Browsing by Author "Da Camara, Ncité Lima"

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    Tools for analysis of Luminex immunoassay data: development of a robust pipeline and best practices recommendations
    (Stellenbosch : Stellenbosch University, 2023-02) Da Camara, Ncité Lima; Tromp, Gerard; Stellenbosch University. Faculty of Medicine and Health Sciences. Dept. of Biomedical Sciences. Molecular Biology and Human Genetics.
    ENGLISH ABSTRACT: Background and Scope: Immunoassays can be used to detect and measure the concentration of many antigens in a variety of specimens for the diagnosis of diseases, and for the detection of microbes and various illegal substances. In addition, they can be used to monitor and study processes, or differentiate infection, latent or active disease in a patient's immune response after infection with a pathogen by measuring the presence of specific antigens. Recent advances in the instrumentation include multiplex immuno-assays, e.g., Luminex 200 or Luminex MAGPIX®, powered by Luminex xMAP® technology. Analysis of data produced by the multiplex immunoassays is complex and current analytical approaches are highly subjective. Currently, there is no standard, robust approach for data pre-processing of multiplex immunoassay data. To overcome this knowledge gap, I developed a robust and standardized data pre-processing pipeline for multiplex immunoassay data to ensure reproducible data. In addition, provide recommendations for best practices of Luminex data generation and data pre-processing to ensure reproducible science by amending existing laboratory standard operating procedure (SOPs) and developed data pre-processing SOPs’ for the Stellenbosch University Bioinformatics Research Group. Design and Approach: I evaluated current laboratory processes, analysed existing Luminex data and drafted best practices recommendations that will ensure reliable input data for the pipeline. I implemented programmatic steps for data management, quality control, and pre-processing to provide high quality data for analyses. A variety of data preprocessing approaches were investigated, and a set of robust standardized options are provided to the user together with appropriate bioinformatics tools in the newly developed pipeline. Results: For robust and reproducible data generation and data preparation, standardization of manual laboratory best practices procedures are recommended and implemented to ensure standard file name convention, file format, file structure and standard plate layout. In addition, the development of a robust automated standardized data pre-processing pipeline using algorithms in R, freely available software will reduce variability and error introduced by humans. In addition, to ensure reliability and reproducibility of the results generated using this pipeline, the pipeline records metadata such as parameter settings, program, and package versions in the output. The methods were validated using existing de-identified Stellenbosch University https://scholar.sun.ac.za iv data sets from the Stellenbosch University Immunology Research Group. In the future the pipeline can be applied to newly generated data from a variety of immunological studies. Conclusions: This pipeline will standardize and speed up data pre-processing, as well as provide consistent and reproducible results with any complex analyses. Furthermore, provide the bioinformatician or statistician with a rapid means to pre-process Luminex data for subsequent analysis. The framework developed here can be easily applied to other data analysis projects from different biomedical fields.