The subject of this internship concerns the comparison of multi-varied methods to analyze and integrate different types of heterogeneous data with the objective of identifying the links between the profiles of Raman techniques and phenotype such as gene expression. Data from [1,2] will be used to set up this comparison. A first step will consist in applying the different methods of mixomics MixOmics page and the visualization of the results will be also central with dataset visualization approaches multidimensional using t-SNE, parallel coordinate approaches...
References:
- (1) Germond A, Ichimura T, Horinouchi T, Fujita H, Furusawa C, Watanabe TM. Spectral Raman signature reflects transcriptomic features of antibiotic resistance in Escherichia coli. Common Biol. 2018 Jul 2;1:85. doi: 10.1038/s42003-018-0093-8. PMID: 30271966; PMCID: PMC6123714.
- (2) Kobayashi-Kirschvink KJ, Nakaoka H, Oda A, Kamei KF, Nosho K, Fukushima H, Kanesaki Y, Yajima S, Masaki H, Ohta K, Wakamoto Y. Linear Regression Links Transcriptomic Data and Cellular Raman Spectra. Cell Syst. 2018 Jul 25;7(1):104-117.e4. doi: 10.1016/j.cels.2018.05.015. Epub 2018 Jun 20. PMID: 29936183.
- MBPLS documentation Baum et al., (2019). Multiblock PLS: Block dependent prediction modeling for Python. Journal of Open Source Software, 4(34), 1190