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scDBic: a novel deep learning-based biclustering algorithm for analyzing scRNA-seq data
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DOI：10.1093/bioinformatics/btag095.png)
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Abstract
En 中文
Clustering single-cell RNA sequencing (scRNA-seq) data plays a vital role in the study of cellular heterogeneity. Many algorithms have been developed to cluster scRNA-seq data. However, traditional clustering algorithms often fail to capture local consistency, whereas biclustering algorithms suffer from issues such as cell loss, poor adaptability to high-dimensional data, and iterative selection challenges.
Keywords:
scRNA-seq
clustering
biclustering
deep learning
cellular heterogeneity
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