SCYN

SCYN profiles single-cell copy number variations (CNVs) using a dynamic programming algorithm to segment read-depth data for analysis of intratumor heterogeneity.

Key Features:

• Dynamic Programming Approach: SCYN employs a dynamic programming algorithm to resolve accurate CNV segmentations and overcomes limitations of traditional circular binary segmentation methods.
• Efficiency and Speed: SCYN processes datasets of approximately 2000 cells up to 150 times faster than existing state-of-the-art tools.
• Robustness in Detection: SCYN detects CNVs and infers copy number profiles from single-cell DNA sequencing data, including low-frequency cancer cell populations.
• Validation and Accuracy: SCYN was validated on triple negative breast cancer single-cell DNA (scDNA) data using array comparative genomic hybridization (aCGH) of purified bulk samples as ground truth and recognized gastric cancer cells in 10x Genomics CNV datasets with 1% and 10% spike-ins.
• Application in Cancer Research: SCYN aids identification of tumor subgroups and reconstruction of tumor evolution lineages at single-cell resolution to address intratumor heterogeneity.
• Integration with SCOPE: SCYN integrates with the R package SCOPE to obtain cell-by-bin read depth matrices and perform normalization.

Scientific Applications:

• Intratumor Heterogeneity Analysis: SCYN enables dissection of genetic diversity within tumors at single-cell resolution.
• Tumor Subgroup Identification: SCYN profiles CNVs to identify distinct tumor subgroups for downstream analyses.
• Reconstruction of Tumor Evolution: SCYN's inferred copy number profiles support reconstruction of tumor evolutionary trajectories.

Methodology:

SCYN integrates with the R package SCOPE to obtain cell-by-bin read depth matrices and perform normalization, then applies a dynamic programming algorithm to segment CNVs; performance was evaluated on in silico datasets and real-world single-cell DNA sequencing (scDNA) data.