The advancements of single-cell RNA sequencing (scRNA-seq) technologies have provided us unprecedented opportunities to characterize cellular states and investigate the mechanisms of complex diseases. Due to technical issues such as dropout events, scRNA-seq data contains excess of false zero counts, which has a substantial impact on the downstream analyses. Although several computational approaches have been proposed to impute dropout events in scRNA-seq data, there is no strong consensus on which is the best approach. In this study, we propose a novel weighted ensemble learning method, named EnTSSR, to impute dropout events in scRNA-seq data. By using a multi-view two-side sparse self-representation framework, our model can exploit the consensus similarities between genes and between cells based on the imputed results of various imputation methods. Moreover, we introduce a weighted ensemble strategy to leverage the information captured by various imputation methods effectively. Down-sampling experiments, clustering analysis, differential expression analysis and cell trajectory inference are carried out to evaluate the performance of our proposed model. Experiment results demonstrate that our EnTSSR can effectively recover the true expression pattern of scRNA-seq data.

中文翻译：

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EnTSSR：一种估算单细胞 RNA 测序数据的加权集成学习方法


单细胞 RNA 测序 (scRNA-seq) 技术的进步为我们提供了前所未有的机会来表征细胞状态和研究复杂疾病的机制。由于丢失事件等技术问题，scRNA-seq 数据包含过多的假零计数，这对下游分析产生重大影响。尽管已经提出了几种计算方法来估算 scRNA-seq 数据中的丢失事件，但对于哪种方法是最佳方法尚未达成强烈共识。在本研究中，我们提出了一种新颖的加权集成学习方法，名为 EnTSSR，用于估算 scRNA-seq 数据中的丢失事件。通过使用多视图两侧稀疏自我表示框架，我们的模型可以根据各种插补方法的插补结果，利用基因之间和细胞之间的一致相似性。此外，我们引入了加权集成策略来有效地利用各种插补方法捕获的信息。进行下采样实验、聚类分析、差异表达分析和细胞轨迹推断来评估我们提出的模型的性能。实验结果表明，我们的EnTSSR可以有效地恢复scRNA-seq数据的真实表达模式。