Why Batch Sensitization is Important for Missing Value Imputation

Data analysis is complex due to a myriad of technical problems. Amongst these, missing values and batch effects are particularly endemic. Although many methods have been developed for missing value imputation (MVI) and batch correction respectively, no study has directly considered the confounding impact of MVI on downstream batch correction. This is surprising as missing values are imputed during early pre-processing while batch effects are mitigated during late pre-processing, prior to functional analysis. The problem is that unless actively managed, MVI approaches generally ignore the batch covariate, with unknown consequences. We examine this problem by modelling 3 imputation strategies: global (M1), self-batch (M2) and cross-batch (M3) using simple matrix simulations, proteomics and genomics data. Considering batch covariates (M2) is important, resulting in enhanced batch correction and lower statistical errors. However, M1 and M3 are insidious: global and cross-batch averaging results in batch-effect dilution, with concomitant and irreversible increase in intra-sample noise. This noise is unremovable via batch correction algorithms and produces false positives and negatives. Hence, careless imputation in the presence of non-negligible covariates such as batch effects is costly.