Great concerns have been raised about the reproducibility of gene signatures based on high-throughput techniques such as microarray. Studies analyzing similar samples often report poorly overlapping results, and the p-value usually lacks biological context. We propose a non-parametric Re-Discovery-Curve (RDCurve) method, to estimate the frequency of rediscovery of gene signature identified. Given a ranking procedure and a dataset with replicated measurements, the RDCurve bootstraps the dataset and repeatedly applies the ranking procedure, selects a subset of k important genes, and estimates the probability of rediscovery of the selected subset of genes. We also propose a permutation scheme to estimate the confidence band under the Null hypothesis for the significance of the RDCurve. The method is non-parametric and model independent. With the RDCurve we can assess the signal-noise ratio of the data, compare the performance of ranking procedures in term of their expected rediscovery rates, and choose the number of genes to be reported.