XBSeq-package


   We developed a novel algorithm, XBSeq, where a statistical model was established based on the 
   assumption that observed signals are the convolution of true expression signals and sequencing 
   noises. The mapped reads in non-exonic regions are considered as sequencing noises, which follows 
   a Poisson distribution. Given measureable observed signal and background noise from RNA-seq data, true 
   expression signals, assuming governed by the negative binomial distribution, can be delineated 
   and thus the accurate detection of differential expressed genes.


We developed a novel algorithm, XBSeq, where a statistical model was established based on the assumption that observed signals are the convolution of true expression signals and sequencing noises. The mapped reads in non-exonic regions are considered as sequencing noises, which follows a Poisson distribution. Given measureable observed and noise signals from RNA-seq data, true expression signals, assuming governed by the negative binomial distribution, can be delineated and thus the accurate detection of differential expressed genes.

Yuanhang Liu

XBSeq

Test for differential expression for RNA-seq data

XBSeq-package function

We developed a novel algorithm, XBSeq, where a statistical model was established based on the 
   assumption that observed signals are the convolution of true expression signals and sequencing 
   noises. The mapped reads in non-exonic regions are considered as sequencing noises, which follows 
   a Poisson distribution. Given measureable observed signal and background noise from RNA-seq data, true 
   expression signals, assuming governed by the negative binomial distribution, can be delineated 
   and thus the accurate detection of differential expressed genes.

XBSeq-package: Differential expression analysis of RNA sequencing data by incorporating non-exonic mapped reads

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