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Quantile normalisation forces every sample's expression values onto the same distribution by rank, and a z-score expresses each gene as standard deviations from its mean; both are ways to make data from different platforms comparable.
Quantile normalisation makes two distributions identical in statistical properties by sorting both and replacing each value with the reference value of the same rank (Wikipedia); it can be applied to a single new sample against a fixed reference, which makes it usable at prediction time. A standard score is the number of standard deviations a value lies from the mean (Wikipedia), applied per gene it aligns RNA-seq and microarray means but needs a cohort to estimate. Both are the correctness backbone that lets a TCGA model read a METABRIC sample.
Shares Microarray expression data, TPM, FPKM and raw counts (expression units), Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Domain shift and domain adaptation (cell line to patient), Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares TPM, FPKM and raw counts (expression units), Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Batch effects and harmonisation, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares TPM, FPKM and raw counts (expression units), Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Microarray expression data, Domain shift and domain adaptation (cell line to patient), Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Domain shift and domain adaptation (cell line to patient), Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Batch effects and harmonisation, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.