We are analyzing https://link.springer.com/article/10.1186/gb-2014-15-2-r29.

Title:
voom: precision weights unlock linear model analysis tools for RNA-seq read counts | Genome Biology
Description:
New normal linear modeling strategies are presented for analyzing read counts from RNA-seq experiments. The voom method estimates the mean-variance relationship of the log-counts, generates a precision weight for each observation and enters these into the limma empirical Bayes analysis pipeline. This opens access for RNA-seq analysts to a large body of methodology developed for microarrays. Simulation studies show that voom performs as well or better than count-based RNA-seq methods even when the data are generated according to the assumptions of the earlier methods. Two case studies illustrate the use of linear modeling and gene set testing methods.
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Keywords {🔍}

gene, methods, rnaseq, article, voom, genes, expression, pubmed, data, analysis, google, scholar, figure, counts, logcpm, values, meanvariance, trend, library, differential, count, number, sizes, size, libraries, read, central, distribution, linear, set, rna, samples, rate, cas, statistical, standard, function, limma, control, smyth, empirical, error, microarray, model, results, limmatrend, simulations, sequencing, variation, reads,

Topics {✒️}

org/packages/release/bioc/html/limma strand-specific rna-seq analysis count-based rna-seq methods square-root standard deviations square-root standard deviation article treats log-cpm x-linked gene expression good-turing frequency estimation rna-seq profiles consist rna-seq profiles give article number r29 multifactor rna-seq experiments mouse rna-seq datasets specialist rna-seq methods summarize rna-seq data converts rna-seq data rna-seq data consist dba/2j mouse striatum inverse chi-square simulation testing rna-seq data inverse chi-square distribution existing rna-seq methods rna-seq analysis methods enable quasi-likelihood testing analyzing rna-seq data rna-seq mouse data gene-wise standard deviations high-throughput rna sequencing real rna-seq experiments real rna-seq data empirical bayes f-tests genome-wide statistical methods rna-seq read counts rna-seq read counts massive-scale mrna sequencing negative binomial dispersion article download pdf performing count-based methods real rna-seq dataset count-based packages implement log-cpm decreases steadily gene-wise biological variation gene-wise linear models equi-spaced development stages examining normal-based methods rna-seq counts rely org/smyth/pubs/robustebayespreprint extended quasi-likelihood function rna-seq count libraries special-purpose count distributions

Schema {🗺️}

WebPage:
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         headline:voom: precision weights unlock linear model analysis tools for RNA-seq read counts
         description:New normal linear modeling strategies are presented for analyzing read counts from RNA-seq experiments. The voom method estimates the mean-variance relationship of the log-counts, generates a precision weight for each observation and enters these into the limma empirical Bayes analysis pipeline. This opens access for RNA-seq analysts to a large body of methodology developed for microarrays. Simulation studies show that voom performs as well or better than count-based RNA-seq methods even when the data are generated according to the assumptions of the earlier methods. Two case studies illustrate the use of linear modeling and gene set testing methods.
         datePublished:2014-02-03T00:00:00Z
         dateModified:2014-02-03T00:00:00Z
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            Differentially Express
            Negative Binomial
            Library Size
            Precision Weight
            Error Rate Control
            Animal Genetics and Genomics
            Human Genetics
            Plant Genetics and Genomics
            Microbial Genetics and Genomics
            Bioinformatics
            Evolutionary Biology
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ScholarlyArticle:
      headline:voom: precision weights unlock linear model analysis tools for RNA-seq read counts
      description:New normal linear modeling strategies are presented for analyzing read counts from RNA-seq experiments. The voom method estimates the mean-variance relationship of the log-counts, generates a precision weight for each observation and enters these into the limma empirical Bayes analysis pipeline. This opens access for RNA-seq analysts to a large body of methodology developed for microarrays. Simulation studies show that voom performs as well or better than count-based RNA-seq methods even when the data are generated according to the assumptions of the earlier methods. Two case studies illustrate the use of linear modeling and gene set testing methods.
      datePublished:2014-02-03T00:00:00Z
      dateModified:2014-02-03T00:00:00Z
      pageStart:1
      pageEnd:17
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         Differentially Express
         Negative Binomial
         Library Size
         Precision Weight
         Error Rate Control
         Animal Genetics and Genomics
         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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            affiliation:
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                     name:Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia
                     type:PostalAddress
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                  address:
                     name:Department of Computing and Information Systems, The University of Melbourne, Parkville, Australia
                     type:PostalAddress
                  type:Organization
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            affiliation:
                  name:Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research
                  address:
                     name:Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia
                     type:PostalAddress
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                  name:The University of Melbourne
                  address:
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         type:PostalAddress
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         name:Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia
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Person:
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      affiliation:
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            address:
               name:Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia
               type:PostalAddress
            type:Organization
            name:The University of Melbourne
            address:
               name:Department of Medical Biology, The University of Melbourne, Parkville, Australia
               type:PostalAddress
            type:Organization
      name:Yunshun Chen
      affiliation:
            name:Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research
            address:
               name:Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia
               type:PostalAddress
            type:Organization
            name:The University of Melbourne
            address:
               name:Department of Medical Biology, The University of Melbourne, Parkville, Australia
               type:PostalAddress
            type:Organization
      name:Wei Shi
      affiliation:
            name:Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research
            address:
               name:Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia
               type:PostalAddress
            type:Organization
            name:The University of Melbourne
            address:
               name:Department of Computing and Information Systems, The University of Melbourne, Parkville, Australia
               type:PostalAddress
            type:Organization
      name:Gordon K Smyth
      affiliation:
            name:Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research
            address:
               name:Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia
               type:PostalAddress
            type:Organization
            name:The University of Melbourne
            address:
               name:Department of Mathematics and Statistics, The University of Melbourne, Parkville, Australia
               type:PostalAddress
            type:Organization
      email:[email protected]
PostalAddress:
      name:Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia
      name:Department of Medical Biology, The University of Melbourne, Parkville, Australia
      name:Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia
      name:Department of Medical Biology, The University of Melbourne, Parkville, Australia
      name:Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia
      name:Department of Computing and Information Systems, The University of Melbourne, Parkville, Australia
      name:Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia
      name:Department of Mathematics and Statistics, The University of Melbourne, Parkville, Australia

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