proprius: Decomposition

In rauschenberger/globalSeq: Global Test for Counts

Description

Even though the function omnibus tests a single hypothesis on a whole covariate set, this function allows to calculate the individual contributions of n samples or p covariates to the test statistic.

Usage

proprius(y, X, type, offset = NULL, group = NULL,
        mu = NULL, phi = NULL,
        alpha = NULL, perm = 1000, plot = TRUE)

Arguments

y	response variable: numeric vector of length n
X	covariate set: numeric matrix with n rows (samples) and p columns (covariates)
type	character 'covariates' or 'samples'
offset	numeric vector of length n
group	confounding variable: factor of length n
mu	mean parameters: numeric vector of length 1 or n
phi	dispersion parameter: non-negative real number
alpha	significance level: real number between 0 and 1
perm	number of iterations: positive integer
plot	plot of results: logical

Details

The user can provide a common mu for all samples or sample-specific mu, and a common phi. Setting phi equal to zero is equivalent to using the Poisson model. If mu is missing, then mu is estimated from y. If phi is missing, then mu and phi are estimated from y. The offset is only taken into account for estimating mu or phi.

The user can provide the confounding variable group. Note that each level of group must appear at least twice in order to allow stratified permutations.

Value

If alpha=NULL, then the function returns a numeric vector, and else a list of numeric vectors.

References

A Rauschenberger, MA Jonker, MA van de Wiel, and RX Menezes (2016). "Testing for association between RNA-Seq and high-dimensional data", BMC Bioinformatics. 17:118. html pdf (open access)

JJ Goeman, SA van de Geer, F de Kort, and HC van Houwelingen (2004). "A global test for groups of genes: testing association with a clinical outcome", Bioinformatics. 20:93-99. html pdf (open access)

See Also

The function omnibus tests for associations between an overdispersed response variable and a high-dimensional covariate set. The function cursus tests for association between RNA-Seq and local genetic or epigenetic alternations across the whole genome. All other functions of the R package globalSeq are internal.

Examples

# simulate high-dimensional data
n <- 30; p <- 100
y <- rnbinom(n,mu=10,size=1/0.25)
X <- matrix(rnorm(n*p),nrow=n,ncol=p)

# decomposition
proprius(y,X,type="samples")
proprius(y,X,type="covariates")

rauschenberger/globalSeq documentation built on May 19, 2020, 4:09 a.m.