{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"bioRxiv"}],"indexed":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T06:59:57Z","timestamp":1785221997898,"version":"3.55.0"},"posted":{"date-parts":[[2026,7,24]]},"group-title":"Bioinformatics","reference-count":52,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T00:00:00Z","timestamp":1784851200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,7,24]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:p>Single-cell DNA sequencing (scDNA-seq) resolves copy number variation (CNV) at single-cell resolution, revealing tumor heterogeneity and subclonal structure. Most existing methods, however, infer only total copy number. Haplotype-resolved copy number, which captures allelic imbalance and clonal evolution, remains far less developed, largely because low coverage, allelic dropout, and technical noise in scDNA-seq make phased allelic inference substantially harder than total copy number estimation. We present CNVeil, a haplotype-aware framework that infers total, allele-specific, and chromosome-scale haplotype-resolved copy number from scDNA-seq data. CNVeil first builds robust total copy number profiles through highly variable bin selection, hierarchical clustering, subclone-aware ploidy estimation, and cross-cell consensus segmentation. Using this profile as a stable scaffold, it infers allele-specific copy number with an expectation-maximization algorithm applied to heterozygous SNP allele counts, then reconstructs haplotype-specific copy number by enforcing coherent haplotype orientation across adjacent segments via dynamic programming. We benchmarked CNVeil against 12 state-of-the-art methods, including eight total copy number callers, two allele-specific callers, and two haplotype-resolved callers, across 20 simulated and real datasets spanning six experimental settings, including high-multiplexed single-nucleus sequencing, Acoustic Cell Tagmentation (ACT), and 10x Chromium. This constitutes the largest comparative evaluation of single-cell copy number inference methods to date. CNVeil consistently outperformed existing tools in segmentation accuracy, ploidy inference, subclone identification, and allele-specific copy number estimation. In a breast cancer multi-omics (wellDR-seq) cohort, CNVeil uncovered haplotype-specific subclonal diversification invisible to total copy number analysis alone and linked allele-specific copy number states to transcriptional variation. By transforming sparse single-cell allelic signals into chromosome-scale haplotype-resolved profiles, CNVeil closes a major methodological gap and provides a scalable framework for studying tumor evolution and functional genomic heterogeneity at single-cell resolution.<\/jats:p>","DOI":"10.64898\/2026.07.20.739667","type":"posted-content","created":{"date-parts":[[2026,7,25]],"date-time":"2026-07-25T01:30:13Z","timestamp":1784943013000},"source":"Crossref","is-referenced-by-count":0,"title":["CNVeil resolves haplotype-specific copy number and uncovers subclonal architecture hidden from total copy number profiling in single-cell cancer genomes"],"prefix":"10.64898","author":[{"given":"Weiman","family":"Yuan","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, Vanderbilt University, 37235, Nashville, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Can","family":"Luo","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Vanderbilt University, 37235, Nashville, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunfei","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Vanderbilt University, 37212, Nashville, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liting","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Florida State University, 32306, Tallahassee, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zi-Hang","family":"Wen","sequence":"additional","affiliation":[{"name":"Computational Biology Department, School of Computer Science, Carnegie Mellon University, 15213, Pittsburgh, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yichen Henry","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Vanderbilt University, 37212, Nashville, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xian","family":"Mallory","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Florida State University, 32306, Tallahassee, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4015-4787","authenticated-orcid":false,"given":"Xin Maizie","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Vanderbilt University, 37235, Nashville, USA"},{"name":"Department of Computer Science, Vanderbilt University, 37212, Nashville, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"54368","reference":[{"key":"2026072723201703000_2026.07.20.739667v1.1","doi-asserted-by":"publisher","DOI":"10.1038\/s12276-020-00499-2"},{"issue":"1","key":"2026072723201703000_2026.07.20.739667v1.2","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1038\/s41392-022-00990-4","article-title":"High-throughput single-cell sequencing in cancer research","volume":"7","year":"2022","journal-title":"Signal Transduction and Targeted Therapy"},{"issue":"1","key":"2026072723201703000_2026.07.20.739667v1.3","doi-asserted-by":"crossref","first-page":"46","DOI":"10.3390\/math14010046","article-title":"Scgclust: Single-cell graph clustering using graph autoencoders that integrate snvs and cnas","volume":"14","year":"2025","journal-title":"Mathematics"},{"key":"2026072723201703000_2026.07.20.739667v1.4","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-genom-111320-090436"},{"key":"2026072723201703000_2026.07.20.739667v1.5","doi-asserted-by":"crossref","unstructured":"Anaparthy, N. , Ho, Y.-J. , Martelotto, L. , Hammell, M. , Hicks, J. : Single-cell applications of next-generation sequencing. 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