block.splsda: N-integration and feature selection with Projection to Latent...
In mixOmicsTeam/mixOmics: Omics Data Integration Project

block.splsda

R Documentation

N-integration and feature selection with Projection to Latent Structures models (PLS) with sparse Discriminant Analysis

Description

Integration of multiple data sets measured on the same samples or observations to classify a discrete outcome to classify a discrete outcome and select features from each data set, ie. N-integration with sparse Discriminant Analysis. The method is partly based on Generalised Canonical Correlation Analysis.

Usage

block.splsda(
  X,
  Y,
  indY,
  ncomp = 2,
  keepX,
  design,
  scheme,
  scale = TRUE,
  init = "svd",
  tol = 1e-06,
  max.iter = 100,
  near.zero.var = FALSE,
  all.outputs = TRUE,
  verbose.call = FALSE
)

wrapper.sgccda(
  X,
  Y,
  indY,
  ncomp = 2,
  keepX,
  design,
  scheme,
  scale = TRUE,
  init = "svd",
  tol = 1e-06,
  max.iter = 100,
  near.zero.var = FALSE,
  all.outputs = TRUE,
  verbose.call = FALSE
)

Arguments

`X`	A named list of data sets (called 'blocks') measured on the same samples. Data in the list should be arranged in matrices, samples x variables, with samples order matching in all data sets.
`Y`	a factor or a class vector for the discrete outcome.
`indY`	To supply if `Y` is missing, indicates the position of the matrix response in the list `X`.
`ncomp`	the number of components to include in the model. Default to 2. Applies to all blocks.
`keepX`	A named list of same length as X. Each entry is the number of variables to select in each of the blocks of X for each component. By default all variables are kept in the model.
`design`	numeric matrix of size (number of blocks in X) x (number of blocks in X) with values between 0 and 1. Each value indicates the strenght of the relationship to be modelled between two blocks; a value of 0 indicates no relationship, 1 is the maximum value. Alternatively, one of c('null', 'full') indicating a disconnected or fully connected design, respecively, or a numeric between 0 and 1 which will designate all off-diagonal elements of a fully connected design (see examples in `block.splsda`). If `Y` is provided instead of `indY`, the `design` matrix is changed to include relationships to `Y`.
`scheme`	Character, one of 'horst', 'factorial' or 'centroid'. Default = `'horst'`, see reference.
`scale`	Logical. If scale = TRUE, each block is standardized to zero means and unit variances (default: TRUE)
`init`	Mode of initialization use in the algorithm, either by Singular Value Decomposition of the product of each block of X with Y ('svd') or each block independently ('svd.single'). Default = `svd.single`
`tol`	Positive numeric used as convergence criteria/tolerance during the iterative process. Default to `1e-06`.
`max.iter`	Integer, the maximum number of iterations. Default to 100.
`near.zero.var`	Logical, see the internal `nearZeroVar` function (should be set to TRUE in particular for data with many zero values). Setting this argument to FALSE (when appropriate) will speed up the computations. Default value is FALSE.
`all.outputs`	Logical. Computation can be faster when some specific (and non-essential) outputs are not calculated. Default = `TRUE`.
`verbose.call`	Logical (Default=FALSE), if set to TRUE then the `$call` component of the returned object will contain the variable values for all parameters. Note that this may cause large memory usage.

Details

block.splsda function fits a horizontal integration PLS-DA model with a specified number of components per block). A factor indicating the discrete outcome needs to be provided, either by Y or by its position indY in the list of blocks X.

X can contain missing values. Missing values are handled by being disregarded during the cross product computations in the algorithm block.pls without having to delete rows with missing data. Alternatively, missing data can be imputed prior using the impute.nipals function.

The type of algorithm to use is specified with the mode argument. Four PLS algorithms are available: PLS regression ("regression"), PLS canonical analysis ("canonical"), redundancy analysis ("invariant") and the classical PLS algorithm ("classic") (see References and ?pls for more details).

Note that our method is partly based on sparse Generalised Canonical Correlation Analysis and differs from the MB-PLS approaches proposed by Kowalski et al., 1989, J Chemom 3(1), Westerhuis et al., 1998, J Chemom, 12(5) and sparse variants Li et al., 2012, Bioinformatics 28(19); Karaman et al (2014), Metabolomics, 11(2); Kawaguchi et al., 2017, Biostatistics.

Variable selection is performed on each component for each block of X if specified, via input parameter keepX.

