R/data-vac18.simulated.r

In mixOmicsTeam/mixOmics: Omics Data Integration Project

#' Simulated data based on the vac18 study for multilevel analysis
#' 
#' Simulated data based on the vac18 study to illustrate the use of the
#' multilevel analysis for one and two-factor analysis with sPLS-DA. This data
#' set contains the expression simulated of 500 genes.
#' 
#' In this cross-over design, repeated measurements are performed 12
#' experiments units (or unique subjects) for each of the 4 stimulations.
#' 
#' The simulation study was based on a mixed effects model (see reference for
#' details). Ten clusters of 100 genes were generated. Amongt those, 4 clusters
#' of genes discriminate the 4 stimulations (denoted LIPO5, GAG+, GAG- and NS)
#' as follows: \ -2 gene clusters discriminate (LIPO5, GAG+) versus (GAG-, NS)
#' \ -2 gene clusters discriminate LIPO5 versus GAG+, while GAG+ and NS have
#' the same effect \ -2 gene clusters discriminate GAG- versus NS, while LIPO5
#' and GAG+ have the same effect \ -the 4 remaining clusters represent noisy
#' signal (no stimulation effect) \
#' 
#' Only a subset of those genes are presented here (to save memory space).
#' 
#' @name vac18.simulated
#' @docType data
#' @usage data(vac18.simulated)
#' @format A list containing the following components: \describe{
#' \item{list("genes")}{data frame with 48 rows and 500 columns. The simulated
#' expression of 500 genes for 48 subjects.} \item{list("sample")}{a vector
#' indicating the repeated measurements on each unique subject. See Details.}
#' \item{list("stimulation")}{a factor indicating the stimulation condition on
#' each sample.} \item{list("time")}{a factor indicating the time condition on
#' each sample.} }
#' @return none
#' @references Liquet, B., Lê Cao, K.-A., Hocini, H. and Thiebaut, R. (2012). A
#' novel approach for biomarker selection and the integration of repeated
#' measures experiments from two platforms. \emph{BMC Bioinformatics}
#' \bold{13}:325.
#' @keywords datasets
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