Clustering single-cell RNA-seq data by rank constrained similarity learning
Clustering single-cell RNA-seq data by rank constrained similarity learning
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DOI：
10.1093/bioinformatics/btab276
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发表时间：
2021-05-07
期刊：
影响因子：
5.8
通讯作者：
Su, Zhengchang
中科院分区：
文献类型：
--
作者：
Mei, Qinglin;Li, Guojun;Su, Zhengchang
动机：单细胞 RNA 测序 (scRNA-seq) 技术的最新突破为识别复杂组织中的异质细胞类型提供了令人兴奋的机会。然而，scRNA-seq 数据中不可避免的生物噪声和技术伪影以及表达载体的高维性使得该问题极具挑战性。因此，尽管已经开发了许多工具，但它们的准确性仍有待提高。结果：在这里，我们引入了一种新颖的聚类算法和工具 RCSL（等级约束相似性学习），以使用来自复杂组织的 scRNA-seq 数据准确识别各种细胞类型。 RCSL同时考虑细胞之间的局部相似性和全局相似性，以辨别同一类型细胞之间的细微差异以及不同类型细胞之间较大的差异。 RCSL 使用细胞表达向量与其他细胞表达向量的斯皮尔曼等级相关性来测量其全局相似性，并自适应地学习细胞的邻居表示作为其局部相似性。一个细胞与其他细胞的整体相似度是其全局相似度和局部相似度的线性组合。 RCSL 自动估计相似性矩阵中定义的细胞类型的数量，并通过构建块对角矩阵来识别它们，从而使其与相似性矩阵的距离最小化。每个块对角子矩阵是一个单元簇/类型，对应于同源相似性图中的连接分量。当在 16 个细胞类型得到良好注释的基准 scRNA-seq 数据集上进行测试时，通过三个指标衡量，RCSL 在准确性和稳健性方面远远优于六种最先进的方法。
Motivation: Recent breakthroughs of single-cell RNA sequencing (scRNA-seq) technologies offer an exciting opportunity to identify heterogeneous cell types in complex tissues. However, the unavoidable biological noise and technical artifacts in scRNA-seq data as well as the high dimensionality of expression vectors make the problem highly challenging. Consequently, although numerous tools have been developed, their accuracy remains to be improved.Results: Here, we introduce a novel clustering algorithm and tool RCSL (Rank Constrained Similarity Learning) to accurately identify various cell types using scRNA-seq data from a complex tissue. RCSL considers both local similarity and global similarity among the cells to discern the subtle differences among cells of the same type as well as larger differences among cells of different types. RCSL uses Spearman's rank correlations of a cell's expression vector with those of other cells to measure its global similarity, and adaptively learns neighbor representation of a cell as its local similarity. The overall similarity of a cell to other cells is a linear combination of its global similarity and local similarity. RCSL automatically estimates the number of cell types defined in the similarity matrix, and identifies them by constructing a block-diagonal matrix, such that its distance to the similarity matrix is minimized. Each block-diagonal submatrix is a cell cluster/type, corresponding to a connected component in the cognate similarity graph. When tested on 16 benchmark scRNA-seq datasets in which the cell types are well-annotated, RCSL substantially outperformed six state-of-the-art methods in accuracy and robustness as measured by three metrics.