Inferring single-cell copy number profiles through cross-cell segmentation of read counts.

ABSTRACT:

Background

Copy number alteration (CNA) is one of the major genomic variations that frequently occur in cancers, and accurate inference of CNAs is essential for unmasking intra-tumor heterogeneity (ITH) and tumor evolutionary history. Single-cell DNA sequencing (scDNA-seq) makes it convenient to profile CNAs at single-cell resolution, and thus aids in better characterization of ITH. Despite that several computational methods have been proposed to decipher single-cell CNAs, their performance is limited in either breakpoint detection or copy number estimation due to the high dimensionality and noisy nature of read counts data.

Results

By treating breakpoint detection as a process to segment high dimensional read count sequence, we develop a novel method called DeepCNA for cros

SUBMITTER: Liu F 

PROVIDER: S-EPMC10762977 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature