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. 2019 Nov;16(11):1531-1533.
doi: 10.1080/15476286.2019.1652525. Epub 2019 Aug 12.

Big data to knowledge: common pitfalls in transcriptomics data analysis and representation

Maryam Abedi  1   2 , Razieh Fatehi  2 , Kobra Moradzadeh  2 , Yousof Gheisari  1   2
Affiliations

Big data to knowledge: common pitfalls in transcriptomics data analysis and representation

Maryam Abedi et al. RNA Biol. 2019 Nov.

Abstract

The omics technologies provide an invaluable opportunity to employ a global view towards human diseases. However, the appropriate translation of big data to knowledge remains a major challenge. In this study, we have performed quality control assessments for 91 transcriptomics datasets deposited in gene expression omnibus database and also have evaluated the publications derived from these datasets. This survey shows that drawbacks in the analyses and reports of transcriptomics studies are more common than one may assume. This report is concluded with some suggestions for researchers and reviewers to enhance the minimal requirements for gene expression data generation, analysis and report.

Keywords: Big data; data analysis; differentially expressed gene; quality control; transcriptomics.

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Figures

Figure 1.
Figure 1.
The framework followed in this study is depicted. For all datasets quality control assessment was performed using PCA (a). The publications derived from the datasets were inspected for the accuracy of data analysis and representation (b–g). QC: quality control, PCA: principal component analysis. DEGs: differentially expressed genes.
See this image and copyright information in PMC

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