# proprius: Decomposition In 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

 ```1 2 3``` ```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)

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`.
 ```1 2 3 4 5 6 7 8``` ```# 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") ```