Detecting copy-number alterations from single-cell chromatin sequencing data by AtaCNA

Area of Science:

• Genomics
• Computational Biology
• Cancer Research

Background:

• Single-cell assay of transposase-accessible chromatin sequencing (scATAC-seq) profiles genome-wide chromatin accessibility.
• Copy-number alterations (CNAs) are crucial for identifying normal cells and tumor clones in single-cell studies.
• Detecting CNAs from scATAC-seq data is challenging due to noise, sparsity, and confounding factors.

Purpose of the Study:

• To develop a computational algorithm, AtaCNA, for accurate high-resolution CNA detection from scATAC-seq data.
• To evaluate AtaCNA's performance against simulation and real-world datasets.
• To demonstrate AtaCNA's utility in distinguishing malignant from non-malignant cells and identifying cancer subclones.

Main Methods:

• Development of the AtaCNA computational algorithm.
• Benchmarking AtaCNA using simulated data.
• Validation using 10 real scATAC-seq datasets from various cancer types.

Main Results:

• AtaCNA accurately detects high-resolution CNAs from scATAC-seq data.
• AtaCNA effectively distinguishes malignant from non-malignant cells across multiple datasets.
• AtaCNA identified distinct cancer subclones with varying cellular states and small-scale CNAs in glioblastoma, endometrial, and ovarian cancers.

Conclusions:

• AtaCNA provides a robust method for CNA detection in scATAC-seq data.
• The algorithm aids in understanding the interplay between genetic and epigenetic plasticity in cancer.
• High-resolution CNA detection is essential for characterizing complex tumor heterogeneity.