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At low prevalence the distributions can be described by the negative binomial distribution, whereas at high prevalence lice tend to be normally distributed.
The gene count is also modeled by the Negative Binomial distribution.
Significant changes in tag expression among groups were detected by the negative binomial GLM test in DESeq.
Here the approximation may fail because the dispersion (variance divided by the mean) of the distribution is too large to be accurately modeled by the negative binomial.
Data is changes in percent of genes called methylated by the negative binomial method at a false discovery rate of 10%.
To distinguish the systematic changes in expression between conditions from noise, the counts are frequently modeled by the Negative Binomial distribution.
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High-throughput data analysis requires accurate prediction of variability within the dynamic range of values and a suitable error model and DESeq attempts to achieve them by using the negative binomial distribution with the variance and mean linked by local regression.
Symbiotic endogeneity has been empirically identified by iterating the negative binomial regressions.
This possibility must be examined by iterating the negative binomial regression analyses to account for all possible combinations.
However, this problem is overcome by estimating the negative binomial regression (NBR) model, in which a cross-section heterogeneity is naturally formulated by introducing an unobserved effect into the conditional mean (Greene 2002).
BayesPeak addresses this issue by using the negative binomial distribution.
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