edge (version 2.4.2)

dispCoxReidSplineTrend: Estimate Dispersion Trend for Negative Binomial GLMs

Description

Estimate trended dispersion parameters across multiple negative binomial generalized linear models using Cox-Reid adjusted profile likelihood.

Usage

dispCoxReidSplineTrend(y, design, offset=NULL, df = 5, subset=10000, AveLogCPM=NULL,
                       method.optim="Nelder-Mead", trace=0)
dispCoxReidPowerTrend(y, design, offset=NULL, subset=10000, AveLogCPM=NULL,
                       method.optim="Nelder-Mead", trace=0)

Arguments

y

numeric matrix of counts

design

numeric matrix giving the design matrix for the GLM that is to be fit.

offset

numeric scalar, vector or matrix giving the offset (in addition to the log of the effective library size) that is to be included in the NB GLM for the genes. If a scalar, then this value will be used as an offset for all genes and libraries. If a vector, it should be have length equal to the number of libraries, and the same vector of offsets will be used for each gene. If a matrix, then each library for each gene can have a unique offset, if desired. In adjustedProfileLik the offset must be a matrix with the same dimension as the table of counts.

df

integer giving the degrees of freedom of the spline function, see ns in the splines package.

subset

integer, number of rows to use in the calculation. Rows used are chosen evenly spaced by AveLogCPM using cutWithMinN.

AveLogCPM

numeric vector giving average log2 counts per million for each gene.

method.optim

the method to be used in optim. See optim for more detail.

trace

logical, should iteration information be output?

Value

• List containing numeric vectors dispersion and abundance containing the estimated dispersion and abundance for each gene. The vectors are of the same length as nrow(y).

Details

In the edgeR context, these are low-level functions called by estimateGLMTrendedDisp.

dispCoxReidSplineTrend and dispCoxReidPowerTrend fit abundance trends to the genewise dispersions. dispCoxReidSplineTrend fits a regression spline whereas dispCoxReidPowerTrend fits a log-linear trend of the form a*exp(abundance)^b+c. In either case, optim is used to maximize the adjusted profile likelihood (Cox and Reid, 1987).

References

Cox, DR, and Reid, N (1987). Parameter orthogonality and approximate conditional inference. Journal of the Royal Statistical Society Series B 49, 1-39.

See Also

estimateGLMTrendedDisp

Examples

design <- matrix(1,4,1)
y <- matrix((rnbinom(400,mu=100,size=5)),100,4)
d1 <- dispCoxReidSplineTrend(y, design, df=3)
d2 <- dispCoxReidPowerTrend(y, design)
with(d2,plot(AveLogCPM,sqrt(dispersion)))