BnpC: Bayesian non-parametric clustering of single-cell mutation profiles.

ABSTRACT:

Motivation

The high resolution of single-cell DNA sequencing (scDNA-seq) offers great potential to resolve intratumor heterogeneity (ITH) by distinguishing clonal populations based on their mutation profiles. However, the increasing size of scDNA-seq datasets and technical limitations, such as high error rates and a large proportion of missing values, complicate this task and limit the applicability of existing methods.

Results

Here, we introduce BnpC, a novel non-parametric method to cluster individual cells into clones and infer their genotypes based on their noisy mutation profiles. We benchmarked our method comprehensively against state-of-the-art methods on simulated data using various data sizes, and applied it to three cancer scDNA-seq datasets. On simulated data, Bnp

SUBMITTER: Borgsmuller N 

PROVIDER: S-EPMC7750970 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature