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.
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Abbreviations
- BSA:
-
Bovine serum albumin
- CWL:
-
Common workflow language
- DAC:
-
Data Access Compliance
- DACO:
-
Data Access Compliance Office
- DDA:
-
Data-dependent acquisition
- DIA:
-
Data-independent acquisition
- FDR:
-
False discovery rate
- GUI:
-
Graphical user interface
- HPC:
-
High-performance computing
- HUPO:
-
Human Proteome Organization
- LC:
-
Liquid chromatography
- LCL:
-
Lower control level
- MS:
-
Mass spectrometry
- PCAWG:
-
Pan-cancer analysis of whole genomes
- PSI:
-
Proteomics Standards Initiative
- QA:
-
Quality assessment
- QC:
-
Quality control
- SOP:
-
Standard operating procedure
- SPC:
-
Statistical process control
- SRM:
-
Selected reaction monitoring
- UCL:
-
Upper control level
- WMS:
-
Workflow management system
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Acknowledgments
The authors would wish to acknowledge funding from ELIXIR Implementation Studies, BBSRC [grant number BB/P024599/1], Wellcome Trust [grant number 208391/Z/17/Z], and EMBL core funding.
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Walzer, M., VizcaĆno, J.A. (2020). Review of Issues and Solutions to Data Analysis Reproducibility and Data Quality in Clinical Proteomics. In: Matthiesen, R. (eds) Mass Spectrometry Data Analysis in Proteomics. Methods in Molecular Biology, vol 2051. Humana, New York, NY. https://doi.org/10.1007/978-1-4939-9744-2_15
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DOI: https://doi.org/10.1007/978-1-4939-9744-2_15
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