# Gamma regression parameters describing the mean-dispersion relationship for two real datasets.

### Description

Gamma regression parameters describing the mean-dispersion relationship for each of the two real datasets considered in the associated paper, as estimated using the DESeq package version 1.8.3 (Anders and Huber, 2010).

### Usage

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### Format

List of length 2, where each list is a vector containing the two estimated coefficients (*α_0* and
*α_1*) for the gamma regression in each study (see details below).

### Details

The `dispFuncs`

object contains the estimated coefficients from the parametric gamma regressions
describing the mean-dispersion relationship for the two real datasets considered in the associated paper.
The gamma regressions were estimated using the DESeq package version 1.8.3 (Anders and Huber, 2010).

Briefly, after estimating a per-gene mean expression and dispersion values, the DESeq package fits a curve
through these estimates. These fitted values correspond to an estimation of the typical relationship between
mean expression values *μ* and dispersions *α* within a given dataset. By default, this relationship
is estimated using a gamma-family generalized linear model (GLM), where two coefficients *α_0* and *α_1*
are found to parameterize the fit as *α = α_0 + α_1 / μ*.

For the first dataset (F078), the estimated mean-dispersion relationship is described using the following gamma-family GLM:

*α = 0.024 + 14.896 / μ.*

For the second dataset (F088), the estimated mean-dispersion relationship is described using the following gamma-family GLM:

*α = 0.00557 + 1.54247 / μ.*

These gamma-family GLMs describing the mean-dispersions relationship in each of the two datasets are used in this package to simulate data using dispersion parameters that are as realistic as possible.

### References

A. Rau, G. Marot and F. Jaffrezic (2014). Differential meta-analysis of RNA-seq data. *BMC Bioinformatics* **15**:91

S. Anders and W. Huber (2010). Differential expression analysis for sequence count data.
*Genome Biology*, 11:R106.

### See Also

`sim.function`

### Examples

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