THD-Tricluster: A robust triclustering technique and its application in condition specific change analysis in HIV-1 progression data. (August 2018)

• Record Type: Journal Article
• Authors: Kakati, Tulika; Ahmed, Hasin A.; Bhattacharyya, Dhruba K.; Kalita, Jugal K.
• Is Part Of: Computational biology and chemistry. Volume 75(2018)
• Page Start: 154
• Page End: 167
• Publication Date: 2018-08
• Subjects: Microarray technology -- Shifting-and-scaling patterns -- Triclustering -- Coexpressd genes -- Differentially coexpressed; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2018.05.007
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Highlights: THD-Tricluster mines co-expressed genes, exhibiting shifting-and-scaling patterns. Our method scales linearly with dataset size, unlike well-known TRICLUSTER [16]. Gene biomarkers related to HIV-1 disease are identified. The proposed method exploits both spatial and temporal coherence of gene expression. Abstract: Developing a cost-effective and robust triclustering algorithm that can identify triclusters of high biological significance in the gene-sample-time (GST) domain is a challenging task. Most existing triclustering algorithms can detect shifting and scaling patterns in isolation, they are not able to handle co-occurring shifting-and-scaling patterns. This paper makes an attempt to address this issue. It introduces a robust triclustering algorithm called THD-Tricluster to identify triclusters over the GST domain. In addition to applying over several benchmark datasets for its validation, the proposed THD-Tricluster algorithm was applied on HIV-1 progression data to identify disease-specific genes. THD-Tricluster could identify 38 most responsible genes for the deadly disease which includes GATA3, EGR1, JUN, ELF1, AGFG1, AGFG2, CX3CR1, CXCL12, CCR5, CCR2, and many others. The results are validated using GeneCard and other established results.