Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN 46556, USA.
Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556, USA.
Nucleic Acids Res. 2018 Feb 16;46(3):e14. doi: 10.1093/nar/gkx1113.
Cell types in cell populations change as the condition changes: some cell types die out, new cell types may emerge and surviving cell types evolve to adapt to the new condition. Using single-cell RNA-sequencing data that measure the gene expression of cells before and after the condition change, we propose an algorithm, SparseDC, which identifies cell types, traces their changes across conditions and identifies genes which are marker genes for these changes. By solving a unified optimization problem, SparseDC completes all three tasks simultaneously. SparseDC is highly computationally efficient and demonstrates its accuracy on both simulated and real data.
当条件发生变化时，细胞群体中的细胞类型会发生变化：一些细胞类型死亡，新的细胞类型可能出现，存活的细胞类型则进化以适应新的条件。我们使用单细胞 RNA 测序数据来测量条件变化前后细胞的基因表达，提出了一种算法 SparseDC，它可以识别细胞类型，追踪它们在不同条件下的变化，并识别这些变化的标记基因。通过解决一个统一的优化问题，SparseDC 可以同时完成这三个任务。SparseDC 的计算效率非常高，在模拟和真实数据上都证明了其准确性。
