Fig. 1

The workflow of ddClone. This figure shows the workflow of our method, ddClone. The ddClone approach is predicated on the notion that single cell sequencing data will inform and improve clustering of allele fractions derived from bulk sequencing data in a joint statistical model. ddClone combines a Bayesian non-parametric prior informed by single cell data with a likelihood model based on bulk sequencing data to infer clonal population architecture. Intuitively, the prior encourages genomic loci with co-occurring mutations in single cells to cluster together. Using a cell-locus binary matrix from single cell sequencing, ddClone computes a distance matrix between mutations using the Jaccard distance with exponential decay. This matrix is then used as a prior for inference over mutation clusters and their prevalences from deeply sequenced bulk data in a distance-dependent Chinese restaurant process framework. The output of the model is the most probable set of clonal genotypes present and the prevalence of each genotype in the population