Bridge Sample Selection and Batch Correction in High-Dimensional Data

Music City Center 

Batch effects, or technical variation across experimental batches, can obscure true biological signals and represent a significant challenge in the analysis and interpretation of 'omics data. This is particularly true of proteomics data generated using Olink Target technology. To mitigate batch effects, Olink recommends using bridge samples, however, it is unclear whether bridge samples are always necessary. Furthermore, if bridge samples are needed, key questions arise regarding the appropriate number of bridge samples, as well as the strategy for the specific selection of bridge samples. To shed light on these questions, we conducted a systematic evaluation of three batch correction approaches including Olink's bridge sample method, COMBAT, and a case-control confounded approach (Remeasure) across three different study designs: (1) cases and controls processed in separate batches, (2) cases and controls mixed within each batch, and (3) cases distributed across multiple batches. Using simulations that closely reflect real-world datasets, we assessed the impact of batch correction methods on statistical power, Type I error, and false discovery rate. Our results provide guidance on which correction method performs best under different scenarios and the optimal number of bridge samples needed for effective correction. We further validated our findings by applying these methods to a real dataset. While our study focuses on batch correction within Olink proteomics data, the methodologies and insights presented here may be applicable to other high-dimensional omics datasets facing similar challenges.

Batch Effect

Bridge Sample Selection

OlinkAnalyze

COMBAT

High-Dimensional Data

Olink Proteomics 

Section on Statistics in Genomics and Genetics