mint.pca: P-integration with Principal Component Analysis

In mixOmicsTeam/mixOmics: Omics Data Integration Project

P-integration with Principal Component Analysis

Description

Function to integrate and combine multiple independent studies measured on the same variables or predictors (P-integration) using a multigroup Principal Component Analysis.

Usage

mint.pca(
  X,
  ncomp = 2,
  study,
  scale = TRUE,
  tol = 1e-06,
  max.iter = 100,
  verbose.call = FALSE
)

Arguments

X	numeric matrix of predictors combining multiple independent studies on the same set of predictors. NAs are allowed.
ncomp	Number of components to include in the model (see Details). Default to 2
study	factor indicating the membership of each sample to each of the studies being combined
scale	Logical. 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.
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

mint.pca fits a vertical PCA model with ncomp components in which several independent studies measured on the same variables are integrated. The study factor indicates the membership of each sample in each study. We advise to only combine studies with more than 3 samples as the function performs internal scaling per study.

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

Useful graphical outputs are available, e.g. plotIndiv, plotLoadings, plotVar.

Value

mint.pca returns an object of class "mint.pca", "pca", a list that contains the following components:

X	the centered and standardized original predictor matrix.
ncomp	the number of components included in the model.
study	The study grouping factor
sdev	the eigenvalues of the covariance/correlation matrix, though the calculation is actually done with the singular values of the data matrix or by using NIPALS.
center, scale	the centering and scaling used, or FALSE.
rotation	the matrix of variable loadings (i.e., a matrix whose columns contain the eigenvectors).
loadings	same as 'rotation' to keep the mixOmics spirit
x	the value of the rotated data (the centred (and scaled if requested) data multiplied by the rotation/loadings matrix), also called the principal components.
variates	same as 'x' to keep the mixOmics spirit
prop_expl_var	Proportion of the explained variance from the multivariate model after setting possible missing values to zero in the data.
names	list containing the names to be used for individuals and variables.
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, Kim-Anh Lê Cao, Al J Abadi

References

Rohart F, Eslami A, Matigian, N, Bougeard S, Lê Cao K-A (2017). MINT: A multivariate integrative approach to identify a reproducible biomarker signature across multiple experiments and platforms. BMC Bioinformatics 18:128.

Eslami, A., Qannari, E. M., Kohler, A., and Bougeard, S. (2014). Algorithms for multi-group PLS. J. Chemometrics, 28(3), 192-201.

See Also

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

Examples

data(stemcells)

res = mint.pca(X = stemcells$gene, ncomp = 3,
study = stemcells$study)

plotIndiv(res, group = stemcells$celltype, legend=TRUE)

mixOmicsTeam/mixOmics documentation built on Dec. 3, 2024, 11:15 p.m.