Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Ihnestraße 63-73, 14195 Berlin, Germany.
Department of Pharmacology, School of Medicine, Southern University of Science and Technology, 1088 Xueyuan Avenue, Shenzhen 518055, Guangdong, P.R. China.
Nucleic Acids Res. 2024 Jul 22;52(13):e57. doi: 10.1093/nar/gkae480.
A fundamental analysis task for single-cell transcriptomics data is clustering with subsequent visualization of cell clusters. The genes responsible for the clustering are only inferred in a subsequent step. Clustering cells and genes together would be the remit of biclustering algorithms, which are often bogged down by the size of single-cell data. Here we present 'Correspondence Analysis based Biclustering on Networks' (CAbiNet) for joint clustering and visualization of single-cell RNA-sequencing data. CAbiNet performs efficient co-clustering of cells and their respective marker genes and jointly visualizes the biclusters in a non-linear embedding for easy and interactive visual exploration of the data.
单细胞转录组学数据分析的基本任务之一是聚类，随后对细胞簇进行可视化。负责聚类的基因仅在后续步骤中进行推断。将细胞和基因聚类在一起是双聚类算法的任务，而单细胞数据的规模往往使双聚类算法陷入困境。在这里，我们提出了基于网络对应分析的双聚类（CAbiNet），用于联合聚类和可视化单细胞 RNA 测序数据。CAbiNet 对细胞及其各自的标记基因进行高效的共聚类，并在非线性嵌入中联合可视化双聚类，以便于对数据进行轻松和交互式的可视化探索。
