splsda: Sparse Partial Least Squares Discriminant Analysis (sPLS-DA)

In mixOmics: Omics Data Integration Project

Description

Function to perform sparse Partial Least Squares to classify samples (supervised analysis) and select variables.

Usage

splsda(X,
Y,
ncomp = 2,
mode = c("regression", "canonical", "invariant", "classic"),
keepX,
scale = TRUE,
tol = 1e-06,
max.iter = 100,
near.zero.var = FALSE,
logratio="none", # one of "none", "CLR"
multilevel=NULL,
all.outputs = TRUE)

Arguments

X	numeric matrix of predictors. NAs are allowed.
Y	a factor or a class vector for the discrete outcome.
ncomp	the number of components to include in the model (see Details). Default is set to from one to the rank of X.
mode	character string. What type of algorithm to use, (partially) matching one of "regression", "canonical", "invariant" or "classic". See Details.
keepX	numeric vector of length ncomp, the number of variables to keep in X-loadings. By default all variables are kept in the model.
scale	boleean. If scale = TRUE, each block is standardized to zero means and unit variances (default: TRUE)
tol	Convergence stopping value.
max.iter	integer, the maximum number of iterations.
near.zero.var	boolean, 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
logratio	one of ('none','CLR') specifies the log ratio transformation to deal with compositional values that may arise from specific normalisation in sequencing dadta. Default to 'none'
multilevel	sample information for multilevel decomposition for repeated measurements. A numeric matrix or data frame indicating the repeated measures on each individual, i.e. the individuals ID. See examples.
all.outputs	boolean. Computation can be faster when some specific (and non-essential) outputs are not calculated. Default = TRUE.

Details

splsda function fits an sPLS model with 1, … ,ncomp components to the factor or class vector Y. The appropriate indicator (dummy) matrix is created. 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).

More details about the PLS modes in ?pls.

Value

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

X	the centered and standardized original predictor matrix.
Y	the centered and standardized indicator response vector or matrix.
ind.mat	the indicator matrix.
ncomp	the number of components included in the model.
keepX	number of X variables kept in the model on each component.
variates	list containing the variates.
loadings	list containing the estimated loadings for the X and Y variates.
names	list containing the names to be used for individuals and variables.
nzv	list containing the zero- or near-zero predictors information.
tol	the tolerance used in the iterative algorithm, used for subsequent S3 methods
iter	Number of iterations of the algorthm for each component
max.iter	the maximum number of iterations, used for subsequent S3 methods
scale	boolean indicating whether the data were scaled in MINT S3 methods
logratio	whether logratio transformations were used for compositional data
explained_variance	amount of variance explained per component (note that contrary to PCA, this amount may not decrease as the aim of the method is not to maximise the variance, but the covariance between X and the dummy matrix Y).
mat.c	matrix of coefficients from the regression of X / residual matrices X on the X-variates, to be used internally by predict.
defl.matrix	residual matrices X for each dimension.

Author(s)

Florian Rohart, Ignacio González, Kim-Anh Lê Cao.

References

On sPLS-DA:

Lê 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.

An overview as part of mixOmics: Rohart F, Gautier B, Singh A, Lê Cao K-A (2017) mixOmics: An R package for ‘omics feature selection and multiple data integration. PLoS Comput Biol 13(11): e1005752. https://doi.org/10.1371/journal.pcbi.1005752

On log ratio transformations:

Filzmoser, P., Hron, K., Reimann, C.: Principal component analysis for compositional data with outliers. Environmetrics 20(6), 621-632 (2009)

Lê Cao K.-A., Costello ME, Lakis VA, Bartolo, F,Chua XY, Brazeilles R, Rondeau P. MixMC: Multivariate insights into Microbial Communities. PLoS ONE, 11(8): e0160169 (2016).

On multilevel decomposition:

Westerhuis, J.A., van Velzen, E.J., Hoefsloot, H.C., Smilde, A.K.: Multivariate paired data analysis: multilevel plsda versus oplsda. Metabolomics 6(1), 119-128 (2010)

Liquet, B., Lê Cao K.-A., Hocini, H., Thiebaut, R.: A novel approach for biomarker selection and the integration of repeated measures experiments from two assays. BMC bioinformatics 13(1), 325 (2012)

See Also

spls, summary, plotIndiv, plotVar, cim, network, predict, perf, mint.block.splsda, block.splsda and http://www.mixOmics.org for more details.

