AMC: accurate mutation clustering from single-cell DNA sequencing data

Author:

Yu Zhenhua12ORCID,Du Fang12

Affiliation:

1. School of Information Engineering, Ningxia University, Yinchuan 750021, China

2. Collaborative Innovation Center for Ningxia Big Data and Artificial Intelligence Co-founded by Ningxia Municipality and Ministry of Education, Ningxia University, Yinchuan 750021, China

Abstract

Abstract Summary Single-cell DNA sequencing (scDNA-seq) now enables high-resolution profiles of intra-tumor heterogeneity. Existing methods for phylogenetic inference from scDNA-seq data perform acceptably well on small datasets but suffer from low computational efficiency and/or degraded accuracy on large datasets. Motivated by the fact that mutations sharing common states over single cells can be grouped together, we introduce a new software called AMC (accurate mutation clustering) to accurately cluster mutations, thus improve the efficiency of phylogenetic inference. AMC first employs principal component analysis followed by K-means clustering to find mutation clusters, then infers the maximum likelihood estimates of the genotypes of each cluster. The inferred genotypes can subsequently be used to reconstruct the phylogenetic tree with high efficiency. Comprehensive evaluations on various simulated datasets demonstrate AMC is particularly useful to efficiently reason the mutation clusters on large scDNA-seq datasets. Availability and implementation AMC is freely available at https://github.com/qasimyu/amc. Supplementary information Supplementary data are available at Bioinformatics online.

Funder

National Natural Science Foundation of China

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

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