guidedPLS

R package for supervised dimensional reduction by guided partial least squares

Installation

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git clone https://github.com/rikenbit/guidedPLS/
R CMD INSTALL guidedPLS

or type the code below in the R console window

library(devtools)
devtools::install_github("rikenbit/guidedPLS")

Sparse matrix support (v1.2.0)

guidedPLS() accepts Matrix::dgCMatrix (sparse matrices) for X1 and X2. This is particularly useful for single-cell data (scRNA-seq, scATAC-seq) where feature matrices are large and sparse.

Internally, the row-centered cross-product is computed without materializing the dense centered matrix, using the identity:

X_centered %*% Y = X %*% Y - mu * (1^T Y)

where mu is the vector of row means and 1^T Y = colSums(Y). Only the small k x p result matrix is dense, keeping memory usage proportional to the number of non-zero entries.

References

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• Sparse Partial Least Squares (sPLS)/Sparse Partial Least Squares Discriminant Analysis (sPLS-DA)
  • Lê Cao, et al. A Sparse PLS for Variable Selection when Integrating Omics Data. Statistical Applications in Genetics and Molecular Biology, 7(1), 2008
• Guided Principal Component Analysis (guided-PCA)
  • Reese S E, et al. A new statistic for identifying batch effects in high-throughput genomic data that uses guided principal component analysis. Bioinformatics, 29(22), 2877-2883, 2013
• Multiset Canonical Correlations Analysis
  • Nielsen A A. Multiset Canonical Correlations Analysis and Multispectral, Truly Multitemporal Remote Sensing Data. IEEE Transactions on Image Processing, 11(3), 293-305, 2002

Contributing

If you have suggestions for how guidedPLS could be improved, or want to report a bug, open an issue! We'd love all and any contributions.

For more, check out the Contributing Guide.

Authors

• Koki Tsuyuzaki