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GO-PCA: An Unsupervised Method to Explore Gene Expression Data Using Prior Knowledge

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GO-PCA (Wagner, 2015) is an unsupervised method to explore gene expression data using prior knowledge. This is a free and open-source implementation of GO-PCA in Python.

Briefly, GO-PCA combines principal component analysis (PCA) with nonparametric GO enrichment analysis in order to generate signatures, i.e., small sets of genes that are both strongly correlated and closely functionally related. It then visualizes the expression profiles of all signatures in a signature matrix, designed to serve as a systematic and easily interpretable representation of biologically relevant expression patterns.

Support and Development

  • For feature requests and bug reports, please create an issue on GitHub.
  • For technical questions, please feel free to email.
  • If you want to contribute code to GO-PCA, please email and/or create a pull request on GitHub.
  • For a list of the latest changes, please see the Changelog.

How to Cite GO-PCA

If you use GO-PCA in your research, please cite Wagner (PLoS One, 2015)

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