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Multiomics measurements are prone to batch effects and can be corrected using appropriate methods. a-c PCA plots based on different batch-effect correction algorithms (BECAs) in balanced and confounded scenarios, using transcriptomics (a), proteomics (b), and metabolomics (c) data. Plots were color-coded by donor (D5, F7, and M8), and shaped by batch. (d) Bar plot of signal-to-noise ratio (SNR) using different BECAs on transcriptomics, proteomics, and metabolomics data
