CAbiNet: Joint Clustering and Visualization of Cells and Genes for Single-cell Transcriptomics

Overview

Journal Nucleic Acids Res

Publisher Oxford University Press

Specialty Biochemistry

Date 2024 Jun 8

PMID 38850160

Authors

Yan Zhao

Clemens Kohl

Daniel Rosebrock

Qinan Hu

Yuhui Hu

Martin Vingron

Affiliations

Soon will be listed here.

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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