Description Usage Arguments Details Value Author(s) References See Also Examples
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. Nintegration with sparse Discriminant Analysis. The method is partly based on Generalised Canonical Correlation Analysis.
1 2 3 
X 
A 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 indicating the discrete outcome of each sample. 
indY 
To be supplied if Y is missing, indicates the position of the
factor / class vector outcome in the list 
ncomp 
the number of components to include in the model. Default to 2. Applies to all blocks. 
keepX 
A 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. If 
scheme 
Either "horst", "factorial" or "centroid". Default =

mode 
character string. What type of algorithm to use, (partially)
matching one of 
scale 
boleean. If scale = TRUE, each block is standardized to zero
means and unit variances. Default = 
init 
Mode of initialization use in the algorithm, either by Singular
Value Decompostion of the product of each block of X with Y ("svd") or each
block independently ("svd.single"). Default = 
tol 
Convergence stopping value. 
max.iter 
integer, the maximum number of iterations. 
near.zero.var 
boolean, see the internal 
all.outputs 
boolean. Computation can be faster when some specific
(and nonessential) outputs are not calculated. Default = 
block.splsda
function fits a horizontal integration PLSDA 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 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 MBPLS 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
.
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 nearzero predictors information. 
iter 
Number of iterations of the algorthm for each component 
weights 
Correlation between the variate of each block and the variate of the outcome. Used to weight predictions. 
explained_variance 
Percentage of explained variance for each component and each block 
Florian Rohart, Benoit Gautier, KimAnh Lê Cao
On multiple integration with sPLSDA 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 KA. , (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), 68295.
mixOmics article:
Rohart F, Gautier B, Singh A, Lê Cao KA. mixOmics: an R package for 'omics feature selection and multiple data integration. PLoS Comput Biol 13(11): e1005752
plotIndiv
, plotArrow
,
plotLoadings
, plotVar
, predict
,
perf
, selectVar
, block.plsda
,
block.spls
and http://www.mixOmics.org/mixDIABLO for more
details and examples.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24  # block.splsda
# 
# 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(20, 2), mirna = rep(10,2), protein = rep(10, 2))
TCGA.block.splsda = block.splsda(X = data, Y = breast.TCGA$data.train$subtype,
ncomp = ncomp, keepX = list.keepX, design = design)
TCGA.block.splsda
plotIndiv(TCGA.block.splsda, ind.names = FALSE)
# illustrates coefficient weights in each block
plotLoadings(TCGA.block.splsda, ncomp = 1, contrib = 'max')
plotVar(TCGA.block.splsda, style = 'graphics', legend = TRUE)

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