Nonnegative Decompositions with Resampling for Improving Gene Expression Data Biclustering Stability

The small sample sizes and high dimensionality of gene expression datasets pose significant problems for unsupervised subgroup discovery. While the stability of unidimensional clustering algorithms has been previously addressed, generalizing existing approaches to biclustering has proved extremely difficult. Despite these difficulties, developing a stable biclustering algorithm is essential for analyzing gene expression data, where genes tend to be co-expressed only for subsets of samples, in certain specific biological contexts, so that both gene and sample dimensions have to be taken into account simultaneously.