{"title":"Data-driven batch detection enhances single-cell omics data analysis.","authors":"Ziqi Zhang, Xiuwei Zhang","doi":"10.1016/j.cels.2024.09.011","DOIUrl":null,"url":null,"abstract":"<p><p>In single-cell omics studies, data are typically collected across multiple batches, resulting in batch effects: technical confounders that introduce noise and distort data distribution. Correcting these effects is challenging due to their unknown sources, nonlinear distortions, and the difficulty of accurately assigning data to batches that are optimal for integration methods.</p>","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":"15 10","pages":"893-894"},"PeriodicalIF":0.0000,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cell systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1016/j.cels.2024.09.011","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In single-cell omics studies, data are typically collected across multiple batches, resulting in batch effects: technical confounders that introduce noise and distort data distribution. Correcting these effects is challenging due to their unknown sources, nonlinear distortions, and the difficulty of accurately assigning data to batches that are optimal for integration methods.
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