Department of Mathematics, University of California, Irvine, CA, 92697, USA.
NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, CA, 92697, USA.
Genome Biol. 2021 Jan 4;22(1):10. doi: 10.1186/s13059-020-02238-2.
Distinguishing biological from technical variation is crucial when integrating and comparing single-cell genomics datasets across different experiments. Existing methods lack the capability in explicitly distinguishing these two variations, often leading to the removal of both variations. Here, we present an integration method scMC to remove the technical variation while preserving the intrinsic biological variation. scMC learns biological variation via variance analysis to subtract technical variation inferred in an unsupervised manner. Application of scMC to both simulated and real datasets from single-cell RNA-seq and ATAC-seq experiments demonstrates its capability of detecting context-shared and context-specific biological signals via accurate alignment.
在整合和比较不同实验的单细胞基因组学数据集时，区分生物学变异和技术变异至关重要。现有的方法缺乏明确区分这两种变异的能力，通常会导致同时去除这两种变异。在这里，我们提出了一种整合方法 scMC，用于去除技术变异，同时保留内在的生物学变异。scMC 通过方差分析来学习生物学变异，以非监督的方式减去技术变异。scMC 应用于单细胞 RNA-seq 和 ATAC-seq 实验的模拟和真实数据集，通过准确的对齐，展示了其检测上下文共享和上下文特定生物学信号的能力。
