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

High-dimensional data acquired from biological experiments such as nextgeneration sequencingare subject to a number of confounding effects. These effects include both technical effects, such as variation across batches from instrument noiseor sample processing ("batch effects"), or institution-specific differences insample acquisition and physical handling ("institutional variability"), as well as biological effects arising from true but irrelevant differences in the biology of each sample, such as age biases in diseases. Prior work has used linear methods toadjust for such batch effects. Here, we apply contrastive metric learning by a nonlinear triplet network to optimize the ability to distinguish biologically distinct sample classes in the presence of irrelevant technical and biological variation. Usingwhole-genome cell-free DNA data from 817 patients, we demonstrate that our approach, METric learning for Confounder Control (METCC), is able to match or exceed the classification performance achieved using a best-in-class linear method(HCP) or no normalization. Critically, results from METCC appear less confounded by irrelevant technical variables like institution and batch than those from other methods even without access to high quality metadata information requiredby many existing techniques; offering hope for improved generalization.