Simultaneous analysis of distinct Omics data sets with integration of biological knowledge: Multiple Factor Analysis approach
2009
Analyzing Multiple Omics Data with Multiple Factor Analysis
Sample size: 43
publication
Evidence: moderate
Author Information
Author(s): Marie de Tayrac, Sébastien Lê, Marc Aubry, Jean Mosser, François Husson
Primary Institution: Université de Rennes 1
Hypothesis
Can Multiple Factor Analysis (MFA) effectively integrate and interpret various omics data sets?
Conclusion
MFA prioritizes biological processes linked to experimental settings and reduces the time needed to analyze large omics data.
Supporting Evidence
- MFA effectively integrates multiple omics data sets.
- The method highlights common structures in biological data.
- Graphical outputs from MFA make biological interpretations easier.
- Gene Ontology terms help in building gene modules for analysis.
Takeaway
This study shows a way to combine different types of biological data to understand diseases better, like brain tumors.
Methodology
Multiple Factor Analysis (MFA) was used to analyze genomic and transcriptomic data sets.
Limitations
The interpretation of results can be complex and time-consuming.
Participant Demographics
The study involved tumor samples from glioma patients.
Statistical Information
P-Value
p<0.05
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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