论文标题
基因组学数据计算反卷积中的挑战和观点
Challenges and perspectives in computational deconvolution of genomics data
论文作者
论文摘要
解密细胞类型异质性对于系统地了解组织稳态及其失调疾病的失调至关重要。计算反卷积是一种有效的方法，可以估计各种OMIC数据中的细胞类型丰度。尽管近年来计算反卷积的方法学进展取得了重大进展，但挑战仍然是出色的。在这里，我们从参考数据的质量，地面真相数据的产生，计算方法的局限性以及基准设计和实施方面提出了与计算反卷积有关的四个重大挑战。最后，我们就参考数据生成，计算方法论的新方向以及促进严格的基准测试提出了建议。
Deciphering cell type heterogeneity is crucial for systematically understanding tissue homeostasis and its dysregulation in diseases. Computational deconvolution is an efficient approach estimating cell type abundances from a variety of omics data. Despite significant methodological progress in computational deconvolution in recent years, challenges are still outstanding. Here we enlist four significant challenges related to computational deconvolution, from 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.
