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Our hypothesis is that gene signatures can be validated when applied to new datasets, using inherent properties of PCA. Uniqueness: the general direction of the data being examined can drive most of the observed signal.
Due to the large number of genes affected by proliferation pathways in tumors, together with large differences in proliferation status between samples, the first principle component of a PCA analysis, PC1, often captures proliferation-related effects in addition to any effects related to the signature of interest.
The PCA model can easily be flipped back by multiplying both the scores and loadings with −1, a 180-degree rotation.
These issues clearly demonstrate the need to define the ideal characteristics of a PCA-based gene signature and measures of how “well behaved” a signature is when applied to a dataset.
One technique is to include all genes known to be involved in a specific pathway or process, such as a signaling pathway, and treat the signature as a representation of pathway activation.
Gene signatures can also be derived from cell line experiments, where a specific biological event is being modulated, or by comparing different known mutation types within the cell lines.
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The PCA model does not necessarily describe all the biological events in the first principal component.