Answers.Body: <p>There is no direct support for this by best knowledge. Similar questions have been asked previously in the Bioconductor support forum, for example:</p>
  <p><a href="https://support.bioconductor.org/p/64300/#64793" rel="nofollow noreferrer">https://support.bioconductor.org/p/64300/#64793</a></p>
  <p>They discuss options around the TOST concept.</p>
  <p>For edgeR:</p>
  <p><a href="https://support.bioconductor.org/p/66283/#66287" rel="nofollow noreferrer">https://support.bioconductor.org/p/66283/#66287</a></p>
  <p>From these posts please go back and forth to find other posts in that forum to capture what the developers of limma/edgeR responded on the matter.</p>
  <p>Depending on your exact purpose it might even be enough to go for genes with large AveExpr and a nominal (not adj) p-value close to one.</p>
  
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Answers.CreationDate: 2023-03-14T18:18:25.930Z
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Body: <p>I love that Deseq2 has altHypothesis=&quot;lessAbs&quot; !!!!!!! I've used it a ton for RNA-seq. However, now I'm working with mass spect data using limma. Is there a way to make limma do the same thing?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-03-14T16:06:35.173Z
Comments.Text: Hi and welcome to the site! Could you please [edit] your question and clarify a bit? First, please make the title be a title: a short, concise description of what the question is about. Second, please give us a bit more context. I think limma is an R package, right? And what exactly would this option do? Identify _genes_ that don't change? Between what and what? Are you comparing two sets of mass-spec data? Please add all extra info to your question, don't answer in the comments, just add it to the main question.
Comments.UserDisplayName: terdon
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Comments.Text: Please clarify your specific problem or provide additional details to highlight exactly what you need. As it's currently written, it's hard to tell exactly what you're asking.
Comments.UserDisplayName: Community
Tags: <r><rna-seq><differential-expression><proteomics><limma>
Title: To find genes that don't change in RNA-seq, Deseq2 has altHypothesis="lessAbs". Is there a way to make limma do the same thing for proteomics?
ThreadText: # To find genes that don't change in RNA-seq, Deseq2 has altHypothesis="lessAbs". Is there a way to make limma do the same thing for proteomics?
  
  (Asked by: username_0 on 2023-03-14)
  
  I love that Deseq2 has altHypothesis="lessAbs" !!!!!!! I've used it a ton for RNA-seq. However, now I'm working with mass spect data using limma. Is there a way to make limma do the same thing?
  
  **Tags:** r, rna-seq, differential-expression, proteomics, limma
  
  > **Comments:**
  > **username_1**: Hi and welcome to the site! Could you please [edit] your question and clarify a bit? First, please make the title be a title: a short, concise description of what the question is about. Second, please give us a bit more context. I think limma is an R package, right? And what exactly would this option do? Identify _genes_ that don't change? Between what and what? Are you comparing two sets of mass-spec data? Please add all extra info to your question, don't answer in the comments, just add it to the main question.  
  > **username_2**: Please clarify your specific problem or provide additional details to highlight exactly what you need. As it's currently written, it's hard to tell exactly what you're asking.  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_3 on 2023-03-14. Score: 2
  
  There is no direct support for this by best knowledge. Similar questions have been asked previously in the Bioconductor support forum, for example:
  
  <https://support.bioconductor.org/p/64300/#64793>
  
  They discuss options around the TOST concept.
  
  For edgeR:
  
  <https://support.bioconductor.org/p/66283/#66287>
  
  From these posts please go back and forth to find other posts in that forum to capture what the developers of limma/edgeR responded on the matter.
  
  Depending on your exact purpose it might even be enough to go for genes with large AveExpr and a nominal (not adj) p-value close to one.
  
  
  ---
  
  