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首页> 美国卫生研究院文献>NAR Genomics and Bioinformatics >Dimensionality reduction for single cell RNA sequencing data using constrained robust non-negative matrix factorization
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Dimensionality reduction for single cell RNA sequencing data using constrained robust non-negative matrix factorization

机译：使用受约束鲁棒非负矩阵分解的单细胞RNA测序数据的维度降低

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摘要

Single cell RNA-sequencing (scRNA-seq) technology, a powerful tool for analyzing the entire transcriptome at single cell level, is receiving increasing research attention. The presence of dropouts is an important characteristic of scRNA-seq data that may affect the performance of downstream analyses, such as dimensionality reduction and clustering. Cells sequenced to lower depths tend to have more dropouts than those sequenced to greater depths. In this study, we aimed to develop a dimensionality reduction method to address both dropouts and the non-negativity constraints in scRNA-seq data. The developed method simultaneously performs dimensionality reduction and dropout imputation under the non-negative matrix factorization (NMF) framework. The dropouts were modeled as a non-negative sparse matrix. Summation of the observed data matrix and dropout matrix was approximated by NMF. To ensure the sparsity pattern was maintained, a weighted ℓ1 penalty that took into account the dependency of dropouts on the sequencing depth in each cell was imposed. An efficient algorithm was developed to solve the proposed optimization problem. Experiments using both synthetic data and real data showed that dimensionality reduction via the proposed method afforded more robust clustering results compared with those obtained from the existing methods, and that dropout imputation improved the differential expression analysis.
机译：单细胞RNA测序（SCRNA-SEQ）技术，一种用于在单个细胞层面分析整个转录组的强大工具，正在接受增加的研究关注。遗失的存在可能会影响下游分析，如降维聚类和的性能scRNA-SEQ数据的一个重要特征。测序到较低深度的细胞倾向于具有比测序到更大深度的更大的辍学。在这项研究中，我们旨在制定一系列减少方法，以解决ScrNA-SEQ数据中的辍学和非消极性约束。开发方法同时在非负矩阵分解（NMF）框架下执行维度降低和丢弃局部。辍学被建模为非负稀疏矩阵。观察到的数据矩阵和丢失矩阵的求和由NMF近似。为了确保维持稀疏性模式，施加了考虑辍学级别对每个细胞中测序深度的依赖性的加权ℓ1罚款。开发了一种有效的算法来解决所提出的优化问题。同时使用合成的数据和真实数据的实验表明通过提供更坚固的聚类结果与那些从现有的方法获得的，并且压差插补相比提高的差异表达分析所提出的方法，其维数降低。

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