Consensus Clustering for Microarray Gene Expression Data

Muthukalathi, Selvamani; Ramanujam, Ravanan; Thalamuthu, Anbupalam

doi:10.9756/bijdm.6140

“…According to the expression profile of the overlapping genes, molecule subtypes screening was obtained from the validation cohort and analyzed with the ClusterPlus R package ( 30 ). The cumulative distribution function (CDF), total CDF curve area (delta area), and tracing plot were performed as supplementary information to distinguish the detailed similarity between clusters, while the samples were calculated for 100 times.…”

Section: Methodsmentioning

confidence: 99%

A broad cuproptosis landscape in inflammatory bowel disease

Chen

¹

,

Li

²

,

Sun

³

et al. 2022

View full text Add to dashboard Cite

BackgroundCuproptosis, a genetic process of copper-dependent cell death linked to mitochondria respiration, demonstrates its correlation with inhibiting tumoral angiogenesis and motility. Recent studies have developed systematic bioinformatics frameworks to identify the association of cuproptosis with tumors but any non-neoplastic diseases. Therefore, against the background of an increased incidence of inflammatory bowel disease (IBD), the landscape of cuproptosis regulation in IBD is a critical need to be investigated.MethodsThe differentially expressed cuproptosis-related genes (DECRGs) were identified with human sequencing profiles for four inflammatory digestive disorders. Another four independent IBD datasets from GEO were used as a validation cohort. And experimental mice model provides another validation method. Using single sample gene set enrichment analysis (ssGSEA), receiver operating characteristic (ROC) curve, CIBERSORT, and consensus clustering algorithms, we explored the association between immune score and cuproptosis-related genes, as well as the diagnostic value of these genes. Molecular docking screened potential interaction of IBD drugs with the structural regulator by Autodock Vina.ResultsCuproptosis-related regulators exhibited extensive differential expression in Crohn’s Disease (CD), Ulcerative Colitis (UC), Celiac Disease (CEL), and IBD-induced cancer (IBD-CA) that share common differential genes (PDHA1, DBT, DLAT, LIAS). The differential expression of DECRGs was reverified in the validated cohort and immunohistochemistry assay. Moreover, the cell signaling pathways and ontology mainly focused on the mitochondrial respiratory function, which was highly enriched in Gene set enrichment analysis (GSEA). According to ssGSEA and ROC, when considering the four regulators, which showed robust association with immune infiltration in IBD, the area under the ROC (AUC) was 0.743. In addition, two clusters of consensus clustering based on the four regulators exhibit different immune phenotypes. According to molecular docking results, methotrexate gained the highest binding affinity to the main chain of key cuproptosis-related regulators compared with the remaining ten drugs.ConclusionCuproptosis-related regulators were widely linked to risk variants, immune cells, immune function, and drug efficacy in IBD. Regulation of cuproptosis may deeply influence the occurrence and development of patients with IBD.

show abstract

“…Popular clustering algorithms such as k-Means [13], Hierarchical clustering [14,15], PAM (Partitional Around Medoids) [16,17] and SOM (Self Organizing Maps) [18] have been used to find patterns of genes in microarray experiments optimizing one validity index. These approaches are known as Single-Objective Clustering (SOC) [19].…”

Section: Introductionmentioning

confidence: 99%

A Bi-Objective Clustering Algorithm for Gene Expression Data

Parraga-Alava¹,

Inostroza-Ponta²

2017

CLEIej

2

0

View full text Add to dashboard Cite

Clustering algorithms are a common method for data analysis in many science field. They have become popular among biologists because of ease to discovery similar cellular functions in gene expression data. Most approaches consider the gene clustering as an optimization problem, where an ad-hoc cluster quality index is optimized which can be defined regarding gene expression data or biological information. However, these approaches may not be sufficient since they cannot guarantee to generate clusters with similar expression patterns and biological coherence. In this paper, we propose a bi-objective clustering algorithm to discover clusters of genes with high levels of co-expression and biological coherence. Our approach uses a multi-objective evolutionary algorithm (MOEA) that optimizes two index based on gene expression level and biological functional classes. The algorithm is tested on three real-life gene expression datasets. Results show that the proposed model yields gene clusters with higher levels of co-expression and biological coherence than traditional approaches.

show abstract

A bi-objective model for gene clustering combining expression data and external biological knowledge

Parraga-Alava

¹

,

Inostroza-Ponta

²

2016

2016 XLII Latin American Computing Conference (CLEI)

3

0

View full text Add to dashboard Cite

Clustering algorithms are a common method for data analysis in many science field. They have become popular among biologists because of ease to discovery similar cellular functions in gene expression data. Most approaches consider the gene clustering as an optimization problem, where an ad-hoc cluster quality index is optimized which can be defined regarding gene expression data or biological information. However, these approaches may not be sufficient since they cannot guarantee to generate clusters with similar expression patterns and biological coherence. In this paper, we propose a bi-objective clustering algorithm to discover clusters of genes with high levels of co-expression and biological coherence. Our approach uses a multi-objective evolutionary algorithm (MOEA) that optimizes two index based on gene expression level and biological functional classes. The algorithm is tested on three real-life gene expression datasets. Results show that the proposed model yields gene clusters with higher levels of co-expression and biological coherence than traditional approaches.

show abstract

Consensus Clustering for Microarray Gene Expression Data

Cited by 4 publications

References 16 publications

A broad cuproptosis landscape in inflammatory bowel disease

A broad cuproptosis landscape in inflammatory bowel disease

A Bi-Objective Clustering Algorithm for Gene Expression Data

A bi-objective model for gene clustering combining expression data and external biological knowledge

Contact Info

Product

Resources

About