Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
Department of Biostatistics, The University of Texas Health Science Center, Houston, TX, 78284, USA.
Sci Rep. 2024 Oct 15;14(1):24152. doi: 10.1038/s41598-024-75226-2.
Single-cell RNA sequencing is a powerful tool to investigate the cellular makeup of tumor samples. However, due to the sparse data and the complex tumor microenvironment, it can be challenging to identify neoplastic cells that play important roles in tumor growth and disease progression. This is especially relevant for blood cancers, where neoplastic cells may be highly similar to normal cells. To address this challenge, we have developed partCNV and partCNVH, two methods for rapid and accurate detection of aneuploid cells with local copy number deletion or amplification. PartCNV uses an expectation-maximization (EM) algorithm with mixtures of Poisson distributions and incorporates cytogenetic information to guide the classification. PartCNVH further improves partCNV by integrating a hidden Markov model for feature selection. We have thoroughly evaluated the performance of partCNV and partCNVH through simulation studies and real data analysis using three scRNA-seq datasets from blood cancer patients. Our results show that partCNV and partCNVH have favorable accuracy and provide more interpretable results compared to existing methods. In the real data analysis, we have identified multiple biological processes involved in the oncogenesis of myelodysplastic syndromes and acute myeloid leukemia.
单细胞 RNA 测序是一种强大的工具，可用于研究肿瘤样本的细胞组成。然而，由于数据稀疏和复杂的肿瘤微环境，识别在肿瘤生长和疾病进展中起重要作用的肿瘤细胞具有挑战性。这在血液癌症中尤为相关，因为肿瘤细胞可能与正常细胞高度相似。为了解决这个挑战，我们开发了 partCNV 和 partCNVH 两种方法，用于快速准确地检测具有局部拷贝数缺失或扩增的非整倍体细胞。partCNV 使用具有泊松分布混合物的期望最大化 (EM) 算法，并结合细胞遗传学信息来指导分类。PartCNVH 通过集成用于特征选择的隐马尔可夫模型进一步改进了 partCNV。我们通过模拟研究和使用来自血液癌患者的三个 scRNA-seq 数据集的实际数据分析，彻底评估了 partCNV 和 partCNVH 的性能。我们的结果表明，partCNV 和 partCNVH 具有良好的准确性，并提供了比现有方法更具可解释性的结果。在实际数据分析中，我们已经确定了涉及骨髓增生异常综合征和急性髓系白血病发生的多个生物学过程。
