Center for Genomic Medicine, Graduate School of Medicine, Kyoto University, Nanbusogo-Kenkyu-To-1, 5F, 53 Syogoin-Kawaramachi, Sakyo-ku, Kyoto, 606-8507, Japan.
J Hum Genet. 2022 Apr;67(4):215-221. doi: 10.1038/s10038-021-00989-9. Epub 2021 Nov 1.
Comparing multiple single-cell expression datasets such as cytometry and scRNA-seq data between case and control donors provides information to elucidate the mechanisms of disease. We propose a completely data-driven computational biological method for this task. This overcomes the challenges of conventional cellular subset-based comparisons and facilitates further analyses such as machine learning and gene set analysis of single-cell expression datasets.
比较病例和对照供体之间的多个单细胞表达数据集，如流式细胞术和 scRNA-seq 数据，可以提供阐明疾病机制的信息。我们为此任务提出了一种完全基于数据的计算生物学方法。这克服了传统基于细胞亚群比较的挑战，并促进了单细胞表达数据集的机器学习和基因集分析等进一步分析。
