Insights from the clustering of microarray data associated with the heart disease

• Published in: Bioinformation Vol. 9; no. 15; pp. 759 - 765
• Main Authors: Perumal, Venkatesan, Mahalingam, Vasantha
• Format: Journal Article
• Language: English
• Published: Singapore Biomedical Informatics 01.01.2013
• Subjects: Hypothesis; Cluster analysis; Heart failure; Microarray data; Principal component analysis
• ISSN: 0973-2063; 0973-8894; 0973-2063
• DOI: 10.6026/97320630009759

Heart failure (HF) is the major of cause of mortality and morbidity in the developed world. Gene expression profiles of animal model of heart failure have been used in number of studies to understand human cardiac disease. In this study, statistical methods of analysing microarray data on cardiac tissues from dogs with pacing induced HF were used to identify differentially expressed genes between normal and two abnormal tissues. The unsupervised techniques principal component analysis (PCA) and cluster analysis were explored to distinguish between three different groups of 12 arrays and to separate the genes which are up regulated in different conditions among 23912 genes in heart failure canines' microarray data. It was found that out of 23912 genes, 1802 genes were differentially expressed in the three groups at 5% level of significance and 496 genes were differentially expressed at 1% level of significance using one way analysis of variance (ANOVA). The genes clustered using PCA and clustering analysis were explored in the paper to understand HF and a small number of differentially expressed genes related to HF were identified.