SCYN : single cell CNV profiling method using dynamic programming

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Author(s)

Detail(s)

Original languageEnglish
Article number651
Journal / PublicationBMC Genomics
Volume22
Issue numberSupplement 5
Online published16 Nov 2021
Publication statusPublished - 2021

Link(s)

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.

Research Area(s)

  • CNV segmentation, Dynamic programming, scDNA-Seq

Citation Format(s)

SCYN : single cell CNV profiling method using dynamic programming. / Feng, Xikang; Chen, Lingxi; Qing, Yuhao; Li, Ruikang; Li, Chaohui; Li, Shuai Cheng.

In: BMC Genomics, Vol. 22, No. Supplement 5, 651, 2021.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review