How can statisticians contribute in the analysis of (high-dimensional) OMICS data? The practical case of metabolomic spectral data
(2017) DHC, Data Sciences — Location: Louvain-la-Neuve, Belgium (18.May.2017)
Files
DHC_UCL_2017_BGovaerts.pdf
Open Access - Adobe PDF
- 14.44 MB
Details
- Authors
Govaerts, Bernadette 
- Abstract
- The use of omics technologies becomes common in a variety of health and pharmaceutical applications with the aim of better understanding the link between the genetic, transcriptomics, proteomic, metabolomic… profiles of biological samples with outcomes of interest as the presence of a disease, a treatment effect,… Among these technologies, spectroscopic techniques (MS, NMR) produce target or untargeted proteomic or metabolomic fingerprints in the form of 1D or 2D high-dimensional spectra that must be preprocessed by finely tuned algorithms and analyzed by advanced multivariate methods in order to extract the relevant information for the biological/medical question of interest. The talk will present several places where the integration of statistical and chemometrics methods provide solutions to process and analyze spectral omics data and how the UCL metabolomic team of ISBA/IMMAQ tries to contribute to the field. Chemometrics methods, traditionally applied in chemistry to the analysis of spectral data, have indeed limitations in the presence of (omics) biological samples affected many sources of variability, issued from complex experimental studies and to answer questions where interpretability and reliable statistical significance measures are necessary to validate the outcomes of the data analysis (e.g. biomarker discovery).
- Affiliations
Citations
Govaerts, B. (2017). How can statisticians contribute in the analysis of (high-dimensional) OMICS data? The practical case of metabolomic spectral data. DHC, Data Sciences, Louvain-la-Neuve, Belgium. https://hdl.handle.net/2078.5/118639