# PScnv: personalized self-normalizing CNV detection with a hierarchical multi-phase framework

## TL;DR

PScnv is a new framework for detecting copy number variations in sequencing data that improves accuracy by using personalized normalization and a multi-phase analysis pipeline.

## Contribution

PScnv introduces a personalized self-normalizing framework for CNV detection that outperforms existing methods without requiring matched normal samples.

## Key findings

- PScnv improves CNV detection accuracy and robustness in clinical tumor samples.
- The framework uses a pre-built panel-of-normals and sample-intrinsic normalization to reduce systematic variation.
- Validation at MET, ERBB2, and MTAP showed improved performance compared to existing methods.

## Abstract

Accurate detection of copy number variations (CNVs) from targeted panel sequencing remains challenging due to limited genomic coverage and pronounced sample-specific biases. Existing normalization strategies, including baseline-cohort, matched-control, and single-sample approaches, often struggle to balance noise suppression with adaptability, leading to inconsistent performance across heterogeneous samples.

We present PScnv, a personalized self-normalizing framework for robust CNV detection from panel sequencing data. PScnv integrates a pre-built panel-of-normals (PoN) with sample-intrinsic stable chromosomes through ridge-regression normalization to generate individualized log2 ratio profiles with reduced systematic variation. CNVs are then identified using a hierarchical multi-phase segmentation pipeline incorporating z-score pre-partitioning, kernel-based correction, and circular binary segmentation. In 139 clinical tumor samples with orthogonal FISH validation at MET, ERBB2, and MTAP, PScnv showed improved accuracy and robustness over existing methods that do not require patient-matched normal samples, provided that a pre-built PoN cohort is available.

Source code is available for academic use at https://github.com/lvws/PScnv.