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Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...

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Related Experiment Video

Updated: Aug 11, 2025

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

ARBic: an all-round biclustering algorithm for analyzing gene expression data.

Xiangyu Liu¹, Ting Yu¹, Xiaoyu Zhao¹

¹Research Center for Mathematics and Interdisciplinary Sciences, Shandong University, Jinan 250100, China.

NAR Genomics and Bioinformatics

|February 3, 2023

Summary

This summary is machine-generated.

A new algorithm, ARBic, accurately identifies broader and narrower gene biclusters in large datasets. ARBic balances effectiveness and efficiency, outperforming existing methods in gene expression analysis.

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Last Updated: Aug 11, 2025

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Published on: October 11, 2018

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Identifying functionally correlated genes through biclustering is crucial for understanding gene expression data.
Existing biclustering algorithms struggle to balance effectiveness and efficiency, limiting their ability to find biclusters of varying shapes and sizes.

Purpose of the Study:

To introduce ARBic, a novel algorithm for identifying significant biclusters of any shape in large-scale gene expression datasets.
To address the limitations of existing algorithms in simultaneously detecting both broader and narrower biclusters.

Main Methods:

ARBic integrates column-based and row-based strategies into a single biclustering procedure.
The column-based strategy, adapted from RecBic, identifies narrower biclusters.
The row-based strategy uses a directed graph's longest path to identify broader biclusters.

Main Results:

ARBic demonstrated superior performance on simulated datasets, achieving an average of 29% higher recovery, relevance, and scores compared to the best existing tool.
ARBic significantly outperformed other algorithms on real datasets.
The algorithm exhibits enhanced robustness against noise, diverse bicluster shapes, and different dataset types.

Conclusions:

ARBic offers an effective and efficient solution for comprehensive bicluster identification in gene expression data.
The algorithm's ability to identify biclusters of various shapes and sizes makes it a valuable tool for biological discovery.