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Correcting batch effects in large-scale multiomics studies using a reference-material-based ratio method.

Ying YuNaixin ZhangYuanbang MaiLuyao RenQiaochu ChenZehui CaoQingwang ChenYaqing LiuWanwan HouJingcheng YangHuixiao HongJoshua XuWeida TongLianhua DongLeming ShiXiang FangYuanting Zheng
Published in: Genome biology (2023)
Multiomics measurements are prone to batch effects, which can be effectively corrected using ratio-based scaling of the multiomics data. Our study lays the foundation for eliminating batch effects at a ratio scale.
Keyphrases
  • electronic health record
  • anaerobic digestion
  • deep learning