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).
data(dispFuncs)List of length 2, where each list is a vector containing the two estimated coefficients (\(\alpha_0\) and \(\alpha_1\)) for the gamma regression in each study (see details below).
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 \(\mu\) and dispersions \(\alpha\) within a given dataset. By default, this relationship is estimated using a gamma-family generalized linear model (GLM), where two coefficients \(\alpha_0\) and \(\alpha_1\) are found to parameterize the fit as \(\alpha = \alpha_0 + \alpha_1 / \mu\).
For the first dataset (F078), the estimated mean-dispersion relationship is described using the following gamma-family GLM: $$\alpha = 0.024 + 14.896 / \mu.$$
For the second dataset (F088), the estimated mean-dispersion relationship is described using the following gamma-family GLM: $$\alpha = 0.00557 + 1.54247 / \mu.$$
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.
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.
# NOT RUN {
data(dispFuncs)
# }
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