Like PCA, Independent Principal Component Analysis (IPCA) is used for exploratory, single `omics, unsupervised analysis. IPCA combines the strengths of PCA and Independent Component Analysis (ICA). ICA as a denoising process prior to principal component generation to maximise statistical independence between these components. Sparse IPCA (sIPCA) allow for the identification of key variables that contribute to defining the principal components.
Data used on this page:liver.toxicity
Key functions used on this page:ipca()sipca()plotIndiv()plotVar()selectVar()plotLoadings()
Related case studies:
Case Study: IPCA Liver toxicity
References:
1.Yao, F., Coquery, J. and Lê Cao, K.A., 2012. Independent principal component analysis for biologically meaningful dimension reduction of large biological data sets. BMC bioinformatics, 13(1), p.24.
2. Comon, P., 1994. Independent component analysis, a new concept?. Signal processing, 36(3), pp.287-314.
3. Hyvärinen, A. and Oja, E., 2000. Independent component analysis: algorithms and applications. Neural networks, 13(4), pp.411-430.