# PairClone: A Bayesian Subclone Caller Based on Mutation Pairs

## TL;DR

PairClone is a Bayesian method that reconstructs tumor subclones from NGS data by modeling mutation pairs, offering improved accuracy over methods using only marginal reads, and is validated on simulated and real datasets.

## Contribution

It introduces a novel Bayesian nonparametric approach using cIBP to model mutation pairs for tumor subclone reconstruction, enhancing existing methods.

## Key findings

- Accurately estimates number and genotypes of subclones
- Performs well on simulated datasets
- Validated on real tumor data

## Abstract

Tumor cell populations can be thought of as being composed of homogeneous cell subpopulations, with each subpopulation being characterized by overlapping sets of single nucleotide variants (SNVs). Such subpopulations are known as subclones and are an important target for precision medicine. Reconstructing such subclones from next-generation sequencing (NGS) data is one of the major challenges in precision medicine. We present PairClone as a new tool to implement this reconstruction. The main idea of PairClone is to model short reads mapped to pairs of proximal SNVs. In contrast, most existing methods use only marginal reads for unpaired SNVs. Using Bayesian nonparametric models, we estimate posterior probabilities of the number, genotypes and population frequencies of subclones in one or more tumor sample. We use the categorical Indian buffet process (cIBP) as a prior probability model for subclones that are represented as vectors of categorical matrices that record the corresponding sets of mutation pairs. Performance of PairClone is assessed using simulated and real datasets. An open source software package can be obtained at http://www.compgenome.org/pairclone.