go-pca：一种利用先验知识探索基因表达数据的无监督方法
gopca的Python项目详细描述
| latest | |
| develop |
go-pca（Wagner, 2015）是一种无监督的方法来探索基因 使用先验知识表达数据。这是一个免费的开源软件 用python实现go-pca。
简言之，go-pca将principal component analysis (PCA)与 nonparametric GO enrichment analysis以便生成签名， 也就是说，一小部分基因既紧密相关又紧密相关 功能相关的。然后它可视化所有 在签名矩阵中的签名，设计用于 易于解释的生物学相关表达 模式。
支持和发展
如何引用go-pca
如果你在研究中使用go-pca，请引用Wagner (PLoS One, 2015)
版权和许可
版权所有（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/>.
欢迎加入QQ群-->： 979659372
