Description Usage Arguments Details Value Author(s) References See Also Examples
A wrapper function that invokes a specific statistical method from the ones available in package GSAR (see Rahmatallah et. al. 2014, Rahmatallah et. al. 2012 and Friedman and Rafsky 1979 for details) to test a list of gene sets in a sequential order and returns results in a list object.
1 2  TestGeneSets(object, group, geneSets=NULL, min.size=10, max.size=500,
test=NULL, nperm=1000, mst.order=1, pvalue.only=TRUE)

object 
a numeric matrix with columns and rows respectively corresponding to samples and features. 
group 
a numeric vector indicating group associations for samples. Possible values are 1 and 2. 
geneSets 
a list of character vectors providing the identifiers of features to be considered in each gene set. 
min.size 
a numeric value indicating the minimum allowed gene set size. Default value is 10. 
max.size 
a numeric value indicating the maximum allowed gene set size. Default value is 500. 
test 
a character parameter indicating which statistical method
to use for testing the gene sets. Must be one of “ 
nperm 
number of permutations used to estimate the null distribution of the test statistic. If not given, a default value 1000 is used. 
mst.order 
numeric value to indicate the consideration of the union
of the first 
pvalue.only 
logical. If 
This is a wrapper function that facilitates the use of any
statistical method in package GSAR for multiple gene sets that are
provided in a list object. The function filters out any gene that is
abscent in the considered data (input parameter object
) from
the gene sets and discard any set that is too small in size (has less
than min.size
genes) or too large (has more than
max.size
genes). The function performs the specified method
for all the remaining gene sets in a sequential order and return
results in a list object.
A list object of length equals the length of the provided gene set list.
When pvalue.only=TRUE
(default), each item in the returned list
by function TestGeneSets
consists of a numeric pvalue indicating
the attained significance level obtained by the specified method. When
pvalue.only=FALSE
, each item in the returned list is a list of
length 3 with the following components:
statistic 
the value of the observed test statistic. 
perm.stat 
numeric vector of the resulting test statistic for

p.value 
pvalue indicating the attained significance level. 
Yasir Rahmatallah and Galina Glazko
Rahmatallah Y., EmmertStreib F. and Glazko G. (2014) Gene sets net correlations analysis (GSNCA): a multivariate differential coexpression test for gene sets. Bioinformatics 30, 360–368.
Rahmatallah Y., EmmertStreib F. and Glazko G. (2012) Gene set analysis for selfcontained tests: complex null and specific alternative hypotheses. Bioinformatics 28, 3073–3080.
Friedman J. and Rafsky L. (1979) Multivariate generalization of the WaldWolfowitz and Smirnov twosample tests. Ann. Stat. 7, 697–717.
GSNCAtest
, WWtest
, MDtest
,
KStest
, RKStest
, RMDtest
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30  ## generate a feature set of size 50 in two conditions
## where each condition has 20 samples
## use multivariate normal distribution
library(MASS)
ngenes < 50
nsamples < 40
## let the mean vector have zeros of length 50 for both conditions
zero_vector < array(0,c(1,ngenes))
## set the covariance matrix to be an identity matrix for both conditions
cov_mtrx < diag(ngenes)
gp < mvrnorm(nsamples, zero_vector, cov_mtrx)
## apply a mean shift of 5 to the first 10 features under condition 1
gp[1:20,1:10] < gp[1:20,1:10] + 5
dataset < aperm(gp, c(2,1))
## assign a unique identifier to each gene
rownames(dataset) < as.character(c(1:ngenes))
## first 20 samples belong to condition 1
## second 20 samples belong to condition 2
sample.labels < c(rep(1,20),rep(2,20))
## construct 3 named gene sets such that they respectively consist of
## genes 1 to 20, 11 to 40, and 31 to 50. Notice that gene sets
## can have intersections and can be of different sizes
## Sine only the first 10 genes have a significant difference between
## the two conditions the only the first gene set (set1) returns a
## small pvalue when KStest is selected
geneSets < list("set1"=as.character(c(1:20)), "set2"=as.character(c(11:40)),
"set3"=as.character(c(31:40)))
results < TestGeneSets(object=dataset, group=sample.labels,
geneSets=geneSets, test="KStest")

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