Value

block.splsda returns an object of class "block.splsda", "block.spls", a list that contains the following components:

`X`	the centered and standardized original predictor matrix.
`indY`	the position of the outcome Y in the output list X.
`ncomp`	the number of components included in the model for each block.
`mode`	the algorithm used to fit the model.
`keepX`	Number of variables used to build each component of each block
`variates`	list containing the variates of each block of X.
`loadings`	list containing the estimated loadings for the variates.
`names`	list containing the names to be used for individuals and variables.
`nzv`	list containing the zero- or near-zero predictors information.
`iter`	Number of iterations of the algorithm for each component
`weights`	Correlation between the variate of each block and the variate of the outcome. Used to weight predictions.
`prop_expl_var`	Percentage of explained variance for each component and each block
`call`	if `verbose.call = FALSE`, then just the function call is returned. If `verbose.call = TRUE` then all the inputted values are accessable via this component

Author(s)

Florian Rohart, Benoit Gautier, Kim-Anh Lê Cao, Al J Abadi

References

On multiple integration with sPLS-DA and 4 data blocks:

Singh A., Gautier B., Shannon C., Vacher M., Rohart F., Tebbutt S. and Lê Cao K.A. (2016). DIABLO: multi omics integration for biomarker discovery. BioRxiv available here: http://biorxiv.org/content/early/2016/08/03/067611

On data integration:

Tenenhaus A., Philippe C., Guillemot V, Lê Cao K.A., Grill J, Frouin V. Variable selection for generalized canonical correlation analysis. Biostatistics. kxu001

Gunther O., Shin H., Ng R. T. , McMaster W. R., McManus B. M. , Keown P. A. , Tebbutt S.J. , Lê Cao K-A. , (2014) Novel multivariate methods for integration of genomics and proteomics data: Applications in a kidney transplant rejection study, OMICS: A journal of integrative biology, 18(11), 682-95.

mixOmics article:

Rohart F, Gautier B, Singh A, Lê Cao K-A. mixOmics: an R package for 'omics feature selection and multiple data integration. PLoS Comput Biol 13(11): e1005752

Examples

# block.splsda
# -------------
data("breast.TCGA")
# this is the X data as a list of mRNA, miRNA and proteins
data = list(mrna = breast.TCGA$data.train$mrna, mirna = breast.TCGA$data.train$mirna,
protein = breast.TCGA$data.train$protein)
# set up a full design where every block is connected
design = matrix(1, ncol = length(data), nrow = length(data),
dimnames = list(names(data), names(data)))
diag(design) =  0
design
# set number of component per data set
ncomp = c(2)
# set number of variables to select, per component and per data set (this is set arbitrarily)
list.keepX = list(mrna = rep(8,2), mirna = rep(8,2), protein = rep(8,2))


TCGA.block.splsda = block.splsda(X = data, Y = breast.TCGA$data.train$subtype, 
                                 ncomp = ncomp, keepX = list.keepX, design = design)
## use design = 'full'
TCGA.block.splsda = block.splsda(X = data, Y = breast.TCGA$data.train$subtype, 
                                 ncomp = ncomp, keepX = list.keepX, design = 'full')
TCGA.block.splsda$design

plotIndiv(TCGA.block.splsda, ind.names = FALSE)
## use design = 'null'
TCGA.block.splsda = block.splsda(X = data, Y = breast.TCGA$data.train$subtype, 
                                 ncomp = ncomp, keepX = list.keepX, design = 'null')
TCGA.block.splsda$design
## set all off-diagonal elements to 0.5
TCGA.block.splsda = block.splsda(X = data, Y = breast.TCGA$data.train$subtype, 
                                 ncomp = ncomp, keepX = list.keepX, design = 0.5)
TCGA.block.splsda$design
# illustrates coefficient weights in each block
plotLoadings(TCGA.block.splsda, ncomp = 1, contrib = 'max')
plotVar(TCGA.block.splsda, style = 'graphics', legend = TRUE)

## plot markers (selected variables) for mrna and mirna
# mrna: show each selected feature separately
plotMarkers(object = TCGA.block.splsda, comp = 1, block = 'mrna')
# mrna: aggregate all selected features and separate by loadings signs
plotMarkers(object = TCGA.block.splsda, comp = 1, block = 'mrna', global = TRUE)
# proteins
plotMarkers(object = TCGA.block.splsda, comp = 1, block = 'protein')
## do not show violin plots
plotMarkers(object = TCGA.block.splsda, comp = 1, block = 'protein', violin = FALSE)
# show top 5 markers
plotMarkers(object = TCGA.block.splsda, comp = 1, block = 'protein', markers = 1:5)
# show specific markers
my.markers <- selectVar(TCGA.block.splsda, comp = 1)[['protein']]$name[c(1,3,5)]
my.markers
plotMarkers(object = TCGA.block.splsda, comp = 1, block = 'protein', markers = my.markers)

mixOmicsTeam/mixOmics documentation built on Nov. 4, 2024, 8:56 a.m.