Enabling Adaptive CNV Detection through A Novel Predictive Control Framework

Accurate detection of copy number variations (CNVs) from sequencing data is crucial in many complex traits and diseases research. Although many CNV detection algorithms have been developed, challenges in precisely identifying CNVs persist. The core statistical model of these algorithms cannot self-adjust, which limits their adaptability to heterogeneous samples and reduces detection accuracy. address this challenge, we reframed the CNV detection problem as a quality control issue and incorporated adaptive mechanisms. We developed adapCNV, a novel adaptive CNV detection framework that integrates machine learning with optimization control. This framework enables dynamic adaptation of primary parameters based on sample features. We defined a quantifiable metric, RD fluctuation values, to assess signal characteristics when the algorithm accurately detects CNVs. We then employed machine learning techniques extract features from panel sequencing data, select initial parameter values for samples, and determine optimal RD fluctuation values. By adopting adaptive model predictive control (AMPC), adapCNV performs optimizations within rolling window. It dynamically adjusts the primary parameters based on error feedback from RD fluctuation values. This adaptive control strategy enables dynamic adjustment automatically match the characteristics of panel sequencing samples, significantly enhancing overall detection quality. The performance of this framework was validated with simulated data. Comparative analysis demonstrated that the proposed method outperforms the baseline approach, particularly in detecting small CNVs. The adapCNV framework is particularly suitable for panel sequencing, which may have broad applications in clinical practice. This novel approach from quality control perspective introduces a new paradigm for CNV detection.