Methods for analyzing next-generation sequencing data 20. Properties and statistical models of RNA-seq count data.

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  • Makino Manon
    Graduate School of Agricultural and Life Sciences, The University of Tokyo.
  • Sakamoto Mitsuo
    BioResource Research Center, RIKEN.
  • Shimizu Kentaro
    Graduate School of Agricultural and Life Sciences, The University of Tokyo. Interfaculty Initiative in Information Studies, The University of Tokyo. Collaborative Research Institute for Innovative Microbiology, The University of Tokyo.
  • Kadota Koji
    Graduate School of Agricultural and Life Sciences, The University of Tokyo. Interfaculty Initiative in Information Studies, The University of Tokyo. Collaborative Research Institute for Innovative Microbiology, The University of Tokyo.

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  • 次世代シーケンサーデータの解析手法 第 20 回 RNA-seq カウントデータの性質と統計モデル
  • ジセダイ シーケンサーデータ ノ カイセキ シュホウ(ダイ20カイ)RNA-seq カウントデータ ノ セイシツ ト トウケイ モデル

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<p>RNA-seq is a tool for measuring gene expression and is commonly used for identifying differentially expressed genes (DEGs) under different conditions or groups. Most of the programs for DEGs has been provided as a free software environment called R. Users typically start the analysis with a numerical matrix called “count data”, where each row a gene, each column a sample (a group’s replicate), and each cell the number of counts. We describe the characteristics of the count data with a scatter plot (so-called “mean-variance plot”) displaying the relationship between the mean (x-axis) and variance (y-axis) within replicates. We explain why a statistical model called the negative binomial distribution (NB model) has been used for identifying DEGs.</p>

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