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DESeq utilizes a negative binomial distribution for modeling read counts per miRNA and implements a method for normalizing the counts.

DESeq utilizes a negative binomial distribution for modeling read counts per gene and implements a method for normalizing the counts [ 63].

DESeq utilizes a negative binomial distribution for modelling read counts [ 42].

DESeq utilizes a negative binomial distribution for modelling read counts per transcript and implements a method for normalizing the counts.

Some of these methods assume that the count distribution of individual genes follows a Poisson distribution, whereas others assume a negative binomial distribution for their model.

It uses a negative binomial distribution to model total variation.

The final models employed a negative binomial distribution for germination date, Poisson distributions for herbivory score and stem count, and a log-linear link for each model.

Both programs assume a negative binomial distribution for the count data, but differ in their models for estimating the distribution parameters from the data.

* p-values are derived from Generalized Linear Models (assuming a gamma distribution for costs and a binomial distribution for the probability of use).

For the rest of the responses measured, generalized linear models for binary data assuming a Binomial distribution for data and logit as link function were used.

All GLLAMM models were fitted using a multi-nominal logit link function and binomial distribution for the binary response.

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