Somatic variant analysis suite: copy number variation clonal visualization online platform for large-scale single-cell genomics

The recent advance of single-cell copy number variation (CNV) analysis plays an essential role in addressing intratumor heterogeneity, identifying tumor subgroups and restoring tumor-evolving trajectories at single-cell scale. Informative visualization of copy number analysis results boosts productive scientific exploration, validation and sharing. Several single-cell analysis figures have the effectiveness of visualizations for understanding single-cell genomics in published articles and software packages. However, they almost lack real-time interaction, and it is hard to reproduce them. Moreover, existing tools are time-consuming and memory-intensive when they reach large-scale single-cell throughputs. We present an online visualization platform, single-cell Somatic Variant Analysis Suite (scSVAS), for real-time interactive single-cell genomics data visualization. scSVAS is specifically designed for large-scale single-cell genomic analysis that provides an arsenal of unique functionalities. After uploading the specified input files, scSVAS deploys the online interactive visualization automatically. Users may conduct scientific discoveries, share interactive visualizations and download high-quality publication-ready figures. scSVAS provides versatile utilities for managing, investigating, sharing and publishing single-cell CNV profiles. We envision this online platform will expedite the biological understanding of cancer clonal evolution in single-cell resolution. All visualizations are publicly hosted at https://sc.deepomics.org.

Automated summary

The recent advance of single-cell copy number variation (CNV) analysis is important in addressing intratumor heterogeneity and restoring tumor-evolving trajectories. However, existing tools lack real-time interaction and are hard to reproduce. We present an online platform for real-time interactive visualization of single-cell genomics data to accelerate the understanding of cancer clonal evolution.