Nonparametric Estimation of the Number of Unique Sequences in Biological Samples

Large-scale determination of uniquely expressed genes (or mRNAs) in specific cells and tissues is a challenging problem in computational and functional genomics. We consider nonparametric approaches for estimating the number of unique, nonredundant sequences in biological samples. By introducing the moments of species' abundance in a population, we analyze the relative abundance of species in the population and present a lower bound estimator and so-called medial estimator for the number of distinct species in the population. The lower bound estimate is applicable to populations with small coefficients of variation (CV). The medial estimator works well for the populations with relatively large CV, especially gene expression data. Simulation analysis shows that the medial estimator performs better than existing methods. Finally, we apply our nonparametric approaches to estimate the number of expressed mRNAs in a normal colon epithelial tissue as well as unique clones in an amplified cDNA sample prepared from the CNS of the sea-slug Aplysia

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