Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel.
Department of Immunology, Weizmann Institute of Science, Rehovot, Israel.
Genome Biol. 2019 Oct 11;20(1):206. doi: 10.1186/s13059-019-1812-2.
scRNA-seq profiles each represent a highly partial sample of mRNA molecules from a unique cell that can never be resampled, and robust analysis must separate the sampling effect from biological variance. We describe a methodology for partitioning scRNA-seq datasets into metacells: disjoint and homogenous groups of profiles that could have been resampled from the same cell. Unlike clustering analysis, our algorithm specializes at obtaining granular as opposed to maximal groups. We show how to use metacells as building blocks for complex quantitative transcriptional maps while avoiding data smoothing. Our algorithms are implemented in the MetaCell R/C++ software package.
scRNA-seq 图谱代表了来自独特细胞的高度局部 mRNA 分子样本，这些样本永远无法再进行重采样，因此稳健的分析必须将采样效应与生物变异性区分开来。我们描述了一种将 scRNA-seq 数据集划分为元细胞的方法：不相交且同质的图谱组，可以从同一细胞中进行重采样。与聚类分析不同，我们的算法专门用于获得细粒度而不是最大的组。我们展示了如何使用元细胞作为构建复杂定量转录图谱的构建块，同时避免数据平滑。我们的算法在 MetaCell R/C++软件包中实现。
