Xu Su, Chen Duan, Wang Xue, Li Shaoyu
Department of Mathematics and Statistics, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC 28223, United States.
School of Data Science, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC 28223, United States.
Brief Bioinform. 2025 May 1;26(3). doi: 10.1093/bib/bbaf264.
This study benchmarks the robustness and resilience of computational deconvolution methods for estimating cell-type proportions in bulk tissues, with a focus on comparing reference-based and reference-free methods. Robustness is evaluated by generating in silico pseudo-bulk tissue RNA sequencing data from cell-level gene expression profiles derived from four different tissue types, with simulated cellular composition at varying levels of heterogeneity. To assess resilience, we intentionally alter single-cell RNA profiles to create pseudo-bulk tissue RNA-seq data. Deconvolution estimates are compared with ground truth using Pearson's correlation coefficient, root mean squared deviation, and mean absolute deviation. The results show that reference-based methods are more robust when reliable reference data are available, whereas reference-free methods excel in scenarios lacking suitable reference data. Furthermore, variations in cell-level transcriptomic profiles and cell composition have emerged as critical factors influencing the performance of deconvolution methods. This study provides significant insights into the factors affecting bulk tissue deconvolution performance, which are essential for guiding users and advancing the development of more powerful and reliable algorithms in the future.
本研究对用于估计大块组织中细胞类型比例的计算反卷积方法的稳健性和恢复力进行了基准测试，重点是比较基于参考的方法和无参考的方法。通过从源自四种不同组织类型的细胞水平基因表达谱生成计算机模拟的伪大块组织RNA测序数据来评估稳健性，模拟的细胞组成具有不同程度的异质性。为了评估恢复力，我们故意改变单细胞RNA谱以创建伪大块组织RNA-seq数据。使用皮尔逊相关系数、均方根偏差和平均绝对偏差将反卷积估计值与真实值进行比较。结果表明，当有可靠的参考数据时，基于参考的方法更稳健，而在缺乏合适参考数据的情况下，无参考的方法表现出色。此外，细胞水平转录组谱和细胞组成的变化已成为影响反卷积方法性能的关键因素。本研究为影响大块组织反卷积性能的因素提供了重要见解，这对于指导用户和推动未来更强大、更可靠算法的开发至关重要。
