Paper Info
Title
Resolving single-cell copy number profiling for large datasets
Abstract
The advances of single-cell DNA sequencing (scDNA-seq) enable us to characterize the genetic heterogeneity of cancer cells. However, the high noise and low coverage of scDNA-seq impede the estimation of copy number variations (CNVs). In addition, existing tools suffer from intensive execution time and often fail on large datasets. Here, we propose SeCNV, an efficient method that leverages structural entropy, to profile the copy numbers. SeCNV adopts a local Gaussian kernel to construct a matrix, depth congruent map (DCM), capturing the similarities between any two bins along the genome. Then, SeCNV partitions the genome into segments by minimizing the structural entropy from the DCM. With the partition, SeCNV estimates the copy numbers within each segment for cells. We simulate nine datasets with various breakpoint distributions and amplitudes of noise to benchmark SeCNV. SeCNV achieves a robust performance, i.e. the Fl-scores are higher than 0.95 for breakpoint detections, significantly outperforming state-of-the-art methods. SeCNV successfully processes large datasets (>50 000 cells) within 4 min, while other tools fail to finish within the time limit, i.e. 120 h. We apply SeCNV to single-nucleus sequencing datasets from two breast cancer patients and acoustic cell tagmentation sequencing datasets from eight breast cancer patients. SeCNV successfully reproduces the distinct subclones and infers tumor heterogeneity. SeCNV is available at https://github.com/deepomicslab/SeCNV.
Year
DOI
Venue
2022
10.1093/bib/bbac264
BRIEFINGS IN BIOINFORMATICS
Keywords
DocType
Volume
single-cell sequencing, copy number variation, cross-sample breakpoint detection, structural information theory
Journal
23
Issue
ISSN
Citations 
4
1467-5463
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Wang Ruohan100.68
Zhang Yuwei200.34
Wang Mengbo300.34
Feng Xikang400.34
Wang Jianping500.34
Shuai Cheng Li618430.25