spca: Sparse Principal Components Analysis
In ajabadi/mixOmics2: Omics Data Integration Project

Description Usage Arguments Value Author(s) See Also Examples

Performs a sparse principal components analysis to perform variable selection by using singular value decomposition.

The calculation employs singular value decomposition of the (centered and scaled) data matrix and LASSO to generate sparsity on the loading vectors.

scale= TRUE is highly recommended as it will help obtaining orthogonal sparse loading vectors.

keepX is the number of variables to keep in loading vectors. The difference between number of columns of X and keepX is the degree of sparsity, which refers to the number of zeros in each loading vector.

Note that spca does not apply to the data matrix with missing values. The biplot function for spca is not available.

According to Filzmoser et al., a ILR log ratio transformation is more appropriate for PCA with compositional data. Both CLR and ILR are valid.

Logratio transform and multilevel analysis are performed sequentially as internal pre-processing step, through logratio.transfo and withinVariation respectively.

Logratio can only be applied if the data do not contain any 0 value (for count data, we thus advise the normalise raw data with a 1 offset). For ILR transformation and additional offset might be needed.

{spca}{default}(X, assay=NULL, ncomp = 2, center = TRUE, scale = TRUE,
keepX = rep(ncol(X), ncomp), max.iter = 500, tol = 1e-06, logratio = 'none',
multilevel = NULL)

## S4 method for signature 'ANY'
X, ncomp = 2, center = TRUE, scale = TRUE,
keepX = repspca(ncol(X), ncomp),max.iter = 500, tol = 1e-06,
logratio = c('none','CLR'), multilevel = NULL)

## S4 method for signature 'MultiAssayExperiment'
spca(X, ncomp = 2, ..., assay = NULL)

`X`	A numeric matrix (or data frame) which provides the data for the principal components analysis. It can contain missing values. Alternatively, a `MultiAssayExperiment` object.
`ncomp`	Integer, if data is complete `ncomp` decides the number of components and associated eigenvalues to display from the `pcasvd` algorithm and if the data has missing values, `ncomp` gives the number of components to keep to perform the reconstitution of the data using the NIPALS algorithm. If `NULL`, function sets `ncomp = min(nrow(X), ncol(X))`.
`...`	currently ignored.
`assay`	Name or index of an assay from `X`.
`keepX`	numeric vector of length ncomp, the number of variables to keep.
`logratio`	one of ('none','CLR'). Specifies the log ratio transformation to deal with compositional values that may arise from specific normalisation in sequencing data. Default to 'none'. in loading vectors. By default all variables are kept in the model. See details.
`...`	aguments passed to the generic.

spca returns a list with class "spca" containing the following components:

`ncomp`	the number of components to keep in the calculation.
`varX`	the adjusted cumulative percentage of variances explained.
`keepX`	the number of variables kept in each loading vector.
`iter`	the number of iterations needed to reach convergence for each component.
`rotation`	the matrix containing the sparse loading vectors.
`x`	the matrix containing the principal components.

Ignacio Gonzalez, Kim-Anh Lê Cao, Fangzhou Yao, Al J Abadi

pca, ipca, selectVar, plotIndiv, plotVar and http://www.mixOmics.org for more details.

#' \dontrun{
## successful: TRUE

library(mixOmics.data)
spca.rat <- spca(liver.toxicity$gene, ncomp = 3, keepX = rep(50, 3))
spca.rat

## variable representation
plotVar(spca.rat, cex=2)

plotVar(spca.rat,style="3d")
# }

# example with MultiAssayExperiment class
# --------------------------------

spca.rat <- spca(liver.toxicity.mae, assay='gene', ncomp = 3, keepX = rep(50, 3))
spca.rat

# \dontrun{


## samples representation
plotIndiv(spca.rat, ind.names = liver.toxicity$treatment[, 3],
          group = as.numeric(liver.toxicity$treatment[, 3]))
plotIndiv(spca.rat, cex = 0.01,
          col = as.numeric(liver.toxicity$treatment[, 3]),style="3d")


# example with multilevel decomposition and CLR log ratio transformation
# ----------------

data("diverse.16S")
pca.res = pca(X = diverse.16S$data.TSS, ncomp = 5,
              logratio = 'CLR', multilevel = diverse.16S$sample)
plot(pca.res)
plotIndiv(pca.res, ind.names = FALSE, group = diverse.16S$bodysite, title = '16S diverse data',
          legend=TRUE)

  #' }