Published August 8, 2019 | Version v3
Dataset Open

Analysis of correlation-based biomolecular networks from different omics data by fitting stochastic block models

Creators

  • 1. Bioinformatics and Modelling, Luxembourg Institute of Health, and Mathematical Modelling of Cellular Processes, Max Delbrück Center for Molecular Medicine in the Helmholtz Association
  • 2. School of Computer Science and Engineering, Nanyang Technological University
  • 3. Bioinformatics and Modelling, Luxembourg Institute of Health

Description

Baum_et_al_2019_Supplementary_Figures.pdf: Supplementary Figures S1-S4. Legends are included under each figure.

sbm-for-correlation-based-networks-master.zip: Archived source code of R and Python functions for the analyses and example workflow description at time of publication. Files are maintained at https://gitlab.com/biomodlih/sbm-for-correlation-based-networks and https://gitlab.com/kabaum/sbm-for-correlation-based-networks.

Files

Baum_et_al_2019_Supplementary_Figures.pdf

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Additional details

References

  • Budczies J, Brockmoller SF, et al. (2013). Comparative metabolomics of estrogen receptor positive and estrogen receptor negative breast cancer: alterations in glutamine and beta-alanine metabolism. J Proteomics. 2013;94:279-88.
  • Peixoto TP (2014). The graph-tool python library. 10.6084/m9.figshare.1164194
  • Langfelder P, Horvath S. WGCNA (2008): an R package for weighted correlation network analysis. BMC Bioinformatics, 9:559.