School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450000, China.
Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324000, China.
Brief Bioinform. 2024 Sep 23;25(6). doi: 10.1093/bib/bbae511.
Single-cell RNA sequencing (scRNA-seq) technology has revolutionized biological research by enabling high-throughput, cellular-resolution gene expression profiling. A critical step in scRNA-seq data analysis is cell clustering, which supports downstream analyses. However, the high-dimensional and sparse nature of scRNA-seq data poses significant challenges to existing clustering methods. Furthermore, integrating gene expression information with potential cell structure data remains largely unexplored. Here, we present scCFIB, a novel information bottleneck (IB)-based clustering algorithm that leverages the power of IB for efficient processing of high-dimensional sparse data and incorporates a cross-view fusion strategy to achieve robust cell clustering. scCFIB constructs a multi-feature space by establishing two distinct views from the original features. We then formulate the cell clustering problem as a target loss function within the IB framework, employing a collaborative information fusion strategy. To further optimize scCFIB's performance, we introduce a novel sequential optimization approach through an iterative process. Benchmarking against established methods on diverse scRNA-seq datasets demonstrates that scCFIB achieves superior performance in scRNA-seq data clustering tasks. Availability: the source code is publicly available on GitHub: https://github.com/weixiaojiao/scCFIB.
单细胞 RNA 测序 (scRNA-seq) 技术通过实现高通量、细胞分辨率的基因表达谱分析，彻底改变了生物学研究。scRNA-seq 数据分析的关键步骤是细胞聚类，这支持下游分析。然而，scRNA-seq 数据的高维性和稀疏性给现有的聚类方法带来了重大挑战。此外，将基因表达信息与潜在的细胞结构数据集成在很大程度上仍未得到探索。在这里，我们提出了 scCFIB，这是一种新颖的基于信息瓶颈 (IB) 的聚类算法，利用 IB 的强大功能高效处理高维稀疏数据，并结合了跨视图融合策略以实现稳健的细胞聚类。scCFIB 通过从原始特征建立两个不同的视图来构建一个多特征空间。然后，我们将细胞聚类问题表述为 IB 框架内的目标损失函数，采用协作信息融合策略。为了进一步优化 scCFIB 的性能，我们通过迭代过程引入了一种新颖的顺序优化方法。在各种 scRNA-seq 数据集上对已建立方法进行基准测试表明，scCFIB 在 scRNA-seq 数据聚类任务中具有卓越的性能。可用性：源代码可在 GitHub 上公开获取：https://github.com/weixiaojiao/scCFIB。
