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PCA rotates the data onto the directions of greatest variance and keeps the top few, compressing twenty thousand genes into a few hundred numbers before a model sees them.
Principal component analysis is a linear dimensionality reduction technique used in exploratory analysis and preprocessing (Wikipedia). Reducing a full transcriptome to about 256 components before ridge or Cox regression is a strong, cheap baseline and the extraction recipe for frozen foundation-model embeddings; reducing each modality separately before concatenation stops a small block (a few hundred proteins) being swamped by a large one (all genes). The components must be fitted on the training fold only.
Shares Multimodal fusion (early, late, modality dropout), Embedding (learned representation), Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Ridge regression and the multilayer perceptron, Embedding (learned representation), Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Multimodal fusion (early, late, modality dropout), Embedding (learned representation), Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Multimodal fusion (early, late, modality dropout), Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Out-of-distribution detection (Mahalanobis guard), Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Ridge regression and the multilayer perceptron, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Spatially aware clustering (SpaGCN, KNN smoothing), Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Ridge regression and the multilayer perceptron, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.