Multi-omics in personalised medicine studies combines different layers of biological data, such as genomics, transcriptomics, proteomics, metabolomics and epigenomics, to help researchers understand patient differences more clearly. This video explains how multi-omics can support biomarker research, patient stratification and treatment-response analysis, alongside the statistical and operational challenges that can affect interpretation.
WHAT THIS VIDEO COVERS
• How different omics layers can be analysed together
• How multi-omics can support personalised medicine and biomarker research
• Why high-dimensional data creates statistical and interpretation challenges
• Where machine learning can help, and the risk of overfitting
• How sample timing, identifiers and data linkage affect clinical trial analysis
• The role of single-cell and spatial multi-omics in studying biological variation
The value of multi-omics depends on more than the amount of data collected. A clear clinical question, suitable statistical methods, careful data handling and appropriate validation are all needed if molecular findings are to support meaningful clinical research.
If you are using multi-omics or running a personalised medicine study, Quanticate’s statistical consultancy experts are here to help. With our vast expertise in advanced clinical trial designs and methodologies, we can support you with your clinical trials. Schedule a consultation by requesting a consultation below and learn more about how we can support your clinical trial and statistical needs.