(s)IPCA

Usage in mixOmics

IPCA is of class sPCA and PCA, and most of the PCA graphical methods can be applied. The default algorithm tp estimate the unmixing matrix is set to mode = ‘deflation’. By default, the data are centered, but not necessarily scaled.

data(liver.toxicity)
# implement IPCA on a microarray dataset
ipca.res <- ipca(liver.toxicity$gene, ncomp = 3, mode="deflation")
ipca.res
# samples representation
plotIndiv(ipca.res, ind.names = liver.toxicity$treatment[, 4], cex = 0.5,
          col = as.numeric(as.factor(liver.toxicity$treatment[, 4])))
plot3dIndiv(ipca.res, cex = 0.01,
            col = as.numeric(as.factor(liver.toxicity$treatment[, 4])))
# variables representation
plotVar(ipca.res, var.label = TRUE, cex = 0.5)
plot3dVar(ipca.res, rad.in = 0.5, cex = 0.5, col = as.numeric(as.factor(liver.toxicity$treatment[, 4])))
ipca.res

 

Parameters to tune

The kurtosis measure is used to order the loading vectors to order the Independent Principal Components.  We have shown that the kurtosis value is a good post hoc indicator of the number of components to choose, as a sudden drop in the values corresponds to irrelevant dimensions.

ipca.res$kurtosis


More details can be found in the case study and the published article below.

References

  • Yao F., Coquery J., Lê Cao K.-A. (2012) Independent Principal Component Analysis for biologically meaningful dimension reduction of large biological data sets, BMC Bioinformatics 13:24.  [link]
  • Comon P: Independent component analysis, a new concept? Signal Process 1994, 36:287-314.
  • Hyvärinen A, Oja E: Indepedent Component Analysis: Algorithms and Applications. Neural Networks 2000, 13(4-5):411-430
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