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Journal Article

CAbiNet: joint clustering and visualization of cells and genes for single-cell transcriptomics

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Zhao,  Yan
Transcriptional Regulation (Martin Vingron), Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Kohl,  Clemens       
Transcriptional Regulation (Martin Vingron), Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Rosebrock,  Daniel       
Transcriptional Regulation (Martin Vingron), Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Vingron,  Martin       
Transcriptional Regulation (Martin Vingron), Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Citation

Zhao, Y., Kohl, C., Rosebrock, D., Hu, Q., Hu, Y., & Vingron, M. (2024). CAbiNet: joint clustering and visualization of cells and genes for single-cell transcriptomics. Nucleic Acids Research, gkae480. doi:10.1093/nar/gkae480.


Cite as: https://hdl.handle.net/21.11116/0000-000F-6407-9
Abstract
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.