Diagnosing cancer using microarray analysis to study differential gene expression has been a recent focus of intense research Although several very sophisticated analysis tools have been developed with this aim in mind, it still remains a challenge to keep these methods free of parametric adjustments as well as maintain their transparency for the final user. Nonparametric methods in general have been associated with these last two characteristics, thus becoming attractive tools for microarray analysis in cancer research. In particular, diagnosing cancer via microarray analysis is an exercise whereby tissue is characterized according to its differential gene expression levels. In this manuscript, two novel nonparametric methods for cancer diagnosis using microarray data are described and their performance assessed against a baseline approach that utilizes the Mann-Whitney test for median differences. Both methods show promising results in terms of their potential use in making diagnoses.