date: 2024-06-10T08:43:22Z
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pdf:docinfo:title: CAbiNet: joint clustering and visualization of cells and genes for single-cell transcriptomics
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subject: DOI: 10.1093/nar/gkae480 , 00, 0, 00-00-2024. 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.
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dc:title: CAbiNet: joint clustering and visualization of cells and genes for single-cell transcriptomics
modified: 2024-06-10T08:43:22Z
cp:subject: DOI: 10.1093/nar/gkae480 , 00, 0, 00-00-2024. 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.
pdf:docinfo:subject: DOI: 10.1093/nar/gkae480 , 00, 0, 00-00-2024. 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.
pdf:docinfo:creator: Zhao Yan, Kohl Clemens, Rosebrock Daniel, Hu Qinan, Hu Yuhui, Vingron Martin
meta:author: Yan Zhao
meta:creation-date: 2024-06-08T04:43:30Z
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Creation-Date: 2024-06-08T04:43:30Z
pdf:docinfo:custom:doi: 10.1093/nar/gkae480
Author: Yan Zhao
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dc:description: Nucleic Acids Research
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dc:creator: Yan Zhao
description: Nucleic Acids Research
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title: CAbiNet: joint clustering and visualization of cells and genes for single-cell transcriptomics
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