SCuPhr: A Probabilistic Framework for Cell Lineage Tree Reconstruction

Reconstruction of cell lineage trees, from single-cell DNA sequencing data, has the potential to become a fundamental tool in studies of development and disease, in particular cancer. For cells without copy number alterations that has not been exposed to specific marking techniques, that is normal cells, lineage tracing is naturally based on somatic point mutations. Current single cell sequencing techniques applicable to such cells require an amplification step, which introduces errors, and still often suffer from so-called allelic dropout. We present a detailed model of current technologies, which is likely to capture also any near future technical improvements, for the purpose of estimating the distance between cells without copy number changes, based on single-cell DNA sequencing data. The model is equipped with prior probabilities for key parameters and allows for a Bayesian inference of the distance between two cells, simultaneously taking all the other cells into account. In particular, the model contains variables associated with pairs of loci, of which one is homozygous and the other heterozygous, and has the capacity to perform Bayesian probabilistic read phasing. By applying a fast distance based method, such as FNJ, to the estimated distance, a cell lineage tree can be obtained. In contrast to MCMC based methods, FNJ can easily handle data sets with tens of thousands of taxa. The high accuracy of the so obtained method, called SCuPhr (Single Cell Urn PHased Read), is shown in studies of several synthetic data set.