ipca: Independent Principal Component Analysis

In mixOmics: Omics Data Integration Project

Description

Performs independent principal component analysis on the given data matrix, a combination of Principal Component Analysis and Independent Component Analysis.

Usage

ipca(X,
ncomp = 2,
mode = "deflation",
fun = "logcosh",
scale = FALSE,
w.init = NULL,
max.iter = 200,
tol = 1e-04)

Arguments

X	a numeric matrix (or data frame) which provides the data for the principal component analysis.
ncomp	integer, number of independent component to choose. Set by default to 3.
mode	character string. What type of algorithm to use when estimating the unmixing matrix, choose one of "deflation", "parallel". Default set to deflation.
fun	the function used in approximation to neg-entropy in the FastICA algorithm. Default set to logcosh, see details of FastICA.
scale	a logical value indicating whether the variables (columns) of the data matrix X should be standardized beforehand. By default, X is centered.
max.iter	integer, maximum number of iterations to perform.
tol	a positive scalar giving the tolerance at which the un-mixing matrix is considered to have converged, see fastICA package.
w.init	initial un-mixing matrix (unlike FastICA, this matrix is fixed here).

Details

In PCA, the loading vectors indicate the importance of the variables in the principal components. In large biological data sets, the loading vectors should only assign large weights to important variables (genes, metabolites ...). That means the distribution of any loading vector should be super-Gaussian: most of the weights are very close to zero while only a few have large (absolute) values.

However, due to the existence of noise, the distribution of any loading vector is distorted and tends toward a Gaussian distribtion according to the Central Limit Theroem. By maximizing the non-Gaussianity of the loading vectors using FastICA, we obtain more noiseless loading vectors. We then project the original data matrix on these noiseless loading vectors, to obtain independent principal components, which should be also more noiseless and be able to better cluster the samples according to the biological treatment (note, IPCA is an unsupervised approach).

Algorithm 1. The original data matrix is centered.

2. PCA is used to reduce dimension and generate the loading vectors.

3. ICA (FastICA) is implemented on the loading vectors to generate independent loading vectors.

4. The centered data matrix is projected on the independent loading vectors to obtain the independent principal components.

Value

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

ncomp	the number of independent principal components used.
unmixing	the unmixing matrix of size (ncomp x ncomp)
mixing	the mixing matrix of size (ncomp x ncomp)
X	the centered data matrix
x	the indepenent principal components
loadings	the independent loading vectors
kurtosis	the kurtosis measure of the independent loading vectors

Author(s)

Fangzhou Yao and Jeff Coquery.

References

Yao, F., Coquery, J. and Lê Cao, K.-A. (2011) Principal component analysis with independent loadings: a combination of PCA and ICA. (in preparation)

A. Hyvarinen and E. Oja (2000) Independent Component Analysis: Algorithms and Applications, Neural Networks, 13(4-5):411-430

J L Marchini, C Heaton and B D Ripley (2010). fastICA: FastICA Algorithms to perform ICA and Projection Pursuit. R package version 1.1-13.

See Also

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

Examples

data(liver.toxicity)

# implement IPCA on a microarray dataset
ipca.res <- ipca(liver.toxicity$gene, ncomp = 3, mode="deflation")
ipca.res

# samples representation
plotIndiv(ipca.res, ind.names = as.character(liver.toxicity$treatment[, 4]),
          group = as.numeric(as.factor(liver.toxicity$treatment[, 4])))
## Not run: 
plotIndiv(ipca.res, cex = 0.01,
            col = as.numeric(as.factor(liver.toxicity$treatment[, 4])),style="3d")

## End(Not run)

# variables representation
plotVar(ipca.res, cex = 0.5)

## Not run: 
plotVar(ipca.res, rad.in = 0.5, cex = 0.5,style="3d")
          
## End(Not run)

Example output

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

Loaded mixOmics 6.14.0
Thank you for using mixOmics!
Tutorials: http://mixomics.org
Bookdown vignette: https://mixomicsteam.github.io/Bookdown
Questions, issues: Follow the prompts at http://mixomics.org/contact-us
Cite us:  citation('mixOmics')


Call:
 ipca(X = liver.toxicity$gene, ncomp = 3, mode = "deflation") 

 IPCA with 3 independent components. 
 You entered data X of dimensions: 64 3116 
 Main numerical outputs: 
 -------------------- 
 unmixing matrix: see object$unmixing 
 independent principal components: see object$x 
 mixing matrix: see object$mixing 
 kurtosis: see object$kurtosis 
 variable names: see object$names 
 independent loading vectors: see object$loadings 
Loading required namespace: rgl
Warning messages:
1: In rgl.init(initValue, onlyNULL) : RGL: unable to open X11 display
2: 'rgl.init' failed, running with 'rgl.useNULL = TRUE'. 

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