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.
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.
htmlpdf
(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.
htmlpdf
(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")
Results
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(globalSeq)
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/globalSeq/proprius.Rd_%03d_medium.png", width=480, height=480)
> ### Name: proprius
> ### Title: Decomposition
> ### Aliases: proprius
> ### Keywords: methods
>
> ### ** 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")
[1] -1.3567212 -0.5201458 -1.0126671 0.7870157 -1.1039208 0.3675273
[7] -1.6114741 -1.5498190 -1.1134177 0.3722417 1.6947976 -1.3683284
[13] -1.7695083 -2.0365125 -1.1901664 -0.2313564 1.2302413 -1.2285466
[19] -1.8233707 -1.8346955 -1.3945066 -0.8868730 -1.3096920 -1.2489024
[25] 1.1394360 -0.6869264 -1.9868579 -1.5406372 24.1076220 3.5542340
> proprius(y,X,type="covariates")
[1] 0.84550018 -0.01513445 -0.27117459 0.01821597 -0.12300187 -0.31693631
[7] -0.32056413 -0.57318360 -0.32912088 0.51803521 -0.43921767 -0.24243664
[13] -0.48524058 -0.42533687 -0.62583602 -0.08437057 0.13610674 0.12563903
[19] -0.49231860 -0.23040041 -0.45424874 0.12828120 0.36343064 -0.34336870
[25] -0.35957062 1.08730627 0.73845091 -0.31464951 -0.17449195 1.01096262
[31] 0.95386152 0.38579172 1.19353649 -0.12029903 -0.06347265 -0.24744725
[37] 0.44277813 -0.39403825 -0.37817400 0.95925079 -0.38768200 -0.22611421
[43] 0.32439396 0.06564774 -0.64392345 -0.24761051 -0.32846393 0.11069493
[49] -0.24770544 -0.57788636 -0.03804907 -0.47142164 -0.26815831 0.17231223
[55] 0.35719293 1.08604523 -0.36095299 -0.32494913 -0.30752131 -0.19480072
[61] -0.06466868 -0.33219776 1.01368264 -0.21156284 -0.23820781 3.30012922
[67] -0.57396646 4.84411670 -0.07969188 0.04110268 -0.47712917 1.69586457
[73] -0.15856122 -0.04271274 0.42774209 -0.27587226 -0.14833885 -0.41330645
[79] -0.27033254 -0.13942205 -0.04957926 1.76549609 -0.46838201 -0.46512151
[85] -0.29554955 -0.45505308 -0.25772613 -0.74533811 0.91903469 -0.05575209
[91] 0.41640106 -0.09516932 -0.35939054 0.09766040 0.11255990 -0.24257242
[97] -0.21531603 -0.50589800 -0.60256936 -0.52049407
>
>
>
>
>
>
> dev.off()
null device
1
>