Perspectives for better batch effect correction in mass-spectrometry-based proteomics

Abstract

Mass-spectrometry-based proteomics presents some unique challenges for batch effect correction. Batch effects are technical sources of variation, can confound analysis and usually non-biological in nature. As proteomic analysis involves several stages of data transformation from spectra to protein, the decision on when and what to apply batch correction on is often unclear. Here, we explore several relevant issues pertinent to batch effect correct considerations. The first involves applications of batch effect correction requiring prior knowledge on batch factors and exploring data to uncover new/unknown batch factors. The second considers recent literature that suggests there is no single best batch effect correction algorithm---i.e., instead of a best approach, one may instead ask, what is a suitable approach. The third section considers issues of batch effect detection. And finally, we look at potential developments for proteomic-specific batch effect correction methods and how to do better functional evaluations on batch corrected data.

Keywords: Batch correction; Batch effects; Batch visualization; Proteomics.

Fig. 1

Summary of all workflows used in this study (A) Protein-level BE correction. (B). (A) Peptide-level BE correction prior to protein assembly. (C) Peptide-level BE correction where ambiguous peptides are first retained for batch estimation, and then discarded before protein assembly.

Fig. 2

Evaluating BE correction across two methods. (A) A line plot showing the relationship between PCs and associative p-values based on Kruskal-Wallis test. The top and bottom panels shows associations with batch effects and class effects respectively. (B) Barcharts of guided PCA (gPCA) delta values for batch (top) and class (bottom) effects. For both (A) and (B), an ideal method is one that promotes class effects while demoting batch effects.