SCYN: single cell CNV profiling method using dynamic programming

Abstract Background Copy number variation is crucial in deciphering the mechanism and cure of complex disorders and cancers. The recent advancement of scDNA sequencing technology sheds light upon addressing intratumor heterogeneity, detecting rare subclones, and reconstructing tumor evolution lineag...

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Autores principales:	Xikang Feng, Lingxi Chen, Yuhao Qing, Ruikang Li, Chaohui Li, Shuai Cheng Li
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	scDNA-Seq CNV segmentation Dynamic programming Biotechnology TP248.13-248.65 Genetics QH426-470
Acceso en línea:	https://doaj.org/article/f2edfd418f534b77b59b2c80c97fe529
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spelling	oai:doaj.org-article:f2edfd418f534b77b59b2c80c97fe5292021-11-21T12:26:22ZSCYN: single cell CNV profiling method using dynamic programming10.1186/s12864-021-07941-31471-2164https://doaj.org/article/f2edfd418f534b77b59b2c80c97fe5292021-11-01T00:00:00Zhttps://doi.org/10.1186/s12864-021-07941-3https://doaj.org/toc/1471-2164Abstract Background Copy number variation is crucial in deciphering the mechanism and cure of complex disorders and cancers. The recent advancement of scDNA sequencing technology sheds light upon addressing intratumor heterogeneity, detecting rare subclones, and reconstructing tumor evolution lineages at single-cell resolution. Nevertheless, the current circular binary segmentation based approach proves to fail to efficiently and effectively identify copy number shifts on some exceptional trails. Results Here, we propose SCYN, a CNV segmentation method powered with dynamic programming. SCYN resolves the precise segmentation on in silico dataset. Then we verified SCYN manifested accurate copy number inferring on triple negative breast cancer scDNA data, with array comparative genomic hybridization results of purified bulk samples as ground truth validation. We tested SCYN on two datasets of the newly emerged 10x Genomics CNV solution. SCYN successfully recognizes gastric cancer cells from 1% and 10% spike-ins 10x datasets. Moreover, SCYN is about 150 times faster than state of the art tool when dealing with the datasets of approximately 2000 cells. Conclusions SCYN robustly and efficiently detects segmentations and infers copy number profiles on single cell DNA sequencing data. It serves to reveal the tumor intra-heterogeneity. The source code of SCYN can be accessed in https://github.com/xikanfeng2/SCYN .Xikang FengLingxi ChenYuhao QingRuikang LiChaohui LiShuai Cheng LiBMCarticlescDNA-SeqCNV segmentationDynamic programmingBiotechnologyTP248.13-248.65GeneticsQH426-470ENBMC Genomics, Vol 22, Iss S5, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	scDNA-Seq CNV segmentation Dynamic programming Biotechnology TP248.13-248.65 Genetics QH426-470
spellingShingle	scDNA-Seq CNV segmentation Dynamic programming Biotechnology TP248.13-248.65 Genetics QH426-470 Xikang Feng Lingxi Chen Yuhao Qing Ruikang Li Chaohui Li Shuai Cheng Li SCYN: single cell CNV profiling method using dynamic programming
description	Abstract Background Copy number variation is crucial in deciphering the mechanism and cure of complex disorders and cancers. The recent advancement of scDNA sequencing technology sheds light upon addressing intratumor heterogeneity, detecting rare subclones, and reconstructing tumor evolution lineages at single-cell resolution. Nevertheless, the current circular binary segmentation based approach proves to fail to efficiently and effectively identify copy number shifts on some exceptional trails. Results Here, we propose SCYN, a CNV segmentation method powered with dynamic programming. SCYN resolves the precise segmentation on in silico dataset. Then we verified SCYN manifested accurate copy number inferring on triple negative breast cancer scDNA data, with array comparative genomic hybridization results of purified bulk samples as ground truth validation. We tested SCYN on two datasets of the newly emerged 10x Genomics CNV solution. SCYN successfully recognizes gastric cancer cells from 1% and 10% spike-ins 10x datasets. Moreover, SCYN is about 150 times faster than state of the art tool when dealing with the datasets of approximately 2000 cells. Conclusions SCYN robustly and efficiently detects segmentations and infers copy number profiles on single cell DNA sequencing data. It serves to reveal the tumor intra-heterogeneity. The source code of SCYN can be accessed in https://github.com/xikanfeng2/SCYN .
format	article
author	Xikang Feng Lingxi Chen Yuhao Qing Ruikang Li Chaohui Li Shuai Cheng Li
author_facet	Xikang Feng Lingxi Chen Yuhao Qing Ruikang Li Chaohui Li Shuai Cheng Li
author_sort	Xikang Feng
title	SCYN: single cell CNV profiling method using dynamic programming
title_short	SCYN: single cell CNV profiling method using dynamic programming
title_full	SCYN: single cell CNV profiling method using dynamic programming
title_fullStr	SCYN: single cell CNV profiling method using dynamic programming
title_full_unstemmed	SCYN: single cell CNV profiling method using dynamic programming
title_sort	scyn: single cell cnv profiling method using dynamic programming
publisher	BMC
publishDate	2021
url	https://doaj.org/article/f2edfd418f534b77b59b2c80c97fe529
work_keys_str_mv	AT xikangfeng scynsinglecellcnvprofilingmethodusingdynamicprogramming AT lingxichen scynsinglecellcnvprofilingmethodusingdynamicprogramming AT yuhaoqing scynsinglecellcnvprofilingmethodusingdynamicprogramming AT ruikangli scynsinglecellcnvprofilingmethodusingdynamicprogramming AT chaohuili scynsinglecellcnvprofilingmethodusingdynamicprogramming AT shuaichengli scynsinglecellcnvprofilingmethodusingdynamicprogramming
_version_	1718419040365445120

SCYN: single cell CNV profiling method using dynamic programming

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