单细胞RNA-seq数据缺失元素补全算法

Algorithm for Missing Elements Completion on Single Cell RNA-seq Data

基于非负矩阵分解模型,提出一种新的数据补全算法.该算法通过循环遍历确定最佳构造矩阵和rank值,解决了单细胞转录组测序(RNA-seq)数据中存在缺失值的问题,避免了由于单细胞测序深度不足对细胞分型分析的影响.在慢性粒细胞白血病单细胞测序数据上的实验结果表明,由补全算法恢复缺失值后的细胞分型更清晰,验证了该算法的有效性.

We proposed a novel data completion algorithm based on the nonnegative matrix factorization model.Using the iterative traversal,the algorithm determined the best construction matrix and rank value,which solved the problem of missing value in single cell transcriptome sequencing data(RNA-seq),and avoided the influence of deficiency of the single cell sequencing depth on cell typing analysis.The experimental result on chronic myeloid leukemia data shows that after the missing values are recovered by the completion algorithm,the cell typing is more clear,which verifies the effectiveness of the proposed algorithm.