R: Gamma regression parameters describing the mean-dispersion...

dispFuncs {metaRNASeq} | R Documentation

## 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

    data(dispFuncs)

### 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

    data(dispFuncs)

[Package metaRNASeq version 1.0.2 Index]