在本研究中，我们引入了通过集成基于单参考的反卷积来稳健估计细胞类型比例（ReCIDE），这是一种通过集成基于单参考的反卷积来稳健估计细胞类型比例的创新框架。 ReCIDE 在基准数据集和真实数据集中优于现有方法，特别是在估计稀有细胞类型比例方面表现出色。通过使用 ReCIDE 对三阴性乳腺癌 (TNBC) 患者的公开批量数据进行探索性分析，我们证明了 TNBC 患者的预后与 T 细胞和血管周围样细胞亚型的比例之间存在显着相关性。基于这一发现，我们开发了 TNBC 患者的预后评估模型。我们的贡献提出了一种提高反卷积精度的新颖框架，展示了其在医学研究中的有效性。© 作者 2024。由牛津大学出版社出版。

In this study, we introduce Robust estimation of Cell type proportions by Integrating single-reference-based DEconvolutions (ReCIDE), an innovative framework for robust estimation of cell type proportions by integrating single-reference-based deconvolutions. ReCIDE outperforms existing approaches in benchmark and real datasets, particularly excelling in estimating rare cell type proportions. Through exploratory analysis on public bulk data of triple-negative breast cancer (TNBC) patients using ReCIDE, we demonstrate a significant correlation between the prognosis of TNBC patients and the proportions of both T cell and perivascular-like cell subtypes. Built upon this discovery, we develop a prognostic assessment model for TNBC patients. Our contribution presents a novel framework for enhancing deconvolution accuracy, showcasing its effectiveness in medical research.© The Author(s) 2024. Published by Oxford University Press.