Abstract

With the advent of single-cell RNA sequencing (scRNA-seq) technology, many scRNA-seq data have become available, providing an unprecedented opportunity to explore cellular composition and heterogeneity. Recently, many computational algorithms for predicting cell type composition have been developed, and these methods are typically evaluated on different datasets and performance metrics using diverse techniques. Consequently, the lack of comprehensive and standardized comparative analysis makes it difficult to gain a clear understanding of the strengths and weaknesses of these methods. To address this gap, we reviewed 20 cutting-edge unsupervised cell type identification methods and evaluated these methods comprehensively using 24 real scRNA-seq datasets of varying scales. In addition, we proposed a new ensemble cell-type identification method, named scEM, which learns the consensus similarity matrix by applying the entropy weight method to the four representative methods are selected. The Louvain algorithm is adopted to obtain the final classification of individual cells based on the consensus matrix. Extensive evaluation and comparison with 11 other similarity-based methods under real scRNA-seq datasets demonstrate that the newly developed ensemble algorithm scEM is effective in predicting cellular type composition.

Graphic Abstract

中文翻译：

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scEM：基于 scRNA-Seq 数据预测细胞类型组成的新集成框架

摘要

随着单细胞 RNA 测序 (scRNA-seq) 技术的出现，许多 scRNA-seq 数据已经可用，为探索细胞组成和异质性提供了前所未有的机会。最近，已经开发了许多用于预测细胞类型组成的计算算法，并且这些方法通常使用不同的技术在不同的数据集和性能指标上进行评估。因此，缺乏全面、规范的比较分析，很难清楚地了解这些方法的优缺点。为了弥补这一差距，我们回顾了 20 种前沿的无监督细胞类型识别方法，并使用 24 个不同规模的真实 scRNA-seq 数据集全面评估了这些方法。此外，我们提出了一种新的集成细胞类型识别方法，称为scEM，该方法通过将熵权法应用于选择的四种代表性方法来学习一致相似度矩阵。采用Louvain算法，根据共识矩阵得到单个细胞的最终分类。在真实 scRNA-seq 数据集下与其他 11 种基于相似性的方法进行的广泛评估和比较表明，新开发的集成算法 scEM 在预测细胞类型组成方面是有效的。

图文摘要