A pre-processing pipeline to quantify, visualize, and reduce technical variation in protein microarray studies

Technical variation, or variation from non-biological sources, is present in most laboratory assays. Correcting for this variation enables analysts to extract a biological signal that informs questions of interest. However, each assay has different sources and levels of technical variation, and the choice of correction methods can impact downstream analyses. Compared to similar assays such as DNA microarrays, relatively few methods have been developed and evaluated for protein microarrays, a versatile tool for measuring levels of various proteins in serum samples. Here, we propose a pre-processing pipeline to correct for some common sources of technical variation in protein microarrays. The pipeline builds upon an existing normalization method by using controls to reduce technical variation. We evaluate our method using data from two protein microarray studies and by simulation. We demonstrate that pre-processing choices impact the fluorescent-intensity based ranks of proteins, which in turn, impact downstream analysis.

Automated summary

Technical variation exists in most laboratory assays, and correcting for this variation is important for extracting biological signals. The choice of correction methods can impact downstream analyses. Protein microarrays, a versatile tool for measuring levels of various proteins in serum samples, have relatively few developed and evaluated methods compared to DNA microarrays. A pre-processing pipeline is proposed to correct for common sources of technical variation in protein microarrays.