2019
DOI: 10.1177/1177932219860817
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Accurate Classification of Differential Expression Patterns in a Bayesian Framework With Robust Normalization for Multi-Group RNA-Seq Count Data

Abstract: Empirical Bayes is a choice framework for differential expression (DE) analysis for multi-group RNA-seq count data. Its characteristic ability to compute posterior probabilities for predefined expression patterns allows users to assign the pattern with the highest value to the gene under consideration. However, current Bayesian methods such as baySeq and EBSeq can be improved, especially with respect to normalization. Two R packages (baySeq and EBSeq) with their default normalization settings and with other no… Show more

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Cited by 17 publications
(11 citation statements)
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“…The TCC+baySeq (ver. 2.18.0) method with a false discovery rate of 0.01 was used for the identification of the DEGs among the synergid, egg, and central cells of the wild type [ 34 ]. Gene Ontology (GO) enrichment analysis was performed using g:Profiler (g:COSt; https://biit.cs.ut.ee/gprofiler/gost ).…”
Section: Methodsmentioning
confidence: 99%
“…The TCC+baySeq (ver. 2.18.0) method with a false discovery rate of 0.01 was used for the identification of the DEGs among the synergid, egg, and central cells of the wild type [ 34 ]. Gene Ontology (GO) enrichment analysis was performed using g:Profiler (g:COSt; https://biit.cs.ut.ee/gprofiler/gost ).…”
Section: Methodsmentioning
confidence: 99%
“…The TCC+baySeq (ver. 2.18.0) method with a false discovery rate < 0.01 was used for the identification of the differentially expressed genes among the synergid, egg, and central cells of the wild type (Osabe et al, 2019). Hierarchical clustering of the gene expression data was carried out using phylogram package (https://github.com/rambaut/figtree/).…”
Section: Methodsmentioning
confidence: 99%
“…RNA sequencing (RNA-seq) is a common tool for obtaining data related to gene expression (Mortazavi et al, 2008 ). Identification of genes exhibiting differential expression (DE) in different groups or conditions is critical to analysis of RNA-seq data (Osabe et al, 2019 ). Recently, Vieth et al ( 2019 ) evaluated a total of 3,000 possible single-cell RNA-seq (scRNA-seq) analysis pipelines, encompassing the entire analytical process—from library preparation protocols to identification of DE genes.…”
mentioning
confidence: 99%