Body: <p>I am completely new to meta-analysis and am a bit lost on how to proceed.</p>
  <p>I have two biomarker datasets; each one is using a different microarray platform. I have the protein names, the concentration in each subject (one dataset has 15 subjects while another has 22) as well as fold change, p-value, and AUC values.</p>
  <p>Is there a simple way to do a meta-analysis with this information? For example, is there any way to join the datasets and calculate a combined fold change and AUC? I was thinking of using sumlog() from the metap package in R to do a combined p-value, but I am lost on how to combine the other two values, or if this would even be appropriate. I've attached a screenshot of one of the datasets</p>
  <p><a href="https://i.sstatic.net/3JfSP.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/3JfSP.png" alt="dataset preview" /></a></p>
  <p>Any help or guidance would be greatly appreciated!</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-11-03T12:19:19.960Z
Comments.Text: You seem new to analyze microarrays. Look at the wonderful limma vignette about how to analyze one dataset. Then I suggest you to analyze both  datasets and see how much do they agree. However what you need to do depends on what you want to do (besides a meta-analysis). What is your biological question (or reason of this analysis)?
Comments.UserDisplayName: llrs
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-11-05T14:46:22.530Z
Comments.Text: Thats a lot of questions rolled into one. First consider data mining to get an overview
Comments.UserDisplayName: M__
Tags: <r><microarray><meta-analysis>
Title: Meta-analysis in R
ThreadText: # Meta-analysis in R
  
  (Asked by: username_0 on 2020-10-31)
  
  I am completely new to meta-analysis and am a bit lost on how to proceed.
  
  I have two biomarker datasets; each one is using a different microarray platform. I have the protein names, the concentration in each subject (one dataset has 15 subjects while another has 22) as well as fold change, p-value, and AUC values.
  
  Is there a simple way to do a meta-analysis with this information? For example, is there any way to join the datasets and calculate a combined fold change and AUC? I was thinking of using sumlog() from the metap package in R to do a combined p-value, but I am lost on how to combine the other two values, or if this would even be appropriate. I've attached a screenshot of one of the datasets
  
  [![dataset preview](https://i.sstatic.net/3JfSP.png)](https://i.sstatic.net/3JfSP.png)
  
  Any help or guidance would be greatly appreciated!
  
  **Tags:** r, microarray, meta-analysis
  
  > **Comments:**
  > **username_1**: You seem new to analyze microarrays. Look at the wonderful limma vignette about how to analyze one dataset. Then I suggest you to analyze both  datasets and see how much do they agree. However what you need to do depends on what you want to do (besides a meta-analysis). What is your biological question (or reason of this analysis)?  
  > **username_2**: Thats a lot of questions rolled into one. First consider data mining to get an overview  
  
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  ## 0 answers
  
  