Description Details Author(s) References Examples
Applicable to any classification problem with more than 2 classes. It relies on bootstrap subsamplings of sPLS-DA and provides tools to select the most stable variables (defined as the ones consistently selected over the bootstrap subsamplings) and to predict the class of test samples.
The DESCRIPTION file:
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The package implements the methodology described in Rohart et al. (2016) for identifying genes that differentiate Mesemchymal Stromal Cells from other cell types. The method is applicable to any classification problem with more than 2 classes. It relies on bootstrap subsamplings of sPLS-DA and provides tools to select the most stable variables (defined as the ones consistently selected over the bootstrap subsamplings) and to predict the class of test samples.
Three major functions:
*bootsPLS
performs a tune.splsda
of the mixOmics
package on several random subsamplings. It records the selected variables on each replication. The compile.bootsPLS.object
function is to be used if several calls to bootsPLS
are made.
*fit.model
fits a constraint spls (see spls.hybrid
) on the most stable variables. The variables can be automatically selected by setting auto.tune=TRUE.
*prediction
predicts the class of test samples and compute Confidence Interval (CI) of the prediction values.
Florian Rohart [aut, cre], Kim-Anh Le Cao [boss], Christine Wells [boss]
Maintainer: Florian Rohart <florian.rohart@gmail.com>
Rohart et al. (2016). A Molecular Classification of Human Mesenchymal Stromal Cells. PeerJ, DOI 10.7717/peerj.1845
Le Cao, K.-A., Boitard, S. and Besse, P. (2011). Sparse PLS Discriminant Analysis: biologically relevant feature selection and graphical displays for multiclass problems. BMC Bioinformatics 12:253.
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data(MSC)
X=MSC$X
Y=MSC$Y
boot1=bootsPLS(X=X,Y=Y,ncomp=3,many=5,kCV=5)
boot2=bootsPLS(X=X,Y=Y,ncomp=3,many=5,kCV=5,showProgress=FALSE)
boot3=bootsPLS(X=X,Y=Y,ncomp=3,many=5,kCV=5,showProgress=FALSE, cpus=2)
# construct a list of bootsPLS object
bootsPLS.object=list(boot1,boot2,boot3)
# compile the outputs in one bootsPLS object
boot=compile.bootsPLS.object(bootsPLS.object)
# fit the model
fit=fit.model(boot,auto.tune=TRUE) #tuning number of components and variables
fit=fit.model(boot,ncomp=2) #tuning number of variables on the 2components
plotIndiv(fit,ind.names=FALSE, legend=TRUE)
# prediction and Confidence Interval, here we use the same dataset but it should be external data
pred=prediction(fit,X.test=X,CI=TRUE)
head(pred$Y.hat.test[,,"comp.1"])
lapply(pred$out.CI$CI$'comp.1',head)
## End(Not run)
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