Fig. 4

Benchmarking results over simulated data. Performance results for ddClone, single cell-only, and bulk data methods on ten synthetic datasets. ddClone and single cell-only methods were provided with single cells, either (1) 50 cells, sampled from a multinomial distribution with true genotype prevalences as parameters (labelled ddClone(λ=∞), OncoNEM(λ=∞), and SCITE(λ=∞)) in absence of doublet and ADO noise, or (2) 50 cells sampled from a Dirichlet-multinomial distribution with λ=10, constituting moderate to small levels of sampling bias (labelled as ddClone(λ=10), OncoNEM(λ=10), and SCITE(λ=10), or (3) 50 cells sampled from a Dirichlet-multinomial distribution with λ=1.12, constituting high levels of sampling bias (labelled as ddClone(λ=1.12), OncoNEM(λ=1.12), and SCITE(λ=1.12), where in the case of (2) and (3), 30% of cells are doublets and r _ADO=30%. Panel a shows V-measure clustering performance. Panel b shows the average over loci of the absolute differences between the inferred and true cellular prevalences. This result shows that in the presence of reasonable levels of noise, ddClone performs comparably well in terms of both V-measure and the accuracy of inferred cellular prevalences