METHODS: Considering the different confidence levels of different zeros in the gene expression matrix, this paper proposes a SinCWIm method for dropout events in scRNA-seq based on weighted alternating least squares (WALS). The method utilizes Pearson correlation coefficient and hierarchical clustering to quantify the confidence of zero entries. It is then combined with WALS for matrix decomposition. And the imputation result is made close to the actual number by outlier removal and data correction operations.
RESULTS: A total of eight single-cell sequencing datasets were used for comparative experiments to demonstrate the overall superiority of SinCWIm over state-of-the-art models. SinCWIm was applied to cluster the data to obtain an adjusted RAND index evaluation, and the Usoskin, Pollen and Bladder datasets scored 94.46%, 96.48% and 76.74%, respectively. In addition, significant improvements were made in the retention of differential expression genes and visualization.
CONCLUSIONS: SinCWIm provides a valuable imputation method for handling dropout events in single-cell sequencing data. In comparison to advanced methods, SinCWIm demonstrates excellent performance in clustering, visualization and other aspects. It is applicable to various single-cell sequencing datasets.
方法:考虑到基因表达矩阵中不同零的不同置信水平,本文提出了一种基于加权交替最小二乘法(WALS)的scRNA-seq中dropout事件的SinCWIm方法。该方法利用皮尔逊相关系数和层次聚类来量化零条目的置信度。然后与WALS结合进行矩阵分解。并且通过离群值去除和数据校正操作使估算结果接近实际数量。
结果:总共使用八个单细胞测序数据集进行比较实验,以证明SinCWIm优于最先进的模型。应用SinCWIm对数据进行聚类,以获得调整后的RAND指数评估,和乌索斯金,花粉和膀胱数据集得分94.46%,96.48%和76.74%,分别。此外,在差异表达基因的保留和可视化方面取得了显着改善。
结论:SinCWIm为处理单细胞测序数据中的丢失事件提供了一种有价值的归因方法。与先进的方法相比,SinCWIm在集群中展示了出色的性能,可视化和其他方面。它适用于各种单细胞测序数据集。