Integrative Modeling of Read Depth and B-Allele Frequency Improves Single-Cell Copy Number Calling from Targeted DNA Sequencing Panels.

Copy number variations (CNVs) drive cancer initiation and progression, but resolving them at single-cell resolution from targeted DNA sequencing panels remains challenging. The Mission Bio Tapestri platform generates 2 complementary signals for CNV inference: sequencing depth and B-allele frequency (BAF) from heterozygous variants; however, existing methods such as karyotapR rely primarily on read depth, leaving allele-specific events unused. Here, we introduce scPloidyR, a hidden Markov model (HMM) that jointly models read depth and BAF at amplicon resolution for single-cell copy number calling from Tapestri data. scPloidyR fits per-chromosome Markov chains with copy number as the hidden state, factorizes emissions into depth and BAF likelihoods, and learns parameters by Baum-Welch expectation-maximization with Viterbi decoding. We compared scPloidyR with the established karyotapR Gaussian mixture model (GMM) in 2 simulation studies spanning BAF noise, variant density, amplicon density, sample size, and heterozygosity rate, and on a public Tapestri 5-cell-line mixture dataset. In simulations, scPloidyR substantially outperformed karyotapR on class-balanced metrics (macro-F1: 0.477 versus 0.273; alteration F1: 0.903 versus 0.381 in simulation study 1) when allelic information was available. Adding just one heterozygous variant per amplicon increased scPloidyR accuracy from 0.556 to 0.897 for gains. However, when BAF information was absent, karyotapR outperformed scPloidyR, and high BAF noise sharply degraded joint-model performance. On real data, scPloidyR produced more spatially coherent and biologically plausible copy number profiles. These results show that joint depth-BAF modeling benefits single-cell CNV calling when allelic information is available, while depth-only methods remain preferable when it is absent.