Monroy Kuhn José Manuel, Miok Viktorian, Lutter Dominik
Computational Discovery Unit, Institute for Diabetes & Obesity, Helmholtz Zentrum München, Neuherberg, Germany.
German Center for Diabetes Research (DZD), Neuherberg, Germany.
Bioinform Adv. 2022 Jun 6;2(1):vbac042. doi: 10.1093/bioadv/vbac042. eCollection 2022.
Today's immense growth in complex biological data demands effective and flexible tools for integration, analysis and extraction of valuable insights. Here, we present CoNI, a practical R package for the unsupervised integration of numerical omics datasets. Our tool is based on partial correlations to identify putative confounding variables for a set of paired dependent variables. CoNI combines two omics datasets in an integrated, complex hypergraph-like network, represented as a weighted undirected graph, a bipartite graph, or a hypergraph structure. These network representations form a basis for multiple further analyses, such as identifying priority candidates of biological importance or comparing network structures dependent on different conditions.
The R package CoNI is available on the Comprehensive R Archive Network (https://cran.r-project.org/web/packages/CoNI/) and GitLab (https://gitlab.com/computational-discovery-research/coni). It is distributed under the GNU General Public License (version 3).
Supplementary data are available at online.
当今复杂生物数据的巨大增长需要有效且灵活的工具来整合、分析和提取有价值的见解。在此，我们展示了CoNI，这是一个用于无监督整合数值组学数据集的实用R包。我们的工具基于偏相关性来识别一组配对的因变量的潜在混杂变量。CoNI将两个组学数据集整合到一个类似复杂超图的网络中，该网络表示为加权无向图、二分图或超图结构。这些网络表示为多种进一步分析奠定了基础，例如识别具有生物学重要性的优先候选物或比较依赖于不同条件的网络结构。
R包CoNI可在综合R存档网络（https://cran.r-project.org/web/packages/CoNI/）和GitLab（https://gitlab.com/computational-discovery-research/coni）上获取。它根据GNU通用公共许可证（第3版）分发。
补充数据可在网上获取。
