ReviewFrontiers in bioengineering and biotechnology2019
Normalization Methods for the Analysis of Unbalanced Transcriptome Data: A Review.
Review in Frontiers in bioengineering and biotechnology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers, 1 of them a synthesis that pooled it.
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Who cites it
48 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pathogenomic Signature and Aberrant Neurogenic Events in Experimental Cerebral Ischemic Stroke: A Neurotranscriptomic-Based Implication for Dementia.Journal of Alzheimer's disease : JAD · 2023Pooled it
- Absence of autoantibodies linked to cancer and autoimmune disorders 26 weeks after BNT162b2 boosting in CoronaVac- primed individuals.Human vaccines & immunotherapeutics · 2026Article
- Large-scale transcriptome analysis reveals KIF20A as a novel indicator of cisplatin resistance in high-grade serous ovarian cancer.Journal of ovarian research · 2026Article
- Transcriptomic Meta-Analysis as a Framework for Robust Cross-Study Biological Inference.International journal of molecular sciences · 2026Review
- T-cell and autoantibody profiling for primary immune regulatory disorders.The Journal of allergy and clinical immunology · 2025Article
- Bioinformatics perspectives on transcriptomics: A comprehensive review of bulk and single-cell RNA sequencing analyses.Quantitative biology (Beijing, China) · 2025Review
- Correcting scale distortion in RNA sequencing data.BMC bioinformatics · 2025Article
- Less is more: relative rank is more informative than absolute abundance for compositional NGS data.Briefings in functional genomics · 2025Review
- Temporal Expression Analysis to Unravel Gene Regulatory Dynamics by microRNAs.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Augusta: From RNA-Seq to gene regulatory networks and Boolean models.Computational and structural biotechnology journal · 2024Article
- Assessing and mitigating batch effects in large-scale omics studies.Genome biology · 2024Review
- Pairwise analysis of gene expression for oral squamous cell carcinoma via a large-scale transcriptome integration.Journal of cellular and molecular medicine · 2024Article
- Forecasting and analyzing influenza activity in Hebei Province, China, using a CNN-LSTM hybrid model.BMC public health · 2024Article
- IMPRINTS.CETSA and IMPRINTS.CETSA.app: an R package and a Shiny application for the analysis and interpretation of IMPRINTS-CETSA data.Briefings in bioinformatics · 2024Article
- Prediction of Prostate Cancer Risk Stratification Based on A Nonlinear Transformation Stacking Learning Strategy.International neurourology journal · 2024Article
- MrGPS: an m6A-related gene pair signature to predict the prognosis and immunological impact of glioma patients.Briefings in bioinformatics · 2023Article
- Data-driven identification of total RNA expression genes for estimation of RNA abundance in heterogeneous cell types highlighted in brain tissue.Genome biology · 2023Article
- Transcriptomic Insight into the Pollen Tube Growth ofPlants (Basel, Switzerland) · 2023Article
- Article
- Co-expression module analysis reveals high expression homogeneity for both coding and non-coding genes in sepsis.BMC genomics · 2023Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Dozens of normalization methods for correcting experimental variation and bias in high-throughput expression data have been developed during the last two decades. Up to 23 methods among them consider the skewness of expression data between sample states, which are even more than the conventional methods, such as loess and quantile. From the perspective of reference selection, we classified the normalization methods for skewed expression data into three categories, data-driven reference, foreign reference, and entire gene set. We separately introduced and summarized these normalization methods designed for gene expression data with global shift between compared conditions, including both microarray and RNA-seq, based on the reference selection strategies. To our best knowledge, this is the most comprehensive review of available preprocessing algorithms for the unbalanced transcriptome data. The anatomy and summarization of these methods shed light on the understanding and appropriate application of preprocessing methods.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.