We are analyzing https://link.springer.com/article/10.1186/gb-2010-11-10-r106.

Title:
Differential expression analysis for sequence count data | Genome Biology
Description:
High-throughput sequencing assays such as RNA-Seq, ChIP-Seq or barcode counting provide quantitative readouts in the form of count data. To infer differential signal in such data correctly and with good statistical power, estimation of data variability throughout the dynamic range and a suitable error model are required. We propose a method based on the negative binomial distribution, with variance and mean linked by local regression and present an implementation, DESeq, as an R/Bioconductor package.
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Keywords {🔍}

data, variance, replicates, figure, distribution, article, samples, gene, counts, google, scholar, expression, file, deseq, values, genes, rnaseq, differential, edger, noise, additional, condition, size, analysis, model, poisson, count, biological, sequencing, sample, supplementary, shot, estimate, expressed, range, binomial, test, note, genome, negative, show, section, raw, method, error, regression, package, variation, small, shows,

Topics {✒️}

org/scipy/raw-attachment/ticket/620/loader2000fast de/users/anders/htseq/] deseq genome-wide profiles org/web/packages/locfit/] agresti glioblastoma-derived neural stem-cells pol-ii chip-seq data article number r106 article download pdf negative binomial dispersion negative binomial distribution 𝜇 𝑖 𝑗 tag-wise dispersion mode negative binomial distributions single-parameter noise model european union research open software development simon anders 𝑤 𝑖 𝜌 tag-wise dispersion estimation size factor sjrepresents author information authors conducted rna-seq experiments size factor sj transcription factor binding strong shot noise full size image 𝐾 𝑖 𝑗 shot noise term shot noise limit common-scale count data pre-determined binding regions negative binomial negative-binomial fly rna-seq data org/www-huber poisson shot noise poisson-based χ2 test common-scale sample variances high-level functions gamma-family glm fit differential expression analysis deseq-estimated size factors maximum quasi-likelihood dependent local regression computational biology solutions article anders privacy choices/manage cookies expression strength parameter creative commons license cancerous neural stem

Questions {❓}

Why is it necessary to develop new statistical methodology for sequence count data?

Schema {🗺️}

WebPage:
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         headline:Differential expression analysis for sequence count data
         description:High-throughput sequencing assays such as RNA-Seq, ChIP-Seq or barcode counting provide quantitative readouts in the form of count data. To infer differential signal in such data correctly and with good statistical power, estimation of data variability throughout the dynamic range and a suitable error model are required. We propose a method based on the negative binomial distribution, with variance and mean linked by local regression and present an implementation, DESeq, as an R/Bioconductor package.
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            Negative Binomial Distribution
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            Size Factor
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            Human Genetics
            Plant Genetics and Genomics
            Microbial Genetics and Genomics
            Bioinformatics
            Evolutionary Biology
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      headline:Differential expression analysis for sequence count data
      description:High-throughput sequencing assays such as RNA-Seq, ChIP-Seq or barcode counting provide quantitative readouts in the form of count data. To infer differential signal in such data correctly and with good statistical power, estimation of data variability throughout the dynamic range and a suitable error model are required. We propose a method based on the negative binomial distribution, with variance and mean linked by local regression and present an implementation, DESeq, as an R/Bioconductor package.
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         Shot Noise
         Local Regression
         Negative Binomial Distribution
         Negative Binomial
         Size Factor
         Animal Genetics and Genomics
         Human Genetics
         Plant Genetics and Genomics
         Microbial Genetics and Genomics
         Bioinformatics
         Evolutionary Biology
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