Examples

## First example
data(breast.tumors)
X <- breast.tumors$gene.exp
# Y will be transformed as a factor in the function,
# but we set it as a factor to set up the colors.
Y <- as.factor(breast.tumors$sample$treatment)

res <- splsda(X, Y, ncomp = 2, keepX = c(25, 25))


# individual names appear
plotIndiv(res, ind.names = Y, legend = TRUE, ellipse =TRUE)

## Second example: one-factor analysis with sPLS-DA, selecting a subset of variables
# as in the paper Liquet et al.
#--------------------------------------------------------------
data(vac18)
X <- vac18$genes
Y <- vac18$stimulation
# sample indicates the repeated measurements
design <- data.frame(sample = vac18$sample)
Y = data.frame(stimul = vac18$stimulation)

# multilevel sPLS-DA model
res.1level <- splsda(X, Y = Y, ncomp = 3, multilevel = design,
    keepX = c(30, 137, 123))

# set up colors for plotIndiv
col.stim <- c("darkblue", "purple", "green4","red3")
plotIndiv(res.1level, ind.names = Y, col.per.group = col.stim)

## Third example: two-factor analysis with sPLS-DA, selecting a subset of variables
# as in the paper Liquet et al.
#--------------------------------------------------------------
## Not run: 
data(vac18.simulated) # simulated data

X <- vac18.simulated$genes
design <- data.frame(sample = vac18.simulated$sample)
Y = data.frame( stimu = vac18.simulated$stimulation,
                time = vac18.simulated$time)

res.2level <- splsda(X, Y = Y, ncomp = 2, multilevel = design,
keepX = c(200, 200))

plotIndiv(res.2level, group = Y$stimu, ind.names = vac18.simulated$time,
legend = TRUE, style = 'lattice')

## End(Not run)


## Fourth example: with more than two classes
# ------------------------------------------------
## Not run: 
data(liver.toxicity)
X <- as.matrix(liver.toxicity$gene)
# Y will be transformed as a factor in the function,
# but we set it as a factor to set up the colors.
Y <- as.factor(liver.toxicity$treatment[, 4])

splsda.liver <- splsda(X, Y, ncomp = 2, keepX = c(20, 20))

# individual name is set to the treatment
plotIndiv(splsda.liver, ind.names = Y, ellipse = TRUE, legend = TRUE)

## End(Not run)

## Fifth example: 16S data with multilevel decomposion and log ratio transformation
# ------------------------------------------------
## Not run: 
splsda.16S = splsda(
X = diverse.16S$data.TSS,  # TSS normalised data
Y =  diverse.16S$bodysite,
multilevel = diverse.16S$sample, # multilevel decomposition
ncomp = 2,
keepX =  c(10, 150),
logratio= 'CLR')  # CLR log ratio transformation


plotIndiv(splsda.16S, ind.names = FALSE, pch = 16, ellipse = TRUE, legend = TRUE)
#OTUs selected at the family level
diverse.16S$taxonomy[selectVar(splsda.16S, comp = 1)$name,'Family']

## End(Not run)

Example output

Loading required package: MASS
Loading required package: lattice
Loading required package: ggplot2

Loaded mixOmics 6.2.0

Visit http://www.mixOmics.org for more details about our methods.
Any bug reports or comments? Notify us at mixomics at math.univ-toulouse.fr or https://bitbucket.org/klecao/package-mixomics/issues

Thank you for using mixOmics!
Warning messages:
1: In rgl.init(initValue, onlyNULL) : RGL: unable to open X11 display
2: 'rgl_init' failed, running with rgl.useNULL = TRUE 
3: .onUnload failed in unloadNamespace() for 'rgl', details:
  call: fun(...)
  error: object 'rgl_quit' not found 
Splitting the variation for 1 level factor.
Splitting the variation for 2 level factors.
Splitting the variation for 1 level factor.
        OTU_97.38174         OTU_97.39439           OTU_97.108 
 "Flavobacteriaceae"   "Streptococcaceae"      "Neisseriaceae" 
           OTU_97.20         OTU_97.39456            OTU_97.55 
  "Burkholderiaceae"   "Streptococcaceae" "Campylobacteraceae" 
        OTU_97.29530            OTU_97.46         OTU_97.33396 
  "Streptococcaceae"      "Neisseriaceae"     "Micrococcaceae" 
         OTU_97.1893 
    "Micrococcaceae" 

mixOmics documentation built on June 1, 2018, 5:06 p.m.