16S rRNA sequence data is oftentimes extremely sparse, making it difficult to normalize properly for differential abundance analyses with current RNASeq methods, such as DESeq, edgeR, and metagenomeSeq. The count adjustment methods included here can be used to ameliorate these effects. The GoodTuring adjustment is derived from from GoodTuring frequency estimation methods. Code to generate simulated test data according to the Dirichletmultinomial model from two different sets of parameters is also included. Further information on the methods and data can be found *IN THIS PAPER*.
Package details 


Maintainer  Who to complain to <[email protected]> 
License  GPL3 
Version  0.5 
Package repository  View on GitHub 
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