单细胞 RNA 和 ATAC 测序技术能够检查单个细胞的基因表达和染色质可及性，从而深入了解细胞表型。在癌症研究中，在基因克隆的进化背景下持续分析这些状态非常重要。在这里，我们提出 CONGAS，这是一种贝叶斯模型，用于将单细胞 RNA 和 ATAC 图谱映射到拷贝数克隆的潜在空间上。 CONGAS 将细胞聚集成具有相似倍性的肿瘤亚克隆，从而可以直接比较它们的表达和染色质谱。该框架在 GPU 上实现，并在真实和模拟数据上进行了测试，可扩展以无缝分析数千个细胞，展示出比单分子模型更好的性能，并支持新的多组学分析。在前列腺癌、淋巴瘤和基底细胞癌中，CONGAS 成功识别了复杂的亚克隆结构，同时提供 ATAC 和 RNA 之间的连贯图谱，促进基因型-表型图谱及其与基因组不稳定性的联系的研究。版权所有：© 2023 Patruno 等人。这是一篇根据知识共享署名许可条款分发的开放获取文章，允许在任何媒体上不受限制地使用、分发和复制，前提是注明原始作者和来源。

Single-cell RNA and ATAC sequencing technologies enable the examination of gene expression and chromatin accessibility in individual cells, providing insights into cellular phenotypes. In cancer research, it is important to consistently analyze these states within an evolutionary context on genetic clones. Here we present CONGAS+, a Bayesian model to map single-cell RNA and ATAC profiles onto the latent space of copy number clones. CONGAS+ clusters cells into tumour subclones with similar ploidy, rendering straightforward to compare their expression and chromatin profiles. The framework, implemented on GPU and tested on real and simulated data, scales to analyse seamlessly thousands of cells, demonstrating better performance than single-molecule models, and supporting new multi-omics assays. In prostate cancer, lymphoma and basal cell carcinoma, CONGAS+ successfully identifies complex subclonal architectures while providing a coherent mapping between ATAC and RNA, facilitating the study of genotype-phenotype maps and their connection to genomic instability.Copyright: © 2023 Patruno et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.