WGCNA

R package WGCNA: Weighted Correlation Network Analysis. Functions necessary to perform Weighted Correlation Network Analysis on high-dimensional data. Includes functions for rudimentary data cleaning, construction of correlation networks, module identification, summarization, and relating of variables and modules to sample traits. Also includes a number of utility functions for data manipulation and visualization.

Keywords for this software

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References in zbMATH (referenced in 26 articles )

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  1. Peeters, C. F. W., Bilgrau, A. E., van Wieringen, W. N. : rags2ridges: A One-Stop-l2-Shop for Graphical Modeling of High-Dimensional Precision Matrices (2022) not zbMATH
  2. Austen Bernardi, Jessica M.J. Swanson: CycFlowDec: A Python module for decomposing flow networks using simple cycles (2021) not zbMATH
  3. Bocharov, Gennady A.; Grebennikov, Dmitry S.; Savinkov, Rostislav S.: Frontiers in mathematical modelling of the lipid metabolism under normal conditions and its alterations in heart diseases (2021)
  4. He, Xinwei; Sun, Xiaoqiang; Shao, Yongzhao: Network-based survival analysis to discover target genes for developing cancer immunotherapies and predicting patient survival (2021)
  5. Ma, Chen; Yao, Zhihao; Zhang, Qinran; Zou, Xiufen: Quantitative integration of radiomic and genomic data improves survival prediction of low-grade glioma patients (2021)
  6. Wang, Y. X. Rachel; Li, Lexin; Li, Jingyi Jessica; Huang, Haiyan: Network modeling in biology: statistical methods for gene and brain networks (2021)
  7. Yu, Chong; Xu, Hong; Wang, Jin: A global and physical mechanism of gastric cancer formation and progression (2021)
  8. Li, Jinyu; Lai, Yutong; Zhang, Chi; Zhang, Qi: TGCnA: temporal gene coexpression network analysis using a low-rank plus sparse framework (2020)
  9. Daniel Conn, Tuck Ngun, Gang Li, Christina M. Ramirez: Fuzzy Forests: Extending Random Forest Feature Selection for Correlated, High-Dimensional Data (2019) not zbMATH
  10. Kharoubi, Rachid; Oualkacha, Karim; Mkhadri, Abdallah: The cluster correlation-network support vector machine for high-dimensional binary classification (2019)
  11. Melina Vidoni; Aldo Vecchietti : rsppfp: An R package for the shortest path problem with forbidden paths (2019) not zbMATH
  12. Sanguinetti, Guido (ed.); Huynh-Thu, Vân Anh (ed.): Gene regulatory networks. Methods and protocols (2019)
  13. Suner, Aslı: Clustering methods for single-cell RNA-sequencing expression data: performance evaluation with varying sample sizes and cell compositions (2019)
  14. Yuan, Ye; Bar-Joseph, Ziv: Deep learning for inferring gene relationships from single-cell expression data (2019)
  15. Bodwin, Kelly; Zhang, Kai; Nobel, Andrew: A testing based approach to the discovery of differentially correlated variable sets (2018)
  16. Esteves, Gustavo H.; Reis, Luiz F. L.: A statistical method for measuring activation of gene regulatory networks (2018)
  17. Md. Bahadur Badsha, Evan A Martin, Audrey Qiuyan Fu: MRPC: An R package for accurate inference of causal graphs (2018) arXiv
  18. von Stechow, Louise (ed.); Delgado, Alberto Santos (ed.): Computational cell biology. Methods and protocols (2018)
  19. Deisy Morselli Gysi, Andre Voigt, Tiago de Miranda Fragoso, Eivind Almaas, Katja Nowick: wTO: an R package for computing weighted topological overlap and consensus networks with an integrated visualization tool (2017) arXiv
  20. Weishaupt, Holger; Johansson, Patrik; Engström, Christopher; Nelander, Sven; Silvestrov, Sergei; Swartling, Fredrik J.: Loss of conservation of graph centralities in reverse-engineered transcriptional regulatory networks (2017)

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