Program for Computational and Systems Biology, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Program for Computational and Systems Biology, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Department of Applied Physics and Applied Math, Columbia University, New York, NY, USA.
Cell. 2018 Jul 26;174(3):716-729.e27. doi: 10.1016/j.cell.2018.05.061. Epub 2018 Jun 28.
Single-cell RNA sequencing technologies suffer from many sources of technical noise, including under-sampling of mRNA molecules, often termed "dropout," which can severely obscure important gene-gene relationships. To address this, we developed MAGIC (Markov affinity-based graph imputation of cells), a method that shares information across similar cells, via data diffusion, to denoise the cell count matrix and fill in missing transcripts. We validate MAGIC on several biological systems and find it effective at recovering gene-gene relationships and additional structures. Applied to the epithilial to mesenchymal transition, MAGIC reveals a phenotypic continuum, with the majority of cells residing in intermediate states that display stem-like signatures, and infers known and previously uncharacterized regulatory interactions, demonstrating that our approach can successfully uncover regulatory relations without perturbations.
单细胞 RNA 测序技术受到许多技术噪声源的影响，包括 mRNA 分子的抽样不足，通常称为“缺失”，这会严重掩盖重要的基因-基因关系。为了解决这个问题，我们开发了 MAGIC（基于马尔可夫亲和力的细胞图推断），这是一种通过数据扩散在相似细胞之间共享信息的方法，以对细胞计数矩阵进行去噪并填充缺失的转录本。我们在几个生物学系统上验证了 MAGIC，发现它有效地恢复了基因-基因关系和其他结构。应用于上皮到间充质转化，MAGIC 揭示了一个表型连续体，大多数细胞位于具有干细胞特征的中间状态，并推断出已知的和以前未表征的调控相互作用，表明我们的方法可以在没有干扰的情况下成功地揭示调控关系。
