title: Why Batch Effects Matter in Omics Data, and How to Avoid Them.
abstract: Effective integration and analysis of new high-throughput data, especially gene-expression and proteomic-profiling data, are expected to deliver novel clinical insights and therapeutic options. Unfortunately, technical heterogeneity or batch effects (different experiment times, handlers, reagent lots, etc.) have proven challenging. Although batch effect-correction algorithms (BECAs) exist, we know little about effective batch-effect mitigation: even now, new batch effect-associated problems are emerging. These include false effects due to misapplying BECAs and positive bias during model evaluations. Depending on the choice of algorithm and experimental set-up, biological heterogeneity can be mistaken for batch effects and wrongfully removed. Here, we examine these emerging batch effect-associated problems, propose a series of best practices, and discuss some of the challenges that lie ahead.
authors.affiliation: School of Pharmaceutical Science and Technology, Tianjin University, Tianjin 300072, P.R. China; Department of Computer Science, National University of Singapore, Singapore 117417, Republic of Singapore. Electronic address: wilson.goh@tju.edu.cn.
authors.forename: Wilson Wen Bin
authors.initials: WWB
authors.lastname: Goh
authors.affiliation: School of Pharmaceutical Science and Technology, Tianjin University, Tianjin 300072, P.R. China.
authors.forename: Wei
authors.initials: W
authors.lastname: Wang
authors.affiliation: Department of Computer Science, National University of Singapore, Singapore 117417, Republic of Singapore; Department of Pathology, National University of Singapore, Singapore 119074, Republic of Singapore. Electronic address: wongls@comp.nus.edu.sg.
authors.forename: Limsoon
authors.initials: L
authors.lastname: Wong
mesh_terms: D000465:Algorithms; D030541:Databases, Genetic; D020869:Gene Expression Profiling; D016247:Information Storage and Retrieval / Q000379:methods*; D008962:Models, Theoretical; D040901:Proteomics
keywords: batch effect; cross-validation; data integration; heterogeneity; reproducibility