{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/removal-of-batch-effects-using-distribution","title":"Removal of Batch Effects using Distribution-Matching Residual Networks","arxiv_id":"1610.04181","date":"2016-10-13","proceeding":null,"authors":["Uri Shaham","Kelly P. Stanton","Jun Zhao","Huamin Li","Khadir Raddassi","Ruth Montgomery","Yuval Kluger"],"abstract":"Sources of variability in experimentally derived data include measurement\nerror in addition to the physical phenomena of interest. This measurement error\nis a combination of systematic components, originating from the measuring\ninstrument, and random measurement errors. Several novel biological\ntechnologies, such as mass cytometry and single-cell RNA-seq, are plagued with\nsystematic errors that may severely affect statistical analysis if the data is\nnot properly calibrated. We propose a novel deep learning approach for removing\nsystematic batch effects. Our method is based on a residual network, trained to\nminimize the Maximum Mean Discrepancy (MMD) between the multivariate\ndistributions of two replicates, measured in different batches. We apply our\nmethod to mass cytometry and single-cell RNA-seq datasets, and demonstrate that\nit effectively attenuates batch effects.","url_abs":"http://arxiv.org/abs/1610.04181v6","url_pdf":"http://arxiv.org/pdf/1610.04181v6.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"removal-of-batch-effects-using-distribution","repo_url":"https://github.com/ushaham/BatchEffectRemoval","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1610.04181","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}