Performs drCCA on a collection of data sets with co-occurring samples. The method utilizes regularized canonical correlation analysis to find linear projections for each of the data sets, and uses those to construct a combined representation of lower dimensionality than the original collection. The method suggests a specific dimensionality for the combined representation, but it is possible to obtain also combined data sets of different dimensionality.
drCCAcombine(datasets, reg=0, nfold=3, nrand=50)Regularization parameter for the whitening step used to remove data-set specific variation. The value of parameter must be between 0 and 1. The default value is set to 0, which means no regularization will be used. If a non-zero value is given it means that some of the dimensions with the lowest variance are ignored when whitening. In more technical terms, the dimensions whose total contribution to the sum of eigenvalues of the covariance matrix of each data set is below reg will not be used for the whitening.
The function uses regCCA to perform the canonical
correlation analysis. The dimensionality of the combined data set is
selected using a statistical test that aims to find which dimensions
capture shared variation significantly more than what would be
found under the assumption that the data sets were independent. For
this purpose rnand collections of random matrices with similar
variance structure but no between-data dependencies are created. The
amount of variation each dimension extracts from leave-out data in the
cross-validation setting with nfold folds is compared to the
distribution obtained from the random matrices, and the dimensions
that differ significantly from the null hypothesis of independence are
kept in the combined representation. For details, please check the
reference.
regCCA
# data(expdata1)
# data(expdata2)
# drCCAcombine(list(expdata1,expdata2),0,2,3)
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