Department of Mechatronics Engineering, Incheon National University, Incheon 22012, Korea.
Department of Mechanical Engineering, Dong-A University, Busan 49315, Korea.
Genes (Basel). 2021 Oct 22;12(11):1670. doi: 10.3390/genes12111670.
Single-cell sequencing provides novel means to interpret the transcriptomic profiles of individual cells. To obtain in-depth analysis of single-cell sequencing, it requires effective computational methods to accurately predict single-cell clusters because single-cell sequencing techniques only provide the transcriptomic profiles of each cell. Although an accurate estimation of the cell-to-cell similarity is an essential first step to derive reliable single-cell clustering results, it is challenging to obtain the accurate similarity measurement because it highly depends on a selection of genes for similarity evaluations and the optimal set of genes for the accurate similarity estimation is typically unknown. Moreover, due to technical limitations, single-cell sequencing includes a larger number of artificial zeros, and the technical noise makes it difficult to develop effective single-cell clustering algorithms. Here, we describe a novel single-cell clustering algorithm that can accurately predict single-cell clusters in large-scale single-cell sequencing by effectively reducing the zero-inflated noise and accurately estimating the cell-to-cell similarities. First, we construct an ensemble similarity network based on different similarity estimates, and reduce the artificial noise using a random walk with restart framework. Finally, starting from a larger number small size but highly consistent clusters, we iteratively merge a pair of clusters with the maximum similarities until it reaches the predicted number of clusters. Extensive performance evaluation shows that the proposed single-cell clustering algorithm can yield the accurate single-cell clustering results and it can help deciphering the key messages underlying complex biological mechanisms.
单细胞测序为解析单个细胞的转录组图谱提供了新方法。为了深入分析单细胞测序，需要有效的计算方法来准确预测单细胞聚类，因为单细胞测序技术仅提供每个细胞的转录组图谱。虽然准确估计细胞间的相似性是获得可靠单细胞聚类结果的重要第一步，但由于相似性评估的基因选择以及准确相似性估计的最佳基因集通常未知，因此很难获得准确的相似性测量。此外，由于技术限制，单细胞测序包含更多的人工零值，技术噪声使得开发有效的单细胞聚类算法变得困难。在这里，我们描述了一种新颖的单细胞聚类算法，通过有效降低零膨胀噪声并准确估计细胞间的相似性，可以准确预测大规模单细胞测序中的单细胞聚类。首先，我们基于不同的相似性估计构建了一个集成相似性网络，并使用随机游走重启动框架来减少人工噪声。最后，从较大数量的小尺寸但高度一致的聚类开始，我们迭代地合并一对具有最大相似度的聚类，直到达到预测的聚类数量。广泛的性能评估表明，所提出的单细胞聚类算法可以产生准确的单细胞聚类结果，有助于解析复杂生物学机制背后的关键信息。
