protGear: A protein microarray data pre-processing suite

Computational and Structural Biotechnology Journal

Nelson Kibinge¹  James Tuju²  James Mburu³  Gathoni Kamuyu⁴  Rinter Kimathi⁴  Lydia Nyamako⁴  Irene Nkumama⁴  Timothy Chege⁴  Samson Kinyanjui⁴  Eustasius Musenge⁴  Emily Chepsat⁴  Faith Osier⁴  Kennedy Mwai⁵ 

[1] Centre for Geographic Medicine Research (Coast), Kenya Medical Research Institute-Wellcome Trust Research Programme, Kilifi, Kenya;Corresponding author at: Epidemiology and Biostatistics Division, School of Public Health, University of the Witwatersrand, Johannesburg, South Africa.;Department of Biotechnology and Biochemistry, Pwani University, Kilifi, Kenya;Centre for Geographic Medicine Research (Coast), Kenya Medical Research Institute-Wellcome Trust Research Programme, Kilifi, Kenya;Epidemiology and Biostatistics Division, School of Public Health, University of the Witwatersrand, Johannesburg, South Africa;

关键词: Protein microarray;    Normalisation;    Background correction;    Batch correction;    Reproducibility;   

DOI  :  

来源: DOAJ

【 摘 要 】

Protein microarrays are versatile tools for high throughput study of the human proteome, but systematic and non-systematic sources of bias constrain optimal interpretation and the ultimate utility of the data. Published guidelines to limit technical variability whilst maintaining important biological variation favour DNA-based microarrays that often differ fundamentally in their experimental design. Rigorous tools to guide background correction, the quantification of within-sample variation, normalisation, and batch correction specifically for protein microarrays are limited, require extensive investigation and are not centrally accessible.Here, we develop a generic one-stop-shop pre-processing suite for protein microarrays that is compatible with data from the major protein microarray scanners. Our graphical and tabular interfaces facilitate a detailed inspection of data and are coupled with supporting guidelines that enable users to select the most appropriate algorithms to systematically address bias arising in customized experiments. The localization and distribution of background signal intensities determine the optimal correction strategy. A novel function overcomes the limitations in the interpretation of the coefficient of variation when signal intensities are at the lower end of the detection threshold. We demonstrate essential considerations in the experimental design and their impact on a range of algorithms for normalization and minimization of batch effects.Our user-friendly interactive web-based platform eliminates the need for prowess in programming. The open-source R interface includes illustrative examples, generates an auditable record, enables reproducibility, and can incorporate additional custom scripts through its online repository. This versatility will enhance its broad uptake in the infectious disease and vaccine development community.

【 授权许可】

Unknown