ArticlePloS one2018
Comparison of normalization approaches for gene expression studies completed with high-throughput sequencing.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 2 of them syntheses that pooled it.
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Who cites it
39 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Meta-Analysis of Quality of Life in Cancer Patients Treated With Immune Checkpoint Inhibitors.Journal of the National Cancer Institute · 2022Pooled it
- Statistical genomics in rare cancer.Seminars in cancer biology · 2020Pooled it
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- Multidimensional exposure architecture shapes vaping-associated transcriptomic dysregulation in oral epithelium.Frontiers in oncology · 2026Article
- BOGO: A Proteome-Wide Gene Overexpression Platform for Discovering Rational Cancer Combination Therapies.bioRxiv : the preprint server for biology · 2025Article
- Comprehensive bioinformatics analysis of omics data to reveal molecular mechanisms and biomarkers in multiple cancers.In silico pharmacology · 2025Article
- Strong correlation of gene counts and differentially expressed genes between a 3' RNA-Seq and an RNA hybridization platform in transcriptome analyses from canine archival tissues.Frontiers in veterinary science · 2025Article
- Normalization and Selecting Non-Differentially Expressed Genes Improve Machine Learning Modelling of Cross-Platform Transcriptomic Data.Transactions on artificial intelligence · 2025Article
- Differential gene expression analysis pipelines and bioinformatic tools for the identification of specific biomarkers: A review.Computational and structural biotechnology journal · 2024Review
- A brief guide to analyzing expression quantitative trait loci.Molecules and cells · 2024Review
- Machine Learning Analysis of RNA-Seq Data Identifies Key Gene Signatures and Pathways in Mpox Virus-Induced Gastrointestinal Complications Using Colon Organoid Models.International journal of molecular sciences · 2024Article
- The effects ofMicrobiology spectrum · 2024Article
- SGCP: a spectral self-learning method for clustering genes in co-expression networks.BMC bioinformatics · 2024Article
- Detection of microRNAs expression signatures in vitreous humor of intraocular tuberculosis.Molecular biology reports · 2023Article
- Statistical normalization methods in microbiome data with application to microbiome cancer research.Gut microbes · 2023Review
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- Evaluating and Validating Sunflower Reference Genes for Q-PCR Studies Under High Temperature Condition.Iranian journal of biotechnology · 2023Article
- Bulk and Single-Cell RNA-Seq Analyses for Studies of Spermatogonia.Methods in molecular biology (Clifton, N.J.) · 2023Article
- scMAGIC: accurately annotating single cells using two rounds of reference-based classification.Nucleic acids research · 2022Article
- Article
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3 authors.
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Abstract
Normalization of RNA-Seq data has proven essential to ensure accurate inferences and replication of findings. Hence, various normalization methods have been proposed for various technical artifacts that can be present in high-throughput sequencing transcriptomic studies. In this study, we set out to compare the widely used library size normalization methods (UQ, TMM, and RLE) and across sample normalization methods (SVA, RUV, and PCA) for RNA-Seq data using publicly available data from The Cancer Genome Atlas (TCGA) cervical cancer study. Additionally, an extensive simulation study was completed to compare the performance of the across sample normalization methods in estimating technical artifacts. Lastly, we investigated the effect of reduction in degrees of freedom in the normalized data and their impact on downstream differential expression analysis results. Based on this study, the TMM and RLE library size normalization methods give similar results for CESC dataset. In addition, the simulated datasets results show that the SVA ("BE") method outperforms the other methods (SVA "Leek", PCA) by correctly estimating the number of latent artifacts. Moreover, ignoring the loss of degrees of freedom due to normalization results in an inflated type I error rates. We recommend adjusting not only for library size differences but also the assessment of known and unknown technical artifacts in the data, and if needed, complete across sample normalization. In addition, we suggest that one includes the known and estimated latent artifacts in the design matrix to correctly account for the loss in degrees of freedom, as opposed to completing the analysis on the post-processed normalized data.
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