omnibus: Omnibus test
In rauschenberger/globalSeq: Global Test for Counts

Description Usage Arguments Details Value References See Also Examples

View source: R/user_functions.R

Test of association between a count response and one or more covariate sets. This test may be conceptualised as a test of overall significance in regression analysis, where the response variable is overdispersed, and where the number of explanatory variables (p) exceeds the sample size (n). The negative binomial distribution accounts for overdispersion and a random effect model accounts for high dimensionality (p>>n).

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omnibus(y, X, offset = NULL, group = NULL,
        mu = NULL, phi = NULL,
        perm = 1000, kind = 1)

`y`	response variable: numeric vector of length `n`
`X`	one covariate set: numeric matrix with `n` rows (samples) and `p` columns (covariates); multiple covariate sets: list of numeric matrices with `n` rows (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
`perm`	number of iterations: positive integer
`kind`	computation : number between 0 and 1

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. By default the offset is rep(1,n).

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.

Efficient alternatives to classical permutation (kind=1) are the method of control variates (kind=0) and permutation in chunks (0 < kind < 1) details.

The function returns a dataframe, with the p-value in the first column, and the test statistic in the second column.

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)

RX Menezes, L Mohammadi, JJ Goeman, and JM Boer (2016). "Analysing multiple types of molecular profiles simultaneously: connecting the needles in the haystack", BMC Bioinformatics. 17:77. html pdf (open access)

S le Cessie, and HC van Houwelingen (1995). "Testing the fit of a regression model via score tests in random effects models", Biometrics. 51:600-614. html pdf (restricted access)

The function proprius calculates the contributions of individual samples or covariates to the test statistic. 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.

# 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)

# hypothesis testing
omnibus(y,X)