ArticleProteomics2022
A pre-processing pipeline to quantify, visualize, and reduce technical variation in protein microarray studies.
Article in Proteomics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Transfer of IgG from long COVID patients induces symptomology in mice.Cell reports. Medicine · 2026Article
- A Bayesian hierarchical model for signal extraction from protein microarrays.Statistics in medicine · 2023Article
- Novel bioinformatic methods and machine learning approaches reveal candidate biomarkers of the intensity and timing of past exposure to Plasmodium falciparum.PLOS global public health · 2023Article
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Authors and funding
7 authors.
Funding
Abstract
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.
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Registered trials
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