Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.
Nat Methods. 2024 Mar;21(3):391-400. doi: 10.1038/s41592-023-02166-6. Epub 2024 Feb 19.
Deciphering cell-type heterogeneity is crucial for systematically understanding tissue homeostasis and its dysregulation in diseases. Computational deconvolution is an efficient approach for estimating cell-type abundances from a variety of omics data. Despite substantial methodological progress in computational deconvolution in recent years, challenges are still outstanding. Here we enlist four important challenges related to computational deconvolution: the quality of the reference data, generation of ground truth data, limitations of computational methodologies, and benchmarking design and implementation. Finally, we make recommendations on reference data generation, new directions of computational methodologies, and strategies to promote rigorous benchmarking.
解析细胞类型异质性对于系统地理解组织内稳态及其在疾病中的失调至关重要。计算去卷积是一种从各种组学数据中估计细胞类型丰度的有效方法。尽管近年来计算去卷积在方法学上取得了重大进展，但仍存在挑战。在这里，我们列出了与计算去卷积相关的四个重要挑战：参考数据的质量、真实数据的生成、计算方法的局限性以及基准测试的设计和实现。最后，我们就参考数据生成、计算方法的新方向以及促进严格基准测试的策略提出了建议。
