Answers.Body: <p>Processed microarray data is commonly represented as log_2(expression). This data transformation is used because the data more closely fit a normal distribution in log space. With such a transformation, negative values represent very small amounts, rather than the absence of something.</p>
  
  <p>Larger values mean that more stuff is being produced (presumably transcripts), but its significance can only be assessed when combined with other data (e.g. through differential expression analysis).</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-02-06T20:41:12.657Z
Answers.LastActivityDate: 2019-02-06T20:41:12.657Z
Answers.UserDisplayName: gringer
Body: <p>I am currently looking at microarray data from a bioconductor microarray dataset. Specifically, I have data (a snippet) which looks like the following:</p>
  
  <pre><code>   GSM794885   GSM794886   GSM794887   GSM794888   GSM794889 
     -1.67170290 -0.07803822  0.99990560 -1.94692850 -1.78671360 
  </code></pre>
  
  <p>I am wondering how to interpret the values in this microarray dataset. Is it generally the case that larger values mean a more significant effect?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-02-04T10:17:55.167Z
Comments.Text: Try to read more about microarray data analysis first, follow some of the tutorials, and you will understand what these values are.
Comments.UserDisplayName: benn
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-02-04T11:09:32.700Z
Comments.Text: Welcome to the site. Please follow benn advice! Also this values should be significant for what? Provide more information about how did you obtained these values (what is the code needed to reproduce this values)?
Comments.UserDisplayName: llrs
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-02-05T07:51:15.177Z
Comments.Text: Looks like a log2(intensity value), but without more information it's impossible to say for sure...
Comments.UserDisplayName: Devon Ryan
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-02-06T20:42:02.593Z
Comments.Text: It would be great if you could add more context to your question for those who want to help you out with your problem. What type of microarray is this? What are you trying to do with this dataset? What question are you trying to answer?
Comments.UserDisplayName: gringer
Tags: <rna-seq><gene><rna><gene-expression>
Title: How can I interpret gene expression data from Bioconductor packages?
ThreadText: # How can I interpret gene expression data from Bioconductor packages?
  
  (Asked by: username_0 on 2019-02-04)
  
  I am currently looking at microarray data from a bioconductor microarray dataset. Specifically, I have data (a snippet) which looks like the following:
  
  ```
     GSM794885   GSM794886   GSM794887   GSM794888   GSM794889 
     -1.67170290 -0.07803822  0.99990560 -1.94692850 -1.78671360 
  
  ```
  
  I am wondering how to interpret the values in this microarray dataset. Is it generally the case that larger values mean a more significant effect?
  
  **Tags:** rna-seq, gene, rna, gene-expression
  
  > **Comments:**
  > **username_1**: Try to read more about microarray data analysis first, follow some of the tutorials, and you will understand what these values are.  
  > **username_2**: Welcome to the site. Please follow username_1 advice! Also this values should be significant for what? Provide more information about how did you obtained these values (what is the code needed to reproduce this values)?  
  > **username_3**: Looks like a log2(intensity value), but without more information it's impossible to say for sure...  
  > **username_4**: It would be great if you could add more context to your question for those who want to help you out with your problem. What type of microarray is this? What are you trying to do with this dataset? What question are you trying to answer?  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_4 on 2019-02-06. Score: 2
  
  Processed microarray data is commonly represented as log\_2(expression). This data transformation is used because the data more closely fit a normal distribution in log space. With such a transformation, negative values represent very small amounts, rather than the absence of something.
  
  Larger values mean that more stuff is being produced (presumably transcripts), but its significance can only be assessed when combined with other data (e.g. through differential expression analysis).
  
  
  ---
  
  