We are analyzing https://link.springer.com/article/10.1186/s13059-014-0550-8.

Title:
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 | Genome Biology
Description:
In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html .
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Keywords {🔍}

genes, dispersion, data, deseq, gene, expression, pubmed, counts, estimates, google, scholar, prior, article, figure, estimate, lfc, differential, analysis, distribution, sample, shrinkage, values, samples, variance, lfcs, rnaseq, file, fold, count, size, standard, estimation, set, logarithmic, sensitivity, algorithms, methods, null, information, number, approach, model, hypothesis, additional, change, central, transformation, fit, log, package,

Topics {✒️}

org/packages/release/bioc/html/deseq2 𝑋 𝑡 wx negative-binomial-based approaches compared chip-seq peak data2013 castration-resistant prostate cancer dba/2j mouse striatum backtracking line search crispr/cas-library assays [46] log-normal prior fits max planck institute cox–reid adjusted likelihood high-throughput sequencing data high-throughput count data multifactor rna-seq experiments comparative rna-seq data log-normal prior distribution noisy gene-wise estimates assign rna-seq reads 𝛼 𝑖 map rna-seq read counts high-uncertainty mle closer gene-wise standard deviation gene-wise dispersion estimates typical rna-seq datasets permutation-based samseq method gene-wise dispersion estimate rna-seq data perform processing rna-seq data article download pdf probabilistic-assignment-based methods maximum-likelihood-based solutions negative binomial family observe discreteness renal cell carcinoma �� cr van wieringen wn high-throughput assays 𝐾 𝑗 related subjects privacy choices/manage cookies 𝜇 𝑗 theory fits reality negative binomial dispersion high-throughput sequencing gamma-family glm regression negative binomial glm high-throughput screening open software development simon anders gene-expression microarray data

Questions {❓}

How should one deal with flagged outliers?

Schema {🗺️}

WebPage:
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         description:In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html .
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            Differential Expression Analysis
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            Observe Fisher Information
            Negative Binomial Generalize Linear Model
            Animal Genetics and Genomics
            Human Genetics
            Plant Genetics and Genomics
            Microbial Genetics and Genomics
            Bioinformatics
            Evolutionary Biology
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         Differential Expression Analysis
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         Observe Fisher Information
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         Human Genetics
         Plant Genetics and Genomics
         Microbial Genetics and Genomics
         Bioinformatics
         Evolutionary Biology
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               type:PostalAddress
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            address:
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               type:PostalAddress
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               type:PostalAddress
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      name:Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Ihnestrasse, Germany
      name:Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
      name:Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany

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