Abstract: Biclustering algorithm is used to find local patterns as an important tool in the analysis of gene expression data. However, most of the biclusters found by existing biclustering algorithms ...

Biclustering algorithms represent a key methodological advance in analysing gene expression data, enabling simultaneous clustering of both genes and experimental conditions. This dual clustering ...

Abstract: Biclustering is an important data mining technique that allows identifying groups of genes which behave similarly under a subset of conditions for analyzing gene expression data from ...

scDBic is a R+Python package for single-cell RNA sequencing (scRNA-seq) biclustering analysis. It integrates dimensionality reduction and graph-based biclustering in a unified deep learning framework.

If you use this code, please cite: Tang, Xiaoqi and Lan, Chaowang (2024): scDCABC: A deep biclustering method integrating denoising, dimensionality reduction, and clustering for scRNA-seq data.

Sparse singular value decomposition (SSVD) is proposed as a new exploratory analysis tool for biclustering or identifying interpretable row-column associations within high-dimensional data matrices.