SCuPhr: A Probabilistic Framework for Cell Lineage Tree Reconstruction

Abstract

AbstractReconstruction of cell lineage trees from single-cell DNA sequencing data, has the potential to become a fundamental tool in study of development of 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 for the purpose of estimating the distance between cells without copy number changes, based on single-cell DNA sequencing data. The model is well suited for full Bayesian analysis by introducing prior probabilities for key parameters as well as maximum a posteriori estimation using expectation maximization algorithm. Our model outputs 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, is shown in studies of several synthetic data set.