BnpC: Bayesian non-parametric clustering of single-cell mutation profiles
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Author / Producer
Date
2020-10-01
Publication Type
Journal Article
ETH Bibliography
yes
Citations
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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, BnpC compared favorably against current methods in terms of accuracy, runtime and scalability. Its inferred genotypes were the most accurate, especially on highly heterogeneous data, and it was the only method able to run and produce results on datasets with 5000 cells. On tumor scDNA-seq data, BnpC was able to identify clonal populations missed by the original cluster analysis but supported by Supplementary Experimental Data. With ever growing scDNA-seq datasets, scalable and accurate methods such as BnpC will become increasingly relevant, not only to resolve ITH but also as a preprocessing step to reduce data size.
Availability and implementation
BnpC is freely available under MIT license at https://github.com/cbg-ethz/BnpC.
Supplementary information
Supplementary data are available at Bioinformatics online.
Permanent link
Publication status
published
Editor
Book title
Journal / series
Volume
36 (19)
Pages / Article No.
4854 - 4859
Publisher
Oxford University Press
Event
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Organisational unit
03790 - Beerenwinkel, Niko / Beerenwinkel, Niko
Notes
Funding
766030 - Computational ONcology TRaining Alliance (EC)
609883 - Mechanisms of Evasive Resistance in Liver Cancer (EC)
609883 - Mechanisms of Evasive Resistance in Liver Cancer (EC)