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A Non-Parametric Empirical Bayes Approach For Estimating Transcript Abundance In Un-Replicated Next-Generation Sequencing Data, Sanvesh Srivastava, R. W. Doerge

Conference on Applied Statistics in Agriculture

Empirical Bayes approaches have been widely used to analyze data from high throughput sequencing devices. These approaches rely on borrowing information available for all the genes across samples to get better estimates of gene level expression. To date, transcript abundance in data from next generation sequencing (NGS) technologies has been estimated using parametric approaches for analyzing count data, namely – gamma-Poisson model, negative binomial model, and over-dispersed logistic model. One serious limitation of these approaches is they cannot be applied in absence of replication. The high cost of NGS technologies imposes a serious restriction on the number of biological replicates …