In this paper we are studying the task of finding coherent-sign-changes biclusters in a binary matrix. This task can be applied to the interpretation of gene expression data, where such a bicluster represents a set of experiments that affect a set of genes in a consistent way. We start with a binary table and study biclustering methods based on FCA and partition pattern structures. Pattern concepts provide biclusters and their hierarchical relation, which can be used to analyze the profile of genes in the given expression data. Our approach is purely symbolic, so we can detect larger biclusters and work with rather complex data.