title: Protein complex-based analysis is resistant to the obfuscating consequences of batch effects --- a case study in clinical proteomics.
abstract: In proteomics, batch effects are technical sources of variation that confounds proper analysis, preventing effective deployment in clinical and translational research.
  
  
  
  Using simulated and real data, we demonstrate existing batch effect-correction methods do not always eradicate all batch effects. Worse still, they may alter data integrity, and introduce false positives. Moreover, although Principal component analysis (PCA) is commonly used for detecting batch effects. The principal components (PCs) themselves may be used as differential features, from which relevant differential proteins may be effectively traced. Batch effect are removable by identifying PCs highly correlated with batch but not class effect. However, neither PC-based nor existing batch effect-correction methods address well subtle batch effects, which are difficult to eradicate, and involve data transformation and/or projection which is error-prone. To address this, we introduce the concept of batch-effect resistant methods and demonstrate how such methods incorporating protein complexes are particularly resistant to batch effect without compromising data integrity.
  
  
  
  Protein complex-based analyses are powerful, offering unparalleled differential protein-selection reproducibility and high prediction accuracy. We demonstrate for the first time their innate resistance against batch effects, even subtle ones. As complex-based analyses require no prior data transformation (e.g. batch-effect correction), data integrity is protected. Individual checks on top-ranked protein complexes confirm strong association with phenotype classes and not batch. Therefore, the constituent proteins of these complexes are more likely to be clinically relevant.
authors.affiliation: School of Pharmaceutical Science and Technology, Tianjin University, 92 Weijin Road, Nankai District, Tianjin, 300072, People's Republic of China. wilson.goh@tju.edu.cn.|Department of Computer Science, National University of Singapore, 13 Computing Drive, Singapore, 117417, Singapore. wilson.goh@tju.edu.cn.
authors.forename: Wilson Wen Bin
authors.initials: WW
authors.lastname: Goh
authors.affiliation: Department of Computer Science, National University of Singapore, 13 Computing Drive, Singapore, 117417, Singapore. wongls@comp.nus.edu.sg.|Department of Pathology, National University of Singapore, Singapore, Singapore. wongls@comp.nus.edu.sg.
authors.forename: Limsoon
authors.initials: L
authors.lastname: Wong
mesh_terms: D016000:Cluster Analysis; D006801:Humans; D007680:Kidney Neoplasms / Q000737:chemistry*; D009363:Neoplasm Proteins / Q000737:chemistry*; D025341:Principal Component Analysis; D011485:Protein Binding; D055503:Protein Multimerization; D040901:Proteomics / Q000379:methods / Q000706:statistics & numerical data*; D015203:Reproducibility of Results; D013048:Specimen Handling / Q000592:standards
chemical_list: D009363:Neoplasm Proteins
keywords: Batch effects; Bioinformatics; Heterogeneity; Principal component analysis; Proteomics