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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)

Copyright and License

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/>.

Metadata

Release files for gopca 0.2.5

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for gopca 0.2.5
File	Size	Uploaded
gopca-0.2.5.tar.gz	1.2 MB	Sep 13, 2017	Details

Built distributions (wheels)

Table of built distributions (wheels) for gopca 0.2.5
File	Interpreter	ABI	Platform	Reset
gopca-0.2.5-py3-none-any.whl 57.1 kB Sep 13, 2017	Python 3	none	any	Details
gopca-0.2.5-py2-none-any.whl 57.1 kB Sep 13, 2017	Python 2	none	any	Details

Total release size: 1.3 MB

Release files / gopca-0.2.5.tar.gz

Download URL	gopca-0.2.5.tar.gz
Size	1.2 MB
Tags	Source
SHA-256 checksum How to use checksums	`0d78be5fce50b57f28e432e18e45fc663c41738914359a8e73d3a3ddf47120a0`
BLAKE2b-256 checksum How to use checksums	`c99c8c6beed9020573514b47191a6515d616aad303fa5f76886767ee3974fbdb`
Upload date	Sep 13, 2017
Uploaded using Trusted Publishing? What is trusted publishing?	No

Release files / gopca-0.2.5-py3-none-any.whl

Download URL	gopca-0.2.5-py3-none-any.whl
Size	57.1 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`acf4d36d3ff8dc7c5b0fd8743b58500171a467760d29d2b615ee771ca7ed6740`
BLAKE2b-256 checksum How to use checksums	`ad993c48ab84737285e2bc9fd3b2ed672a5839dc279bf1c60fb4f03170bf23a7`
Upload date	Sep 13, 2017
Uploaded using Trusted Publishing? What is trusted publishing?	No

Release files / gopca-0.2.5-py2-none-any.whl

Download URL	gopca-0.2.5-py2-none-any.whl
Size	57.1 kB
Tags	Python 2
SHA-256 checksum How to use checksums	`2127c6ecc0c98cec31b1e0b54c633d7a7c07721661ec3ac438106f22b7b87ddc`
BLAKE2b-256 checksum How to use checksums	`94e8059b263dbade7d4d3db22af2aa3ad9b71b759c1a2210132d44d91da9df4b`
Upload date	Sep 13, 2017
Uploaded using Trusted Publishing? What is trusted publishing?	No