Answers.Body: <p>You have only two samples?</p>
  
  <p>You aren't going to be able to draw strong conclusions from that no matter what you do.  Clever statistics don't work without replicates.</p>
  
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Answers.Body: <p>What I have generally done in the past is to process the data using <code>voom</code> in the <code>limma</code> package for bulk RNASeq. Inside <code>voom</code> you can call for different normalization methods to be used - "TMM" works fine for me and, is advocated by many in the field.</p>
  
  <p><code>voom</code> will output an object containing the normalized expression values in a log2 scale, which, also in my experience, has worked out just fine for calculating log fold changes.</p>
  
  <p>Check out this link for more info on the package as well as normalization methods: <a href="https://www.bioconductor.org/packages/devel/workflows/vignettes/RNAseq123/inst/doc/limmaWorkflow.html" rel="nofollow noreferrer">https://www.bioconductor.org/packages/devel/workflows/vignettes/RNAseq123/inst/doc/limmaWorkflow.html</a>
  It is a very thorough introduction to the package and all of its capabilities.</p>
  
  <p>Good luck!</p>
  
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Answers.Body: <p>It's not a good idea to do tpm normalisation prior to differential expression analysis, because the actual read counts are useful to determine shot noise and statistical significance. <a href="https://bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.html" rel="nofollow noreferrer">DESeq2</a> includes read normalisation as part of its methods for differential expression analysis.</p>
  
  <p>I think shot noise is best explained in terms of shooting photons at a target, as found on <a href="https://en.wikipedia.org/wiki/Shot_noise#Origin" rel="nofollow noreferrer">Wikipedia</a>:</p>
  
  <blockquote>
    <p>Shot noise exists because phenomena such as light and electric current
    consist of the movement of discrete (also called "quantized")
    'packets'. Consider light—a stream of discrete photons—coming out of a
    laser pointer and hitting a wall to create a visible spot. The
    fundamental physical processes that govern light emission are such
    that these photons are emitted from the laser at random times; but the
    many billions of photons needed to create a spot are so many that the
    brightness, the number of photons per unit of time, varies only
    infinitesimally with time. However, if the laser brightness is reduced
    until only a handful of photons hit the wall every second, the
    relative fluctuations in number of photons, i.e., brightness, will be
    significant, just as when tossing a coin a few times. These
    fluctuations are shot noise.</p>
  </blockquote>
  
  <p>Sequencing also has shot noise effects because the sequencing process is a random sampling method. For low-abundance targets, the likelihood of sampling a target is low, so the effect of a single hit is amplified. This reduces the statistical significance when comparing different low-abundance expression values, because the variation in expression is highly dependent on which targets won the sampling lottery. The shot noise variation / expression curve can be seen in <a href="https://genomebiology.biomedcentral.com/articles/10.1186/gb-2010-11-10-r106#Fig7" rel="nofollow noreferrer">Figure 7 of this paper</a>.</p>
  
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Answers.Text: I agree with TPM for a lot of reasons, unfortunately the data was already in TPM. Can you explain more about how read counts are useful to determine shot noise and statistical significance? Thanks
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Body: <p>I have two bulk RNA-Seq samples, already tpm-normalized.</p>
  
  <p>I would like to know what is a reasonable normalization procedure to enable downstream log fold-change estimation.</p>
  
  <p>The distribution of the two samples using the common set of genes looks similar:</p>
  
  <p><a href="https://i.sstatic.net/mcXNW.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/mcXNW.png" alt="TPM distribution"></a></p>
  
  <p>However, the two samples have only been tpm-normalized, is it enough to guarantee reliable fold-change estimation? Should I use another normalization procedure, e.g. <a href="https://en.wikipedia.org/wiki/Quantile_normalization" rel="nofollow noreferrer">Quantile Normalization</a>, before comparison?</p>
  
  <p>My objective is to define a signature using the genes that are up-regulated in Sample1 with respect to Sample0, and vice versa. I'm using log fold-changes, but I'm concerned that their value may be affected by each sample distribution.</p>
  
  <p>Do you also have suggestions for the definition of up-regulated genes with these data?</p>
  
  <p><a href="https://i.sstatic.net/aVoPa.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/aVoPa.png" alt="scatter"></a></p>
  
Tags: <rna-seq><normalization><fold-change>
Title: Normalization for two bulk RNA-Seq samples to enable reliable fold-change estimation between genes
ThreadText: # Normalization for two bulk RNA-Seq samples to enable reliable fold-change estimation between genes
  
  (Asked by: username_0 on 2019-02-28)
  
  I have two bulk RNA-Seq samples, already tpm-normalized.
  
