Answers.Body: <p>Actually, there is three terms to introduce, which is normalization, transformation and standardization.</p>
  <p>Normalization corrects for biases such as different baselines of array intensities, or in RNA-seq different read counts and library compositions.</p>
  <p>What you do with log2 here is a transformation, because it brings data to a different scale, so it <em>transforms</em>. log2 is necessary because on normal scale the variance of genes is an almost linear transformation of the magnitude of intensity values (or counts in sequencing experiments), but what you want is not this technical variance, but an information for variability due to treatment or group effects.</p>
  <p>Then standarization is strictly speaking another transformation that brings data from an absolute scale to a relative one. This is also called Z-scoring. Data are transformed to represent the deviation from the mean. See: <a href="https://en.wikipedia.org/wiki/Standard_score" rel="nofollow noreferrer">https://en.wikipedia.org/wiki/Standard_score</a></p>
  <p>To the question &quot;can I do...&quot;, you should read established analysis pipelines, for example the limma documentation, as different arrays need different preprocessing, and this is all established and standard these days so don't reinvent the wheel. Generally, you always analyze arrays on log2 scale.</p>
  
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Body: <p>When we do differential expression analysis, it is common to normalize the data using a log transform:</p>
  <pre><code>exprs(gse) &lt;- log2(exprs(gse))
  </code></pre>
  <p>This ensures that the expression data are more comparable and consistent.</p>
  <p>However, I have recently learnt that there is a method called <em>standarization</em> which can also be used to preprocess data. So what is the difference between these two methods? Can I just do <em>normalization</em> for differential expression analysis?</p>
  
Tags: <normalization><microarray>
Title: When analysing microarray data, is it need to do normalization and standardization both?
ThreadText: # When analysing microarray data, is it need to do normalization and standardization both?
  
  (Asked by: username_0 on 2024-04-19)
  
  When we do differential expression analysis, it is common to normalize the data using a log transform:
  
  ```
  exprs(gse) <- log2(exprs(gse))
  
  ```
  
  This ensures that the expression data are more comparable and consistent.
  
  However, I have recently learnt that there is a method called *standarization* which can also be used to preprocess data. So what is the difference between these two methods? Can I just do *normalization* for differential expression analysis?
  
  **Tags:** normalization, microarray
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2024-04-19. Score: 1 | Accepted answer
  
  Actually, there is three terms to introduce, which is normalization, transformation and standardization.
  
  Normalization corrects for biases such as different baselines of array intensities, or in RNA-seq different read counts and library compositions.
  
  What you do with log2 here is a transformation, because it brings data to a different scale, so it *transforms*. log2 is necessary because on normal scale the variance of genes is an almost linear transformation of the magnitude of intensity values (or counts in sequencing experiments), but what you want is not this technical variance, but an information for variability due to treatment or group effects.
  
  Then standarization is strictly speaking another transformation that brings data from an absolute scale to a relative one. This is also called Z-scoring. Data are transformed to represent the deviation from the mean. See: <https://en.wikipedia.org/wiki/Standard_score>
  
  To the question "can I do...", you should read established analysis pipelines, for example the limma documentation, as different arrays need different preprocessing, and this is all established and standard these days so don't reinvent the wheel. Generally, you always analyze arrays on log2 scale.
  
  
  ---
  
  