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.
proprius(y, X, type, offset = NULL, group = NULL, mu = NULL, phi = NULL, alpha = NULL, perm = 1000, plot = TRUE)
y | response variable:
numeric vector of length |
---|---|
X | covariate set:
numeric matrix with |
type | character 'covariates' or 'samples' |
offset | numeric vector of length |
group | confounding variable:
factor of length |
mu | mean parameters:
numeric vector of length |
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 |
If alpha=NULL
, then the function returns a numeric vector,
and else a list of numeric vectors.
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.
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
.
# 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] -1.473829651 -1.621989536 0.389933792 -1.083847301 1.892962633 #> [6] -0.960717613 1.721503713 -0.122347060 2.878808190 -1.212848450 #> [11] 0.265357110 -1.419499667 -0.945988913 -1.218230787 -1.565611811 #> [16] 1.924302618 -0.749555757 -1.266216291 -0.778061491 -0.009607322 #> [21] -1.164619359 -1.077795751 -1.272294761 -1.232923857 -1.496262593 #> [26] -0.515822170 2.721173151 -1.648988791 0.638509691 -1.424787341proprius(y,X,type="covariates")#> [1] -0.061952584 0.592890632 -0.092819492 0.084168673 -0.397585279 #> [6] -0.183539882 0.031889996 -0.426508046 -0.259698391 -0.265361798 #> [11] -0.317232615 0.072810328 -0.239941836 -0.245169345 -0.365328320 #> [16] -0.167861221 0.019013502 -0.293306936 -0.074805315 -0.269415642 #> [21] 0.270934190 -0.086943070 -0.619497569 0.755689349 -0.227954044 #> [26] -0.205152081 -0.223616292 -0.376515968 -0.197909498 0.272830697 #> [31] -0.417295924 -0.572140052 -0.139501582 -0.418759569 -0.195663061 #> [36] -0.188234195 -0.521401610 -0.415634949 -0.346467928 -0.349298405 #> [41] -0.270644797 -0.058625362 -0.435316923 -0.261076519 -0.314005429 #> [46] -0.376683458 -0.544577063 0.139635838 -0.452980287 0.660662887 #> [51] -0.284095283 -0.529192814 -0.293848069 -0.152128001 -0.451823530 #> [56] 0.598375469 0.024277366 0.386251570 0.118416105 -0.275617185 #> [61] -0.243857126 -0.235047343 0.008511704 -0.275980122 -0.173835916 #> [66] -0.270343598 -0.433204607 -0.301079993 -0.452220868 -0.437245563 #> [71] 0.909077707 1.224714475 0.035826449 -0.203264859 1.192802248 #> [76] 0.094335123 -0.245741028 0.620533156 -0.302885587 -0.508894499 #> [81] -0.331829320 -0.218798231 -0.360255918 -0.162935165 -0.323970670 #> [86] -0.143899832 -0.247114748 -0.161513535 -0.044980916 -0.136249689 #> [91] 0.648363088 0.604569515 0.332039489 -0.302868514 -0.468019955 #> [96] -0.131218846 -0.247416189 -0.281275540 -0.009120144 -0.009719390