Normalization, testing, and false discovery rate estimation for RNA-sequencing data.

By Jun Li, Daniela M. Witten, Iain M. Johnstone, Robert Tibshirani. Biostatistics, 2012

366 citations recorded by the source corpus.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/ceaf89d5cd2391e13ebc5e3f1e26002f77465b3d

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In Molecular Biology

1. Gordon K. Smyth
2. Yunshun Chen
3. Wei Shi
4. Laura L. Elo
5. Charity W. Law
6. Jun Li
7. Ana Conesa
8. Sonia Tarazona

Citations in the corpus

• A survey of best practices for RNA-seq data analysis (2016, methodology): “Normalization methods that take this into account are TMM [47], DESeq [48], PoissonSeq [49] and UpperQuartile [45], which ignore highly variable and/or highly expressed features.”
• voom: precision weights unlock linear model analysis tools for RNA-seq read counts (2014, methodology): “This article compares the performance of the limma-based pipelines to edgeR [20,34], DESeq [13], baySeq [21], TSPM [25], PoissonSeq [26] and DSS [22], all of which are based on NB or over-dispersed Poisson distributions.”
• Selecting between‐sample RNA‐Seq normalization methods from the perspective of their assumptions (2016, methodology): “Normalization by testing: PoissonSeq [30] uses an iterative process that alternates between estimating a set of non-differentially expressed genes, and estimating the scaling factor for each sample using that set.”
• Finding consistent patterns: A nonparametric approach for identifying differential expression in RNA-Seq data (2013, methodology): “([14]) did a detailed comparison of many methods on simulated data sets, including (1) their own method, PoissonSeq, (2) SAM (Significance Analysis of Microarrays, [31]) applied to the square root of normalized data, (3) The Poisson distribution based method proposed by [16], which is implemented in”
• Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data (2013, methodology): “• PoissonSeq (v.1.1.2): No minimum expression mean was applied and the number of permutations was 500.”
• Single-cell RNA sequencing technologies and bioinformatics pipelines (2018, methodology): “To overcome the inherent problems in within-sample normalization methods, alternative approaches have been developed 52 – 54 .”