GO-PCA: An Unsupervised Method to Explore Gene Expression Data Using Prior Knowledge
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.
If you use GO-PCA in your research, please cite Wagner (PLoS One, 2015)
Copyright (c) 2015, 2016 Florian Wagner
GO-PCA is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License, Version 3, as published by the Free Software Foundation. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see <http://www.gnu.org/licenses/>.
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