Quality Control of Expression Profiling Data

Soloviev, Mikhail; Milnthorpe, Andrew T.

doi:10.4172/jpb.1000366

Cited by 2 publications

(1 citation statement)

References 120 publications

(193 reference statements)

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“…Especially in the omics era, QC has encompassed various statistical methods to check for proper data generation and experimental procedures and for potential contamination before running the main analysis. [114][115][116] In transplantation, some papers have shown the importance of QC procedures in large clinical databases. 117,118 Generally, the standard QC analysis includes calculating quality-related measures, checking consistency among samples, detecting any experiment batch effect (technical heterogeneity), or outlying observations.…”

Section: Quality Control Of Datamentioning

confidence: 99%

Surfing the Big Data Wave: Omics Data Challenges in Transplantation

Geffard

Douillard

et al. 2021

Transplantation

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Ba et al FIGURE 1. Workflow of big data analysis. The workflow is a process that follows a certain direction (the orientation of arrow is important). 1-Scientific questions are the starting point to process big data analysis. 2-Preanalytical processes that include the recruitment, the collect, and the storage of data. The large amount of data potentially available warrants QC methods that ensure data integrity. 3-Downstream analysis processes and the right interpretation of the results, that is, which question can be answered with our results and possibly which application can be made. QC, quality control.

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