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  "Description": "We present a novel statistical framework for identifying\ndifferential distributions in single-cell RNA-sequencing\n(scRNA-seq) data between treatment conditions by modeling gene\nexpression read counts using generalized linear models (GLMs).\nWe model each gene independently under each treatment condition\nusing error distributions Poisson (P), Negative Binomial (NB),\nZero-inflated Poisson (ZIP) and Zero-inflated Negative Binomial\n(ZINB) with log link function and model based normalization for\ndifferences in sequencing depth. Since all four distributions\nconsidered in our framework belong to the same family of\ndistributions, we first perform a Kolmogorov-Smirnov (KS) test\nto select genes belonging to the family of ZINB distributions.\nGenes passing the KS test will be then modeled using GLMs.\nModel selection is done by calculating the Bayesian Information\nCriterion (BIC) and likelihood ratio test (LRT) statistic.",
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