ArticleAlgorithms for molecular biology : AMB2012
A normalization strategy for comparing tag count data.
Article in Algorithms for molecular biology : AMB, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.
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Who cites it
47 citing papers in PubMed.
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- Statistical normalization methods in microbiome data with application to microbiome cancer research.Gut microbes · 2023Review
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- Differential expression analysis using a model-based gene clustering algorithm for RNA-seq data.BMC bioinformatics · 2021Article
- Precision modeling of gall bladder cancer patients in mice based on orthotopic implantation of organoid-derived tumor buds.Oncogenesis · 2021Article
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- Toward uncharted territory of cellular heterogeneity: advances and applications of single-cell RNA-seq.Journal of translational genetics and genomics · 2021Article
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- A graph-based algorithm for RNA-seq data normalization.PloS one · 2020Article
- Commentary: A Systematic Evaluation of Single Cell RNA-Seq Analysis Pipelines.Frontiers in genetics · 2020Article
- TCC-GUI: a Shiny-based application for differential expression analysis of RNA-Seq count data.BMC research notes · 2019Article
- Global transcriptome analysis of pig induced pluripotent stem cells derived from six and four reprogramming factors.Scientific data · 2019Article
- Transcriptome analyses of tumor-adjacent somatic tissues reveal genes co-expressed with transposable elements.Mobile DNA · 2019Article
- Accurate Classification of Differential Expression Patterns in a Bayesian Framework With Robust Normalization for Multi-Group RNA-Seq Count Data.Bioinformatics and biology insights · 2019Article
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3 authors.
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Abstract
backgroundHigh-throughput sequencing, such as ribonucleic acid sequencing (RNA-seq) and chromatin immunoprecipitation sequencing (ChIP-seq) analyses, enables various features of organisms to be compared through tag counts. Recent studies have demonstrated that the normalization step for RNA-seq data is critical for a more accurate subsequent analysis of differential gene expression. Development of a more robust normalization method is desirable for identifying the true difference in tag count data.
resultsWe describe a strategy for normalizing tag count data, focusing on RNA-seq. The key concept is to remove data assigned as potential differentially expressed genes (DEGs) before calculating the normalization factor. Several R packages for identifying DEGs are currently available, and each package uses its own normalization method and gene ranking algorithm. We compared a total of eight package combinations: four R packages (edgeR, DESeq, baySeq, and NBPSeq) with their default normalization settings and with our normalization strategy. Many synthetic datasets under various scenarios were evaluated on the basis of the area under the curve (AUC) as a measure for both sensitivity and specificity. We found that packages using our strategy in the data normalization step overall performed well. This result was also observed for a real experimental dataset.
conclusionOur results showed that the elimination of potential DEGs is essential for more accurate normalization of RNA-seq data. The concept of this normalization strategy can widely be applied to other types of tag count data and to microarray data.
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