Project properties
Title Integration of Single sample networks for multiomics data.
Group Systems and Synthetic Biology
Project type thesis
Credits 36
Supervisor(s) Edoardo Saccenti
Examiner(s) Edoardo Saccenti, Rob Smith
Contact info robert1.smith@wur.nl
Begin date 2024/05/15
End date
Description Networks and network analyses are fundamental tools of systems biology. Networks are built by inferring pair-wise relationships among biological entities from a large number of samples such that subject-specific information is lost. The possibility of constructing these sample (individual)-specific networks from single molecular profiles might offer new insights in systems and personalized medicine and as a consequence is attracting more and more research interest.
Several methods have been proposed, all with their pro and cons, but have been evaluated on one data type.
Scope of this project is to assess, quantify and show the added value of the use of single sample networks for the integration of different omics data to address biological problems. In particular several multiomics data sets will be considered containing transcriptomics, proteomics, metabolomics and macrobiotics data will be analysis through a variety of standard techniques (including machine learning) and results compared with those obtained from single sample networks.


References

1. Jahagirdar, S., and Saccenti, E. (2021). Evaluation of Single Sample Network Inference Methods for Metabolomics-Based Systems Medicine. Journal of Proteome Research 20, 932-949. 10.1021/acs.jproteome.0c00696.
2. Liu, X., Wang, Y., Ji, H., Aihara, K., and Chen, L. (2016). Personalized characterization of diseases using sample-specific networks. Nucleic Acids Research 44, e164. 10.1093/nar/gkw772.
3. Kuijjer, M.L., Tung, M.G., Yuan, G., Quackenbush, J., and Glass, K. (2019). Estimating Sample-Specific Regulatory Networks. iScience 14, 226-240. 10.1016/j.isci.2019.03.021.





Used skills Processing and analysis of large omics data sets; inference, validation and interpretation of single sample networks . Biological interpretation of data analysis results in the context of different data types. Advanced programming skills in R and Matlab.
Requirements Good understanding of fundamental statistical concepts like correlation, basic linear algebra (matrices and determinants). Basic molecular biology, basic understanding of omics data (transcriptomics, proteomics, metabolomics) characteristics. Knowledge of Machine learning and biological networks at the level of Molecular Systems Biology course (SSB-30306). Ability of programming in R and/or Matlab is a pre.