June 8, 2024Nucleic Acids ResearchOpen Access

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

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Joint biclustering achieves simultaneous grouping of single cells and their corresponding marker genes, overcoming efficiency bottlenecks in large single-cell transcriptomics datasets.
Methodological implementation utilizes Correspondence Analysis based Biclustering on Networks, which constructs a non-linear embedding to perform co-clustering across single-cell data.
Interactive visual exploration supports rapid functional interpretation of biclustering algorithms, though performance depends on dataset quality across single-cell RNA-sequencing runs.

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Computational framework demonstrates joint clustering of cells and marker genes in single-cell transcriptomics, highlighting improved visual exploration of complex sequencing data.

Cite This Study

Zhao et al. (2024) studied this question.

synapsesocial.com/papers/68e65981b6db6435875e8120 https://doi.org/10.1093/nar/gkae480

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Implication

Computational framework demonstrates joint clustering of cells and marker genes in single-cell transcriptomics, highlighting improved visual exploration of complex sequencing data.

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

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Implication

Cite This Study

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

Key Points

Discussion

Implication

Cite This Study

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