Answers.Body: <p>Having given some thought to this, I'd still use machine learning. I would attempt to augment the negative controls however. Is there a suitable published data set? R program is caret but it high barrier to entry without appropriate support.</p>
  <p>250 samples is just about enough. If you can augment that to 350 with negatives ... should be enough. What you do is dump all your data (&quot;features&quot;) into a single record, including the methylation data and use toxic element exposure as the training target. The output you want is called &quot;feature selection&quot;, what you are wanting is methylation sites to carry more &quot;weight&quot; than any other feature. Then you start other analytics to see what it's associating with. Its not trivial getting it working and a sceptic would complain about the lack of significance in the results, but its a good starting point.</p>
  <p>GWAS might work, just considering methylation vs toxic element exposure. SEM, I've never used it so I don't know.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2023-11-18T23:58:06.893Z
Answers.LastActivityDate: 2023-11-18T23:58:06.893Z
Answers.UserDisplayName: M__
Body: <p>I would be thankful to you if you can help with the statistical approach for case-control study to link DNA methylation (epic array) with toxic element exposure (arsenic) and health outcome (Cardiovascular diseases).</p>
  <p>I do have data from 125 Cardiovascular diseases and 125 non-Cardiovascular diseases individuals matched with age, sex, and smoking.I do have phenotypes files in a csv format having sex, age, smoking, disease, samples id, iAs (arsenic) values in log format and then a separate files for methylation data.</p>
  <hr />
  <p><strong>Notes</strong></p>
  <ol>
  <li>The sample size is small.</li>
  <li>Maybe structural equation modelling could be used to find CpG sites associated with arsenic exposure and disease condition as I have very small size samples</li>
  <li>R is a preferred language.</li>
  </ol>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-11-16T12:37:53.433Z
Comments.Text: This forum is not dedicated to general help requests and your post may be closed. Ask a precise bioinformatics question if you are encountering difficulties in your analysis, there is a lot of tutorials available on the internet.
Comments.UserDisplayName: Basti
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-11-16T14:10:35.930Z
Comments.Text: What computer language are you going to utilize in this project?  Are you planning on an ML/AI approach?  That's a major question to ask yourself.  If you are planning on a statistical approach, I highly recommend R and then Python.  If your skills are advanced, Id try an AI/ML approach!  What databases/sources did you use to get the phenotype files in .csv format?  What data types are the csv values in?  Are they integers, decimals/floats, strings?  Should you consider a .tsv files format instead?  What's the purpose to your research question to arsenic and DNA methylation?
Comments.UserDisplayName: Rachel Sweeney
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-11-16T15:04:21.347Z
Comments.Text: The question is part of a previous question here https://bioinformatics.stackexchange.com/questions/21823/mendelian-randomization . If they are taken together, plus comments it's answerable. I will make the appropriate edits shortly.
Comments.UserDisplayName: M__
Tags: <r><linear-regression><epigenetics><epidemiology>
Title: Statistical approach to link DNA methylation with toxic element exposure and health outcome
ThreadText: # Statistical approach to link DNA methylation with toxic element exposure and health outcome
  
  (Asked by: username_0 on 2023-11-16)
  
  I would be thankful to you if you can help with the statistical approach for case-control study to link DNA methylation (epic array) with toxic element exposure (arsenic) and health outcome (Cardiovascular diseases).
  
  I do have data from 125 Cardiovascular diseases and 125 non-Cardiovascular diseases individuals matched with age, sex, and smoking.I do have phenotypes files in a csv format having sex, age, smoking, disease, samples id, iAs (arsenic) values in log format and then a separate files for methylation data.
  
  ---
  
  **Notes**
  
  1. The sample size is small.
  2. Maybe structural equation modelling could be used to find CpG sites associated with arsenic exposure and disease condition as I have very small size samples
  3. R is a preferred language.
  
  **Tags:** r, linear-regression, epigenetics, epidemiology
  
  > **Comments:**
  > **username_1**: This forum is not dedicated to general help requests and your post may be closed. Ask a precise bioinformatics question if you are encountering difficulties in your analysis, there is a lot of tutorials available on the internet.  
  > **username_2**: What computer language are you going to utilize in this project?  Are you planning on an ML/AI approach?  That's a major question to ask yourself.  If you are planning on a statistical approach, I highly recommend R and then Python.  If your skills are advanced, Id try an AI/ML approach!  What databases/sources did you use to get the phenotype files in .csv format?  What data types are the csv values in?  Are they integers, decimals/floats, strings?  Should you consider a .tsv files format instead?  What's the purpose to your research question to arsenic and DNA methylation?  
  > **username_3**: The question is part of a previous question here https://bioinformatics.stackexchange.com/questions/21823/mendelian-randomization . If they are taken together, plus comments it's answerable. I will make the appropriate edits shortly.  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_3 on 2023-11-18. Score: 1
  
  Having given some thought to this, I'd still use machine learning. I would attempt to augment the negative controls however. Is there a suitable published data set? R program is caret but it high barrier to entry without appropriate support.
  
  250 samples is just about enough. If you can augment that to 350 with negatives ... should be enough. What you do is dump all your data ("features") into a single record, including the methylation data and use toxic element exposure as the training target. The output you want is called "feature selection", what you are wanting is methylation sites to carry more "weight" than any other feature. Then you start other analytics to see what it's associating with. Its not trivial getting it working and a sceptic would complain about the lack of significance in the results, but its a good starting point.
  
  GWAS might work, just considering methylation vs toxic element exposure. SEM, I've never used it so I don't know.
  
  
  ---
  
  