Gene expression data from microarray experiments is widely used for large scale gene expression analysis which facilitates the investigation of fundamental biological processes at molecular level. Such an investigation may be helpful for various biological purposes including disease diagnosis and prognosis, biomarker detection, differentially expressed gene detection, and predicting survival rate of patients. However, data from microarray experiments come with less sample size and thus have limited statistical power for any further biological investigation. To address this problem, researchers are now relying on a more powerful technique called meta-analysis, an integrated analysis of existing data from different but related independent studies. Gene expression data reveal that genes are normally expressed in related functionalities and exhibit hidden patterns, which on elucidating often offers a great opportunity to enhance biological understanding at molecular level. Clustering can play an important role to identify natural and interesting patterns in the underlying gene expression data. In this paper, we explore the applications of three well known clustering techniques i.e. k-Means, Partitioning Around Medoids (PAM) and Hierarchical Clustering (HC) to perform meta-analysis for differentially expressed gene detection in microarray gene expression data. The results of clustering techniques are compared with the results of various statistical meta-analysis techniques, which prove clustering as a robust alternative technique for meta-analysis of gene expression data.