We are analyzing https://link.springer.com/article/10.1186/gb-2010-11-3-r25.

Title:
A scaling normalization method for differential expression analysis of RNA-seq data | Genome Biology
Description:
The fine detail provided by sequencing-based transcriptome surveys suggests that RNA-seq is likely to become the platform of choice for interrogating steady state RNA. In order to discover biologically important changes in expression, we show that normalization continues to be an essential step in the analysis. We outline a simple and effective method for performing normalization and show dramatically improved results for inferring differential expression in simulated and publicly available data sets.
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Keywords {🔍}

normalization, data, genes, expression, pubmed, article, gene, tmm, analysis, rna, number, sample, rnaseq, google, scholar, figure, reads, total, library, methods, samples, additional, method, sequencing, size, file, factor, central, model, values, simulation, counts, distribution, expressed, kidney, housekeeping, cas, observed, statistical, set, liver, microarray, technical, logfoldchanges, scaling, lower, robust, test, genome, similar,

Topics {✒️}

org/web/packages/statmod/index article number r25 article download pdf ensembl gene identifiers length-normalized count data rna-seq data analysis generating rna-seq data interrogate allele-specific expression massive-scale mrna sequencing human tissue transcriptomes long-sage-seq data small-rna-seq data open software development chip-seq experiments relative read count distributions differential expression analysis related subjects gene-wise log-fold discover biologically important log-fold-change caused full size image short read sequencing steady state rna privacy choices/manage cookies high throughput sequencing article robinson transcriptome-wide identification 'virtual length' virtual length [2] full access evidence based selection rna-seq experiments sequencing-based datasets library size normalized larger read counts simulation data poisson-distributed inferring differential expression m-values normalization method rna-seq data digital transcriptome analysis moderated t-statistics alicia oshlack medical research council exploratory data analysis distinct count distributions microarray data analysis gene length biases author information authors rna-seq datasets original library size

Schema {🗺️}

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         headline:A scaling normalization method for differential expression analysis of RNA-seq data
         description:The fine detail provided by sequencing-based transcriptome surveys suggests that RNA-seq is likely to become the platform of choice for interrogating steady state RNA. In order to discover biologically important changes in expression, we show that normalization continues to be an essential step in the analysis. We outline a simple and effective method for performing normalization and show dramatically improved results for inferring differential expression in simulated and publicly available data sets.
         datePublished:2010-03-02T00:00:00Z
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      headline:A scaling normalization method for differential expression analysis of RNA-seq data
      description:The fine detail provided by sequencing-based transcriptome surveys suggests that RNA-seq is likely to become the platform of choice for interrogating steady state RNA. In order to discover biologically important changes in expression, we show that normalization continues to be an essential step in the analysis. We outline a simple and effective method for performing normalization and show dramatically improved results for inferring differential expression in simulated and publicly available data sets.
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         Differential Expression Analysis
         Library Size
         Ensembl Gene Identifier
         Virtual Length
         Animal Genetics and Genomics
         Human Genetics
         Plant Genetics and Genomics
         Microbial Genetics and Genomics
         Bioinformatics
         Evolutionary Biology
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