A package based on a bi-clustering algorithm to perform meta-GSA that addresses the shortcomings of ‘ranked list’ meta-GSA approaches. iBBiG scales well when applied to hundreds of datasets, is tolerant to noise characteristic of genomics data and when applied on simulated data, outperforms clustering and bi-clustering methods including hierarchical and k-means clustering, FABIA, COALESCE and Bimax. iBBiG is optimized for meta-analysis of large numbers of diverse genomics data that may have unmatched samples. It does not require prior knowledge of the number or size of clusters. When applied to simulated data, it outperforms commonly used clustering methods, discovers overlapping clusters of diverse sizes and is robust in the presence of noise. In summary, iBBiG provides a simple, robust, rapid and scalable method for meta-GSA.