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High-dimensional data acquired from biological experiments such as nextgeneration sequencing are subject to a number of confounding effects. Theseeffects include both technical effects, such as variation across batches frominstrument noise or sample processing, or institution-specific differences insample acquisition and physical handling, as well as biological effects arisingfrom true but irrelevant differences in the biology of each sample, such as agebiases in diseases. Prior work has used linear methods to adjust for such batcheffects. Here, we apply contrastive metric learning by a non-linear tripletnetwork to optimize the ability to distinguish biologically distinct sampleclasses in the presence of irrelevant technical and biological variation. Usingwhole-genome cell-free DNA data from 817 patients, we demonstrate that ourapproach, METric learning for Confounder Control (METCC), is able to match orexceed the classification performance achieved using a best-in-class linearmethod (HCP) or no normalization. Critically, results from METCC appear lessconfounded by irrelevant technical variables like institution and batch thanthose from other methods even without access to high quality metadatainformation required by many existing techniques; offering hope for improvedgeneralization.

METCC: METric learning for Confounder Control Making distance matter in high dimensional biological analysis