A Divide-and-Conquer Approach to Large-Scale Evolutionary Analysis of Single-Cell DNA Data

Abstract

Single-cell sequencing technology is producing large datasets, often containing thousands or even tens of thousands of single-cell genomic data points from an individual patient. Evolutionary analyses of these data sets help uncover and order genetic variants in the data as well as elucidate mutation trees and intra-tumor heterogeneity (ITH) in the case of cancer data sets. To enable such large-scale analyses computationally, we propose a divide-and-conquer approach that could be used to scale up computationally intensive inference methods. The approach consists of four steps: 1) partitioning the dataset into subsets, 2) constructing a rooted tree for each subset, 3) computing a representative genotype for each subset by utilizing its inferred tree, and 4) assembling the individual trees using a tree built on the representative genotypes. Besides its flexibility and enabling scalability, this approach also lends itself naturally to ITH analysis, as the clones would be the individual subsets, and the "assembly tree" could be the mutation tree that defines the clones. To demonstrate the effectiveness of our proposed approach, we conducted experiments employing a range of methods at each stage. In particular, as clustering and dimensionality reduction methods are commonly used to tame the complexity of large datasets in this area, we analyzed the performance of a variety of such methods within our approach.