Answers.Body: <p>Limma is relatively generic in what it accepts. Typically it is either data on log2 scale that are either already normalized or need some of the inbuilt normalization methods (such as quantile normalization), or in case of RNA-seq you can provide raw counts which then can run through the limma-voom pipeline.</p>
  <p>The format is always a matrix of counts/values, with genes/observations in rows and samples/specimen in columns.</p>
  <p>For practice, you could follow the RNA-seq/limma-voom section (chapter 15) of the <a href="http://www.bioconductor.org/packages/current/bioc/vignettes/limma/inst/doc/usersguide.pdf" rel="nofollow noreferrer">limma manual</a> which has a RNA-seq case study available at <a href="https://subread.sourceforge.net/RNAseqCaseStudy.html" rel="nofollow noreferrer">https://subread.sourceforge.net/RNAseqCaseStudy.html</a> with example data and code.</p>
  <p>GEO has a lot of published data, often microarray gene expression values or RNA-seq, but lets discuss this in a separate question if you need it to keep this here focused on limma.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2023-07-20T08:40:34.917Z
Answers.Text: Thank you very much for your help, I managed to find some data on GEO that I could practice on and its working with the code that I knew worked already since I had used it in a code along tutorial
Answers.UserDisplayName: Sabrina
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2023-07-19T07:30:58.317Z
Answers.LastActivityDate: 2023-07-19T08:01:10.443Z
Answers.LastEditDate: 2023-07-19T08:01:10.443Z
Answers.UserDisplayName: ATpoint
Body: <p>I am very new to bioinformatics but have some background in coding with R. I have been asked to find a data set and complete differential expression using the Limma package in R.
  I have tried a few data sets but haven't been able to get any code to work.</p>
  <p>What kind of data do I need to have in order for Differential Expression to work with Limma R? I have tried load and read.table, is there a read function that I am missing?</p>
  <p>I was advised to look on Gene Expression Omnibus to find published data sets to work on but again I am struggling to find any that will work.</p>
  <p>Thank you</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-07-18T15:19:00.193Z
Comments.Text: Thanks Sabrina, there is a recent Limma question which might help. Please could you supply the code you have tried and resulting error? I don't use Limma but it will almost certainly have a test data set. For example "16 Two-Color Case Studies" in the manual http://www.bioconductor.org/packages/devel/bioc/vignettes/limma/inst/doc/usersguide.pdf
Comments.UserDisplayName: M__
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-07-18T15:38:10.590Z
Comments.Text: Thank you so much for your help. I am pretty sure its the kind of data I am trying to input, I tried to use exprs() for my data and even that pulls up a data type error
Comments.UserDisplayName: Sabrina
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-07-18T15:55:55.483Z
Comments.Text: Showing your code will help. There isn't many here who use Limma, but let's see. Also keep in mind not to confuse Limma with Limma-voom.
Comments.UserDisplayName: M__
Tags: <r><differential-expression>
Title: What kind of data do I need to be able to complete Differential Expression using Limma
ThreadText: # What kind of data do I need to be able to complete Differential Expression using Limma
  
  (Asked by: username_0 on 2023-07-18)
  
  I am very new to bioinformatics but have some background in coding with R. I have been asked to find a data set and complete differential expression using the Limma package in R.
  I have tried a few data sets but haven't been able to get any code to work.
  
  What kind of data do I need to have in order for Differential Expression to work with Limma R? I have tried load and read.table, is there a read function that I am missing?
  
  I was advised to look on Gene Expression Omnibus to find published data sets to work on but again I am struggling to find any that will work.
  
  Thank you
  
  **Tags:** r, differential-expression
  
  > **Comments:**
  > **username_1**: Thanks username_0, there is a recent Limma question which might help. Please could you supply the code you have tried and resulting error? I don't use Limma but it will almost certainly have a test data set. For example "16 Two-Color Case Studies" in the manual http://www.bioconductor.org/packages/devel/bioc/vignettes/limma/inst/doc/usersguide.pdf  
  > **username_0**: Thank you so much for your help. I am pretty sure its the kind of data I am trying to input, I tried to use exprs() for my data and even that pulls up a data type error  
  > **username_1**: Showing your code will help. There isn't many here who use Limma, but let's see. Also keep in mind not to confuse Limma with Limma-voom.  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_2 on 2023-07-19. Score: 0 | Accepted answer
  
  Limma is relatively generic in what it accepts. Typically it is either data on log2 scale that are either already normalized or need some of the inbuilt normalization methods (such as quantile normalization), or in case of RNA-seq you can provide raw counts which then can run through the limma-voom pipeline.
  
  The format is always a matrix of counts/values, with genes/observations in rows and samples/specimen in columns.
  
  For practice, you could follow the RNA-seq/limma-voom section (chapter 15) of the [limma manual](http://www.bioconductor.org/packages/current/bioc/vignettes/limma/inst/doc/usersguide.pdf) which has a RNA-seq case study available at <https://subread.sourceforge.net/RNAseqCaseStudy.html> with example data and code.
  
  GEO has a lot of published data, often microarray gene expression values or RNA-seq, but lets discuss this in a separate question if you need it to keep this here focused on limma.
  
  > **Comments:**
  > **username_0**: Thank you very much for your help, I managed to find some data on GEO that I could practice on and its working with the code that I knew worked already since I had used it in a code along tutorial  
  
  ---
  
  