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A binary biclustering algorithm based on the adjacency difference matrix for gene expression data analysis.

He-Ming Chu¹, Jin-Xing Liu¹, Ke Zhang²

¹School of Computer Science, Qufu Normal University, Rizhao, 276826, China.

BMC Bioinformatics

|September 19, 2022

Summary

We introduce the Adjacency Difference Matrix Binary Biclustering (AMBB) algorithm for analyzing gene expression data. AMBB improves the balance between speed and performance for binary datasets, offering practical advantages.

Keywords:

Adjacency matrix Biclustering Binary data Gene expression data

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Biclustering is vital for gene expression data analysis, handling both binary and non-binary matrices.
Existing biclustering algorithms for binary data often struggle with a balance between computational efficiency and accuracy.
Preprocessing gene expression data into binary matrices can mitigate noise and outliers.

Purpose of the Study:

To introduce a novel biclustering algorithm, Adjacency Difference Matrix Binary Biclustering (AMBB), specifically designed for binary gene expression data.
To address the performance and runtime limitations of current biclustering methods for binary datasets.

Main Methods:

The AMBB algorithm constructs an adjacency matrix using adjacency difference values.
Biclusters are identified through the continuous updating of this adjacency difference matrix.
The method facilitates the clustering of genes exhibiting similar responses across various conditions.

Main Results:

The AMBB algorithm demonstrates a superior balance between running time and performance compared to existing methods for binary data.
Experimental results on both synthetic and real-world datasets validate the algorithm's high practicability.
The adjacency matrix effectively groups genes with similar expression patterns.

Conclusions:

The AMBB algorithm presents a significant advancement in biclustering for binary gene expression data.
It offers a practical and efficient solution for identifying biologically relevant gene clusters.
The approach holds promise for enhancing downstream gene expression analysis.