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Offers a comprehensive and general framework to characterize NanoString nCounter data and to detect differential expression (DE) genes for both simple and complex experimental designs. As a method specifically designed for nCounter data, NanoStringDiff utilizes a negative binomial-based model to fit the discrete nature of the data and incorporates several normalization parameters in the model to fully adjust for platform source of variation, sample content variation and background noise. Simulation and real data analyses results show that NanoStringDiff outperforms the existing methods in DE detection.
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