text: user4121: I am currently doing a Differential Gene Expression of RNAseq data in Lung Adenocarcinoma using TCGAbiolinks. In the data Preprocessing step TCGAanalyze_Normalisation, I am confused as to which method to select for normalization (geneLength or gcContent). What are the possible implications of both methods and how to decide which is the best suited model for the analysis?
  Is there a better way to perform the DGE ?
  
  user1269: The TCGAbiolinks reference manual provides a clear explanation of the two methods:
  
  Normalization for RNA-Seq Numerical and graphical summaries of RNA-Seq
    read data. Withinlane normalization procedures to adjust for
    GC-content effect (or other gene-level effects) on read counts: loess
    robust local regression, global-scaling, and full-quantile
    normalization (Risso et al., 2011). Between-lane normalization
    procedures to adjust for distributional differences between lanes
    (e.g., sequencing depth): global-scaling and full-quantile
    normalization (Bullard et al., 2010).
  
  Ideally, you would know if your samples spanned different lanes or not, which would indicate which you should use - "geneLength" if so, "gcContent" if not. If you're using TCGA data, that metadata is likely available somewhere.
  
original_text: 4121: <p>I am currently doing a Differential Gene Expression of RNAseq data in Lung Adenocarcinoma using TCGAbiolinks. In the data Preprocessing step TCGAanalyze_Normalisation, I am confused as to which method to select for normalization (geneLength or gcContent). What are the possible implications of both methods and how to decide which is the best suited model for the analysis?</p>
  
  <p>Is there a better way to perform the DGE ?</p>
   1269: <p>The <a href="https://bioconductor.org/packages/release/bioc/manuals/TCGAbiolinks/man/TCGAbiolinks.pdf" rel="nofollow noreferrer">TCGAbiolinks reference manual</a> provides a clear explanation of the two methods:</p>
  
  <blockquote>
    <p>Normalization for RNA-Seq Numerical and graphical summaries of RNA-Seq
    read data. Withinlane normalization procedures to adjust for
    GC-content effect (or other gene-level effects) on read counts: loess
    robust local regression, global-scaling, and full-quantile
    normalization (Risso et al., 2011). Between-lane normalization
    procedures to adjust for distributional differences between lanes
    (e.g., sequencing depth): global-scaling and full-quantile
    normalization (Bullard et al., 2010).</p>
  </blockquote>
  
  <p>Ideally, you would know if your samples spanned different lanes or not, which would indicate which you should use - "geneLength" if so, "gcContent" if not. If you're using TCGA data, that metadata is likely available somewhere.</p>
  