Sparse Contrastive Principal Component Analysis for Computational Biology
Authors: Philippe Boileau, Nima Hejazi, Sandrine Dudoit
The exploration and analysis of modern high-dimensional biological data regularly involves the use of dimension reduction techniques in order to tease out meaningful and interpretable information from complex experimental data, often subject to batch effects and other noise. In tandem with the development of sequencing technology (e.g., RNA-seq, scRNA-seq), many variants of PCA have been developed in attempts to remedy deficiencies in interpretability and stability that plague vanilla PCA.
Such developments have included both various forms of sparse PCA (SPCA)
(Zou, Hastie, and Tibshirani 2006; Erichson et al. 2018), which increase
the stability and interpretability of principal component loadings in
high dimensions, and, more recently, contrastive PCA (cPCA) (Abid et al.
2018), which captures relevant information in the target (experimental)
data set by eliminating technical noise through comparison to a
so-called background data set. While SPCA and cPCA have both
individually proven useful in resolving distinct shortcomings of PCA,
neither is capable of simultaneously tackling the issues of
interpretability, stability and relevance simultaneously. The scPCA
package implements sparse contrastive PCA (Boileau, Hejazi, and Dudoit
2020) to accomplish these tasks in the context of high-dimensional
biological data. In addition to implementing this newly developed
technique, the scPCA package implements cPCA and generalizations
thereof.
For standard use, install from
Bioconductor using
BiocManager:
if (!requireNamespace("BiocManager", quietly=TRUE)) {
install.packages("BiocManager")
}
BiocManager::install("scPCA")To contribute, install the bleeding-edge development version from
GitHub via remotes:
remotes::install_github("PhilBoileau/scPCA")Current and prior Bioconductor releases are available under branches with numbers prefixed by “RELEASE_”. For example, to install the version of this package available via Bioconductor 3.10, use
remotes::install_github("PhilBoileau/scPCA@RELEASE_3_10")For details on how to best use the scPCA R package, please consult the
most recent package
vignette
available through the Bioconductor
project.
If you encounter any bugs or have any specific feature requests, please file an issue.
Contributions are welcome. Interested contributors should consult our contribution guidelines prior to submitting a pull request.
Please cite the first paper below after using the scPCA R software
package. Please also make sure to cite the article describing the
statistical methodology when using scPCA or cross-validated cPCA as part
of an analysis.
@article{boileau2020scPCAjoss,
doi = {10.21105/joss.02079},
url = {https://doi.org/10.21105/joss.02079},
year = {2020},
publisher = {The Open Journal},
volume = {5},
number = {46},
pages = {2079},
author = {Philippe Boileau and Nima Hejazi and Sandrine Dudoit},
title = {scPCA: A toolbox for sparse contrastive principal component analysis in R},
journal = {Journal of Open Source Software}
}
@article{boileau2020scPCA,
author = {Boileau, Philippe and Hejazi, Nima S and Dudoit, Sandrine},
title = "{Exploring High-Dimensional Biological Data with Sparse Contrastive Principal Component Analysis}",
journal = {Bioinformatics},
year = {2020},
month = {03},
issn = {1367-4803},
doi = {10.1093/bioinformatics/btaa176},
url = {https://doi.org/10.1093/bioinformatics/btaa176},
note = {btaa176},
eprint = {https://academic.oup.com/bioinformatics/article-pdf/doi/10.1093/bioinformatics/btaa176/32914142/btaa176.pdf},
}
© 2019-2023 Philippe Boileau
The contents of this repository are distributed under the MIT license.
See file LICENSE for details.
Abid, Abubakar, Martin J Zhang, Vivek K Bagaria, and James Zou. 2018. “Exploring Patterns Enriched in a Dataset with Contrastive Principal Component Analysis.” Nature Communications 9 (1): 2134.
Boileau, Philippe, Nima S Hejazi, and Sandrine Dudoit. 2020. “Exploring High-Dimensional Biological Data with Sparse Contrastive Principal Component Analysis.” Bioinformatics, March. https://doi.org/10.1093/bioinformatics/btaa176.
Erichson, N. Benjamin, Peng Zeng, Krithika Manohar, Steven L. Brunton, J. Nathan Kutz, and Aleksandr Y. Aravkin. 2018. “Sparse Principal Component Analysis via Variable Projection.” ArXiv abs/1804.00341.
Zou, Hui, Trevor Hastie, and Robert Tibshirani. 2006. “Sparse Principal Component Analysis.” Journal of Computational and Graphical Statistics 15 (2): 265–86.