Robust foreground detection in somatic copy number data

Sensitive detection of somatic copy number alterations (SCNA) in cancer genomes is confounded by “waviness” in read depth data. We present dryclean , a signal processing algorithm to optimize SCNA detection in whole genome (WGS) and targeted sequencing platforms through foreground detection and background subtraction of read depth data. Application of dryclean to WGS demonstrates that WGS waviness is driven by replication timing. Re-analysis of thousands of tumor profiles reveals that dryclean provides superior detection of biologically relevant SCNAs relative to state-of-the-art algorithms. Applied to in silico tumor dilutions, dryclean improves the sensitivity of relapse detection 10-fold relative to current standards. dryclean is available as an R package in the GitHub repository https://github.com/mskilab/dryclean