Answers.Body: <p>Since this is a microarray (U133A), what you're seeing is that each probe is associated with one or more transcripts. The general strategy when dealing with microarrays is to use RMA (or fRMA) to summarize to probesets and then <code>collapseRows</code> to summarize to gene-level information. Since this is all typically done in R, you can find some further discussion <a href="https://support.bioconductor.org/p/60214/" rel="noreferrer">on the bioconductor site</a>. I would suggest going through this process and then only using the result of that in building your network (whether you're happy with sticking to transcript-level quantifications or gene-level quantifications for building your network is up to you).</p>
  
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Answers.Text: I have a problem with using fRMA since the there is no supplementary `.cel` file in GEO database for this dataset. How can I use R on `.SOFT` files to summarize gene information?  Another point is that in one of the subsets of my data set, (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM73388), it seems that RMA have been used and data is collected in `.SOFT` file, but again there is duplicates in it.
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Answers.Text: If RMA has already been used then you needn't run it a second time.
Answers.UserDisplayName: Devon Ryan
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Answers.Text: Very useful answer! In EuropePMC I found 63 publications mentioning the use of collapseRows(). Many  don't report the setting they apply. Some high impact publications reported as follows: "MaxMean" (default) = Gandal (Science2018), Raznahan (PNAS2018), Langfelder (NatNeurosci2016); "maxRowVariance" = Langfelder (NatNeurosci2016)
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Body: <p>I have downloaded a gene expression data from GEO database (<a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE3268" rel="nofollow noreferrer">GSE3268</a>) in which in some of its rows there are duplicate gene symbols.</p>
  <p>For example <strong>TP53</strong> exists in two rows with different expression values and different <em>GenBank Accession</em> IDs.</p>
  <p>They have the following <em>Nucleotide Title</em>:</p>
  <blockquote>
  <p>Human p53 cellular tumor antigen mRNA, complete cds</p>
  <p>Homo sapiens tumor protein p53 (TP53), transcript variant 1, mRNA</p>
  </blockquote>
  <p>I want to map these data with edge list of gene symbols to make a network. Which expression value should I consider for such duplicate gene symbols for mapping?</p>
  
Tags: <database><gene>
Title: Duplicate gene symbol handling in GEO gene expression data
ThreadText: # Duplicate gene symbol handling in GEO gene expression data
  
  (Asked by: username_0 on 2017-12-14)
  
  I have downloaded a gene expression data from GEO database ([GSE3268](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE3268)) in which in some of its rows there are duplicate gene symbols.
  
  For example **TP53** exists in two rows with different expression values and different *GenBank Accession* IDs.
  
  They have the following *Nucleotide Title*:
  
  > Human p53 cellular tumor antigen mRNA, complete cds
  >
  > Homo sapiens tumor protein p53 (TP53), transcript variant 1, mRNA
  
  I want to map these data with edge list of gene symbols to make a network. Which expression value should I consider for such duplicate gene symbols for mapping?
  
  **Tags:** database, gene
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2017-12-14. Score: 5 | Accepted answer
  
  Since this is a microarray (U133A), what you're seeing is that each probe is associated with one or more transcripts. The general strategy when dealing with microarrays is to use RMA (or fRMA) to summarize to probesets and then `collapseRows` to summarize to gene-level information. Since this is all typically done in R, you can find some further discussion [on the bioconductor site](https://support.bioconductor.org/p/60214/). I would suggest going through this process and then only using the result of that in building your network (whether you're happy with sticking to transcript-level quantifications or gene-level quantifications for building your network is up to you).
  
  > **Comments:**
  > **username_0**: I have a problem with using fRMA since the there is no supplementary `.cel` file in GEO database for this dataset. How can I use R on `.SOFT` files to summarize gene information?  Another point is that in one of the subsets of my data set, (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM73388), it seems that RMA have been used and data is collected in `.SOFT` file, but again there is duplicates in it.  
  > **username_1**: If RMA has already been used then you needn't run it a second time.  
  > **username_2**: Very useful answer! In EuropePMC I found 63 publications mentioning the use of collapseRows(). Many  don't report the setting they apply. Some high impact publications reported as follows: "MaxMean" (default) = Gandal (Science2018), Raznahan (PNAS2018), Langfelder (NatNeurosci2016); "maxRowVariance" = Langfelder (NatNeurosci2016)  
  
  ---
  
  