametric clustering using categorial Indian buffet processR[33]
Cloe2016ACBeBinYYPhylogenetic latent feature model that reflects hierarchical relationship between subclonal featuresR[34]
Canopy2016ACBinYYMulti-sample tumor phylogeny reconstruction by joint modeling of SNV and SCNAR[35]
ddClone2017ACBin, BeBinNNBayesian nonparametric clustering using distance-dependent Chinese restaurant processR[36]
ClonEvol2017CVAFN/AYYUses bootstrapping to get CIs of subclonal prevalences generated from other methods, then enumerates and evaluates possible subclonal orderingsR[37]

|$^{\ddagger }$|Denotes whether the tool is able to conduct joint analysis of several spatially or longitudinally distinct samples collected from a single tumor. Abbreviations: |$^{*}$|AC, allele count; VAF, variant allele frequency; CVAF, clustered variant allele frequency; |$^{\dagger }$|Bin, binomial; BeBin, beta-binomial; §ILP, integer linear programming; CI, confidence interval.

Table 1
Open in new tab

SNV-driven tools for subclonal inference

NameYearFeature|$^{\ast }$|Model|$^{\dagger }$|Evolutionary constraintMulti-sample analysis|$^{\ddagger }$|Description§ImplementationRef
PurBayes2013ACBinNNBayesian finite mixture model evaluated with penalized expected devianceR[21]
TrAp2013CVAFN/AYYEnumerates possible tree structures by iteratively merging first-generation treesJava[22]
PyClone2014ACBin, BeBinNYBayesian nonparametric clustering using Dirichlet processPython[23]
SciClone2014AC, VAFBeta, Gaussian, BinNYVariational Bayesian mixture model that prunes marginal clusters to obtain optimal number of clustersR[24]
PhyloSub2014ACBinYYBayesian nonparametric model using tree-structured stick-breaking processC++, Python[25]
EXPANDS2014VAFN/ANNHeuristic clustering of variants based on KL-divergence.R[26]
TITAN2014ACBinNNFactorial HMM with joint emission model for allele counts and RDsR[27]
Clomial2014ACBinNYConstrained matrix factorization using multi-region sequencing dataR[28]
Rec-BTP2014CVAFN/AYNRecursive approximation algorithm for binary tree partitionMATLAB, Python[29]
PhyloWGS2015ACBinYYExtends PhyloSub by incorporating SCNA information from WGSC++, Python[30]
AncesTree2015ACBinYYSolves VAF factorization problem with ILPC++[31]
LICHeE2015VAFGaussianYYMulti-sample tumor phylogeny reconstruction by solving a set cover problemJava[32]
Bayclone2015ACBinNYBayesian nonparametric clustering using categorial Indian buffet processR[33]
Cloe2016ACBeBinYYPhylogenetic latent feature model that reflects hierarchical relationship between subclonal featuresR[34]
Canopy2016ACBinYYMulti-sample tumor phylogeny reconstruction by joint modeling of SNV and SCNAR[35]
ddClone2017ACBin, BeBinNNBayesian nonparametric clustering using distance-dependent Chinese restaurant processR[36]
ClonEvol2017CVAFN/AYYUses bootstrapping to get CIs of subclonal prevalences generated from other methods, then enumerates and evaluates possible subclonal orderingsR[37]
NameYearFeature|$^{\ast }$|Model|$^{\dagger }$|Evolutionary constraintMulti-sample analysis|$^{\ddagger }$|Description§ImplementationRef
PurBayes2013ACBinNNBayesian finite mixture model evaluated with penalized expected devianceR[21]
TrAp2013CVAFN/AYYEnumerates possible tree structures by iteratively merging first-generation treesJava[22]
PyClone2014ACBin, BeBinNYBayesian nonparametric clustering using Dirichlet processPython[23]
SciClone2014AC, VAFBeta, Gaussian, BinNYVariational Bayesian mixture model that prunes marginal clusters to obtain optimal number of clustersR[24]
PhyloSub2014ACBinYYBayesian nonparametric model using tree-structured stick-breaking processC++, Python[25]
EXPANDS2014VAFN/ANNHeuristic clustering of variants based on KL-divergence.R[26]
TITAN2014ACBinNNFactorial HMM with joint emission model for allele counts and RDsR[27]
Clomial2014ACBinNYConstrained matrix factorization using multi-region sequencing dataR[28]
Rec-BTP2014CVAFN/AYNRecursive approximation algorithm for binary tree partitionMATLAB, Python[29]
PhyloWGS2015ACBinYYExtends PhyloSub by incorporating SCNA information from WGSC++, Python[30]
AncesTree2015ACBinYYSolves VAF factorization problem with ILPC++[31]
LICHeE2015VAFGaussianYYMulti-sample tumor phylogeny reconstruction by solving a set cover problemJava[32]
Bayclone2015ACBinNYBayesian nonparametric clustering using categorial Indian buffet processR[33]
Cloe2016ACBeBinYYPhylogenetic latent feature model that reflects hierarchical relationship between subclonal featuresR[34]
Canopy2016ACBinYYMulti-sample tumor phylogeny reconstruction by joint modeling of SNV and SCNAR[35]
ddClone2017ACBin, BeBinNNBayesian nonparametric clustering using distance-dependent Chinese restaurant processR[36]
ClonEvol2017CVAFN/AYYUses bootstrapping to get CIs of subclonal prevalences generated from other methods, then enumerates and evaluates possible subclonal orderingsR[37]

|$^{\ddagger }$|Denotes whether the tool is able to conduct joint analysis of several spatially or longitudinally distinct samples collected from a single tumor. Abbreviations: |$^{*}$|AC, allele count; VAF, variant allele frequency; CVAF, clustered variant allele frequency; |$^{\dagger }$|Bin, binomial; BeBin, beta-binomial; §ILP, integer linear programming; CI, confidence interval.

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