Statistical analysis of human tuberculosis microarray gene expression data in the bioconductor R package

Abstract

Tuberculosis is an intracellular bacterial infection that attack organs of human body system, it is a worldwide disease with high estimated number of death rate every year. Microarray technology produces large amount of disease gene expression data and provides opportunities mine the data to understand disease mechanisms at molecular level. The aim of this study is to explore the usage of some tools available for analysing human TB microarray gene expression data. The control stimulated samples with phosphate buffer saline (PBS) and experimental unstimulated samples of three different clinical forms of human TB microarray gene expression data such as latent TB (LTB), pulmonary TB (PTB) and meningeal TB (TBM) were collected from GEO-NCBI database and all analysis were performed by using Bioconductor R packages. The results of this study, explore the use of affycoretool for microarray TB image visualization analysis, AffyQCReport tool for TB microarray data quality assessment, GCRMA method for TB microarray data normalization and LIMMA as a statistical tool for the identification of significantly expressed genes of human TB. According to LIMMA, there was a significant different between stimulated and unstimulated tuberculosis and majority of the significantly expressed genes identified were genes responsible for cellular immune response. The regulated genes identified from the LIMMA analysis using Venn diagram indicated more decrease in rate of gene expression than the increase in stimulated tuberculosis while show more increase in rate of gene expression than decrease in unstimulated tuberculosis. Hierarchical clustering (hclust) method was used to determine common expression pattern among the three different clinical forms of human TB infection, it suggested that, hierarchical clustering analysis distinguish different clinical forms of human TB infection. This study recommended that the results generated from these findings can be used in further analysis for detection and control of human TB infection.