Knowledge Driven Decomposition of Tumor Expression Profiles
Author Information
Author(s): van Vliet Martin H, Wessels Lodewyk F A, Reinders Marcel J T
Primary Institution: Delft University of Technology
Hypothesis
Tumors are the result of a mixture of oncogenic events that can be reflected in gene expression profiles.
Conclusion
The lasso-based constrained least squares decomposition provides a stable and relevant relation between samples and knowledge-based components, offering better molecular characterization of breast cancer subtypes.
Supporting Evidence
- The lasso-based method outperformed other decomposition methods in classifying mouse samples.
- The study identified significant associations between molecular components and clinical parameters in breast cancer.
- The proposed method allows for the incorporation of knowledge-driven components, enhancing interpretation.
Takeaway
This study shows how scientists can break down tumor data to understand cancer better, helping to find the best treatments.
Methodology
A linear model using knowledge-driven, pre-defined components was applied to decompose gene expression profiles.
Limitations
The method requires a set of components derived from knowledge, which may not always be available.
Participant Demographics
The study involved human breast cancer samples and mouse models.
Statistical Information
P-Value
0.0014
Statistical Significance
p = 0.0014
Digital Object Identifier (DOI)
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