<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>

CC BY-SA 4.0
2019-10-23T01:06:24.073Z
2019-10-23T01:06:24.073Z
Jared Andrews
<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>

<rna-seq>
RNAseq data analysis
# RNAseq data analysis

(Asked by: username_0 on 2019-10-22)

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 ?

**Tags:** rna-seq

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## 1 answers

### Answer by username_1 on 2019-10-23. Score: 2

The [TCGAbiolinks reference manual](https://bioconductor.org/packages/release/bioc/manuals/TCGAbiolinks/man/TCGAbiolinks.pdf) 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.


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