scPCA

scPCA: A toolbox for sparse contrastive principal component analysis in R - Published in JOSS (2020)

https://github.com/philboileau/scpca

Keywords

Keywords from Contributors

Repository

A toolbox for sparse contrastive principal component analysis

README.Rmd

---
output:
  rmarkdown::github_document
bibliography: "inst/REFERENCES.bib"
---



```{r, echo = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "README-"
)
```

# R/`scPCA`

[![Travis CI Build Status](https://travis-ci.org/PhilBoileau/scPCA.svg?branch=master)](https://travis-ci.org/PhilBoileau/scPCA.svg?branch=master)
[![AppVeyor Build  Status](https://ci.appveyor.com/api/projects/status/github/PhilBoileau/scPCA?branch=master&svg=true)](https://ci.appveyor.com/project/PhilBoileau/scPCA/)
[![Codecov test coverage](https://codecov.io/gh/PhilBoileau/scPCA/branch/master/graph/badge.svg)](https://codecov.io/gh/PhilBoileau/scPCA?branch=master)
[![Project Status: Active – The project has reached a stable, usable state and is being actively developed.](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active)
[![BioC status](http://www.bioconductor.org/shields/build/release/bioc/scPCA.svg)](https://bioconductor.org/checkResults/release/bioc-LATEST/scPCA)
[![Bioc Time](http://bioconductor.org/shields/years-in-bioc/scPCA.svg)](https://bioconductor.org/packages/release/bioc/html/scPCA.html)
[![status](https://joss.theoj.org/papers/7f0f1271ede7aba120d71c9b5a14c865/status.svg)](https://joss.theoj.org/papers/7f0f1271ede7aba120d71c9b5a14c865)
[![MIT license](http://img.shields.io/badge/license-MIT-brightgreen.svg)](http://opensource.org/licenses/MIT)

> Sparse Contrastive Principal Component Analysis for Computational Biology

__Authors:__ [Philippe Boileau](https://pboileau.ca/),
  [Nima Hejazi](https://nimahejazi.org),
  [Sandrine Dudoit](https://statistics.berkeley.edu/~sandrine/)

---

## What's `scPCA`?

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)
[@zou2006sparse; @erichson2018sparse], which increase the stability and
interpretability of principal component loadings in high dimensions, and, more
recently, contrastive PCA (cPCA) [@abid2018exploring], 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_ [@boileau2020] 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.

---

## Installation

For standard use, install from
[Bioconductor](https://bioconductor.org/packages/scPCA) using
[`BiocManager`](https://CRAN.R-project.org/package=BiocManager):

```{r bioc-installation, eval = FALSE}
if (!requireNamespace("BiocManager", quietly=TRUE)) {
  install.packages("BiocManager")
}
BiocManager::install("scPCA")
```

To contribute, install the bleeding-edge _development version_ from GitHub via
[`remotes`](https://CRAN.R-project.org/package=remotes):

```{r gh-master-installation, eval = FALSE}
remotes::install_github("PhilBoileau/scPCA")
```

Current and prior [Bioconductor](https://bioconductor.org) 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

```{r gh-develop-installation, eval = FALSE}
remotes::install_github("PhilBoileau/scPCA@RELEASE_3_10")
```

---

## Example

For details on how to best use the `scPCA` R package, please consult the most
recent [package
vignette](https://bioconductor.org/packages/release/bioc/vignettes/scPCA/inst/doc/scpca_intro.html)
available through the [Bioconductor
project](https://bioconductor.org/packages/scPCA).

---

## Issues

If you encounter any bugs or have any specific feature requests, please [file an
issue](https://github.com/PhilBoileau/scPCA/issues).

---

## Contributions

Contributions are welcome. Interested contributors should consult our
[contribution
guidelines](https://github.com/PhilBoileau/scPCA/blob/master/CONTRIBUTING.md)
prior to submitting a pull request.

---

## Citation

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},
}
```

---

## License

© 2019-2023 [Philippe Boileau](https://pboileau.ca/)

The contents of this repository are distributed under the MIT license. See file
`LICENSE` for details.

---

## References

Owner

JOSS Publication

scPCA: A toolbox for sparse contrastive principal component analysis in R

Published

February 25, 2020

DOI

10.21105/joss.02079

Volume 5, Issue 46, Page 2079

Authors

Philippe Boileau
Graduate Group in Biostatistics, University of California, Berkeley

Nima S. Hejazi
Graduate Group in Biostatistics, University of California, Berkeley, Center for Computational Biology, University of California, Berkeley

Sandrine Dudoit
Center for Computational Biology, University of California, Berkeley, Department of Statistics, University of California, Berkeley, Division of Epidemiology and Biostatistics, School of Public Health, University of California, Berkeley

Editor

Charlotte Soneson

Tags

dimensionality reduction principal component analysis computational biology unwanted variation sparsity

View PDF Review Thread Software Archive

GitHub Events

Committers

Last synced: 7 months ago

Issues and Pull Requests

Last synced: 6 months ago

Packages