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

RNA Biology
Maryam AbediYousof Gheisari

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.

References

Jul 19, 2012·Nature Reviews. Genetics·Ziv Bar-JosephItamar Simon
Mar 24, 2016·Methods in Molecular Biology·Emily Clough, Tanya Barrett
May 29, 2018·Cell Biology International·Reyhaneh RabieianYousof Gheisari

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Citations

Jan 8, 2020·Clinical and Translational Medicine·Ilnaz Rahimmanesh, Razieh Fatehi

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Methods Mentioned

BETA
PCA

Software Mentioned

ggplot2
R
PCA
prcomp

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