Answers.Body: <p>As I already told you here (<a href="https://bioinformatics.stackexchange.com/questions/22414/when-analysing-microarray-data-is-it-need-to-do-normalization-and-standardizati">When analysing microarray data, is it need to do normalization and standardization both?</a>) log2 is a transformation, not a normalization so unless these data were already normalized you cannot expect equal medians.</p>
  <p>What the method section tells is</p>
  <blockquote>
  <p>&quot;The scanned images were analyzed with Feature Extraction Software 9.5 (Agilent) using default parameters (protocol : GE1-v5_95_Feb07  and Grid: 014850_D_F_20060807) to obtain background subtracted and spatially detrended Processed Signal intensities. Features flagged in Feature Extraction as Feature Non-uniform outliers were excluded.&quot;</p>
  </blockquote>
  <p>So that is probably what these values are. Hence, you might want to apply something like quantile normalization (for example via the limma package) to the log2-transformed data before applying limma for differential testing or any other downstream normalization. Read the limma manual. Note that I did not invest much time to read my self into that experiment. Treat advise with care and collaborate with local experienced people if you need hands-on guidance.</p>
  
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Answers.CreationDate: 2024-04-22T16:15:48.020Z
Answers.Text: Thank you for your patience in explaining. I confused these two concepts and mistakenly thought that log2 transformation is also a form of normalization. I will try to normalize the data via limma package after log2-transformation.
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Body: <p>I know it is necessary to normalize the GEO microassay data before analysing. Howerver, after using log2 to normalize the data and checking with boxplot, I found the data is still not align in the same level:
  <a href="https://i.sstatic.net/pIuHP.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/pIuHP.png" alt="enter image description here" /></a></p>
  <p>I notice the expression in some samples is lower overall, can I add a value manually to it?</p>
  <p>Update: As @ATPoint had pointed out, log2 is not a normalization method but transformation. So it is normal to see the median number of sample data not at the same level. Actually, I need to normalize it via limma or other R package.</p>
  
Tags: <normalization><microarray>
Title: Inconsistent microarray expression levels after normalizing with log2
ThreadText: # Inconsistent microarray expression levels after normalizing with log2
  
  (Asked by: username_0 on 2024-04-22)
  
  I know it is necessary to normalize the GEO microassay data before analysing. Howerver, after using log2 to normalize the data and checking with boxplot, I found the data is still not align in the same level:
  [![enter image description here](https://i.sstatic.net/pIuHP.png)](https://i.sstatic.net/pIuHP.png)
  
  I notice the expression in some samples is lower overall, can I add a value manually to it?
  
  Update: As @ATPoint had pointed out, log2 is not a normalization method but transformation. So it is normal to see the median number of sample data not at the same level. Actually, I need to normalize it via limma or other R package.
  
  **Tags:** normalization, microarray
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2024-04-22. Score: 2 | Accepted answer
  
  As I already told you here ([When analysing microarray data, is it need to do normalization and standardization both?](https://bioinformatics.stackexchange.com/questions/22414/when-analysing-microarray-data-is-it-need-to-do-normalization-and-standardizati)) log2 is a transformation, not a normalization so unless these data were already normalized you cannot expect equal medians.
  
  What the method section tells is
  
  > "The scanned images were analyzed with Feature Extraction Software 9.5 (Agilent) using default parameters (protocol : GE1-v5\_95\_Feb07 and Grid: 014850\_D\_F\_20060807) to obtain background subtracted and spatially detrended Processed Signal intensities. Features flagged in Feature Extraction as Feature Non-uniform outliers were excluded."
  
  So that is probably what these values are. Hence, you might want to apply something like quantile normalization (for example via the limma package) to the log2-transformed data before applying limma for differential testing or any other downstream normalization. Read the limma manual. Note that I did not invest much time to read my self into that experiment. Treat advise with care and collaborate with local experienced people if you need hands-on guidance.
  
  > **Comments:**
  > **username_0**: Thank you for your patience in explaining. I confused these two concepts and mistakenly thought that log2 transformation is also a form of normalization. I will try to normalize the data via limma package after log2-transformation.  
  
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