Review of Issues and Solutions to Data Analysis Reproducibility and Data Quality in Clinical Proteomics.

Clicks: 286
ID: 56569
2020
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Ranked #64 of 139 articles by views in methods in molecular biology (clifton, nj)

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Abstract
In any analytical discipline, data analysis reproducibility is closely interlinked with data quality. In this book chapter focused on mass spectrometry-based proteomics approaches, we introduce how both data analysis reproducibility and data quality can influence each other and how data quality and data analysis designs can be used to increase robustness and improve reproducibility. We first introduce methods and concepts to design and maintain robust data analysis pipelines such that reproducibility can be increased in parallel. The technical aspects related to data analysis reproducibility are challenging, and current ways to increase the overall robustness are multifaceted. Software containerization and cloud infrastructures play an important part.We will also show how quality control (QC) and quality assessment (QA) approaches can be used to spot analytical issues, reduce the experimental variability, and increase confidence in the analytical results of (clinical) proteomics studies, since experimental variability plays a substantial role in analysis reproducibility. Therefore, we give an overview on existing solutions for QC/QA, including different quality metrics, and methods for longitudinal monitoring. The efficient use of both types of approaches undoubtedly provides a way to improve the experimental reliability, reproducibility, and level of consistency in proteomics analytical measurements.
Reference Key
walzer2020reviewmethods Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Walzer, Mathias;Vizcaíno, Juan Antonio;
Journal methods in molecular biology (clifton, nj)
Year 2020
DOI
10.1007/978-1-4939-9744-2_15
URL
Keywords
cloud technology computational mass spectrometry large scale data analysis quality control approaches reproducible analysis pipelines

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