  I would like to know what is a reasonable normalization procedure to enable downstream log fold-change estimation.
  
  The distribution of the two samples using the common set of genes looks similar:
  
  [![TPM distribution](https://i.sstatic.net/mcXNW.png)](https://i.sstatic.net/mcXNW.png)
  
  However, the two samples have only been tpm-normalized, is it enough to guarantee reliable fold-change estimation? Should I use another normalization procedure, e.g. [Quantile Normalization](https://en.wikipedia.org/wiki/Quantile_normalization), before comparison?
  
  My objective is to define a signature using the genes that are up-regulated in Sample1 with respect to Sample0, and vice versa. I'm using log fold-changes, but I'm concerned that their value may be affected by each sample distribution.
  
  Do you also have suggestions for the definition of up-regulated genes with these data?
  
  [![scatter](https://i.sstatic.net/aVoPa.png)](https://i.sstatic.net/aVoPa.png)
  
  **Tags:** rna-seq, normalization, fold-change
  
  
  ---
  
  ## 3 answers
  
  ### Answer by username_1 on 2019-02-28. Score: 5
  
  You have only two samples?
  
  You aren't going to be able to draw strong conclusions from that no matter what you do. Clever statistics don't work without replicates.
  
  
  ---
  
  ### Answer by username_2 on 2019-02-28. Score: 3
  
  What I have generally done in the past is to process the data using `voom` in the `limma` package for bulk RNASeq. Inside `voom` you can call for different normalization methods to be used - "TMM" works fine for me and, is advocated by many in the field.
  
  `voom` will output an object containing the normalized expression values in a log2 scale, which, also in my experience, has worked out just fine for calculating log fold changes.
  
  Check out this link for more info on the package as well as normalization methods: <https://www.bioconductor.org/packages/devel/workflows/vignettes/RNAseq123/inst/doc/limmaWorkflow.html>
  It is a very thorough introduction to the package and all of its capabilities.
  
  Good luck!
  
  
  ---
  
  ### Answer by username_3 on 2019-02-28. Score: 3
  
  It's not a good idea to do tpm normalisation prior to differential expression analysis, because the actual read counts are useful to determine shot noise and statistical significance. [DESeq2](https://bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.html) includes read normalisation as part of its methods for differential expression analysis.
  
  I think shot noise is best explained in terms of shooting photons at a target, as found on [Wikipedia](https://en.wikipedia.org/wiki/Shot_noise#Origin):
  
  > Shot noise exists because phenomena such as light and electric current
  > consist of the movement of discrete (also called "quantized")
  > 'packets'. Consider light—a stream of discrete photons—coming out of a
  > laser pointer and hitting a wall to create a visible spot. The
  > fundamental physical processes that govern light emission are such
  > that these photons are emitted from the laser at random times; but the
  > many billions of photons needed to create a spot are so many that the
  > brightness, the number of photons per unit of time, varies only
  > infinitesimally with time. However, if the laser brightness is reduced
  > until only a handful of photons hit the wall every second, the
  > relative fluctuations in number of photons, i.e., brightness, will be
  > significant, just as when tossing a coin a few times. These
  > fluctuations are shot noise.
  
  Sequencing also has shot noise effects because the sequencing process is a random sampling method. For low-abundance targets, the likelihood of sampling a target is low, so the effect of a single hit is amplified. This reduces the statistical significance when comparing different low-abundance expression values, because the variation in expression is highly dependent on which targets won the sampling lottery. The shot noise variation / expression curve can be seen in [Figure 7 of this paper](https://genomebiology.biomedcentral.com/articles/10.1186/gb-2010-11-10-r106#Fig7).
  
  > **Comments:**
  > **username_0**: I agree with TPM for a lot of reasons, unfortunately the data was already in TPM. Can you explain more about how read counts are useful to determine shot noise and statistical significance? Thanks  
  
  ---
  
  