Dear all, 

Paper describing the mathematical details about tweeDEseq package has been published at BMC Bioinformatics 

http://www.biomedcentral.com/1471-2105/14/254/abstract 

The paper also compares the performance of tweeDEseq against edgeR and DESeq under a large number of scenarios. The results obtained from simulations and real data analysis show that tweeDEseq accurately detects differentially expressed genes with improved performance and reproducibility over the previously compared methodologies. Results obtained from microarrays were used to check for reproducibility. 

In summary, tweeDEseq provides a better fit to the underlying biological variability. This may be critical when comparing groups of RNA-seq samples with markedly different count data distributions with different oddities including zero-inflation (e.g, more zeros than expected under negative binomial assumption) or heavy tail (e.g, outliers - individuals with large number of counts) among others. 

Best regards, 
Juan R Gonzalez, PhD 
Bioinformatic Research Group in Epidemiology 
Center for Research in Environmental Epidemiology (CREAL) 
www.creal.cat/jrgonzalez/software.htm 



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