10.6084/m9.figshare.9256433.v2 Maryam Abedi Maryam Abedi Razieh Fatehi Razieh Fatehi Kobra Moradzadeh Kobra Moradzadeh Yousof Gheisari Yousof Gheisari Big data to knowledge: common pitfalls in transcriptomics data analysis and representation Taylor & Francis Group 2019 Big data data analysis differentially expressed gene transcriptomics quality control 2019-08-12 09:55:43 Dataset https://tandf.figshare.com/articles/dataset/Big_Data_to_Knowledge_Common_Pitfalls_in_Transcriptomics_Data_Analysis_and_Representation/9256433 <p>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.</p>