Two other approaches: Maq (Li,H. et al., 2008) and SOAPSNP (Li,R. et al., 2008) have proposed using Binomial distributions to model genotypes; however, these were developed in the context of sequencing normal genomes, not cancer genomes.
In contrast, the particularly low rate in Anesthesiology within DC (0 of 162, Table 1; 95% CI 0.0%to2.3%3% according to binomial distribution) could have been due to chance, as prevalence in this unit within IVC (1.6%) did not differ significantly.
This avoided bias in estimating the probability of surfing, as otherwise a negative binomial distribution would have had to be used to model this probability (Hogg and Tanis 1997).
Previous studies have used the beta-binomial distribution to model the count data underlying ASE measurements (e.g. Skelly et al. 2011).
Different approaches, such as Poisson distribution, quasi-Poisson distribution and negative binomial distribution, have been used to represent the statistical distribution of sequence data [ 46- 48].
It is expected that for RNA-seq data methods based on binomial distribution should have similar performance as those methods based on Poisson distribution and therefore are not considered in this paper.
A normal approximation to the binomial distribution has been used to determine the standard error in the mean.
Mixtures of the negative binomial distribution have also been widely used in connection with applications in a plethora of fields.
However, edgeR and other RNA-Seq analysis tools using the negative binomial distribution have high rates of false discovery [ 21].
For example, our binomial distribution (Bin20,0.1) has m1=2 and m2=5.8.
