Answers.Body: <blockquote>
    <p>If I'm interested to get normalized gene counts, can I go ahead and
    multiple each individual's norm.factor into its gene counts? For
    example, the expected count for IND1 for Gene1 is 100 and it's
    norm.factor is 0.80, can I say that the normalized gene count is
    100*0.80=80?</p>
  </blockquote>
  
  <p>No; you divide, but if you poke around, you can probably find a way to get edgeR to return the normalized counts.</p>
  
  <blockquote>
    <p>I think since normalization uses per person library size, each
    person's normalization is independent of the others.</p>
  </blockquote>
  
  <p>Not true for the default normalization used in edgeR, TMM.  CPM normalization is independently determined for each sample.</p>
  
  <p>In general, normalize everything together, unless you think that the algorithm assumptions might be violated by including all of them together.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-09-06T20:52:00.860Z
Answers.Text: I was thinking to get the quantile normalization as on the question link:  https://bioinformatics.stackexchange.com/questions/2586/how-to-apply-upperquartile-normalization-on-rsem-expected-counts    
  
  Thank you for your kind reply.  :)
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Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-09-06T21:07:03.463Z
Answers.Text: I got what I was looking for  thank you @swbarnes  :) `poke around thing helped` :D
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Answers.UserDisplayName: swbarnes2
Body: <p>I've RNA seq, Human, Paired-end data, Sample size is &lt;40. These are aligned using STAR, RSEM processed. With RSEM I've TPM and expected counts, that is two files columns as individual IDs and row as gene names.    </p>
  
  <p>I'm interested to normalize gene data. With edgeR tutorial (link in the end) and few other online resources I see that after following steps there's an R object that contains <code>norm.factors</code> (Page 15) value for each individual.   </p>
  
  <p>I'm unable to wrap my head around it, now. If I'm interested to get normalized gene counts, can I go ahead and multiple each individual's norm.factor into its gene counts? For example, the expected count for <code>IND1</code> for <code>Gene1</code> is 100 and it's <code>norm.factor</code> is <code>0.80</code>, can I say that the normalized gene count is <code>100*0.80=80</code>?  </p>
  
  <p>I'm not interested to perform voomm, or differential expression analysis.     </p>
  
  <p>Follow up question: If I'm to sample few individuals' data from these RNA-seq should I<br>
  a) normalize all together and extract interested individuals, or,<br>
  b) extract interested individuals and then perform normalization.<br>
  I think since normalization uses per person library size, each person's normalization is independent of the others.  Can someone please correct me if I'm wrong?  </p>
  
  <p><a href="https://www.bioconductor.org/packages/release/bioc/vignettes/edgeR/inst/doc/edgeRUsersGuide.pdf" rel="nofollow noreferrer">https://www.bioconductor.org/packages/release/bioc/vignettes/edgeR/inst/doc/edgeRUsersGuide.pdf</a></p>
  
Tags: <rna-seq><normalization><edger>
Title: RNA-seq: How to get new expression count after normalization
ThreadText: # RNA-seq: How to get new expression count after normalization
  
  (Asked by: username_0 on 2019-09-06)
  
  I've RNA seq, Human, Paired-end data, Sample size is <40. These are aligned using STAR, RSEM processed. With RSEM I've TPM and expected counts, that is two files columns as individual IDs and row as gene names.
  
  I'm interested to normalize gene data. With edgeR tutorial (link in the end) and few other online resources I see that after following steps there's an R object that contains `norm.factors` (Page 15) value for each individual.
  
  I'm unable to wrap my head around it, now. If I'm interested to get normalized gene counts, can I go ahead and multiple each individual's norm.factor into its gene counts? For example, the expected count for `IND1` for `Gene1` is 100 and it's `norm.factor` is `0.80`, can I say that the normalized gene count is `100*0.80=80`?
  
  I'm not interested to perform voomm, or differential expression analysis.
  
  Follow up question: If I'm to sample few individuals' data from these RNA-seq should I  
  a) normalize all together and extract interested individuals, or,  
  b) extract interested individuals and then perform normalization.  
  I think since normalization uses per person library size, each person's normalization is independent of the others. Can someone please correct me if I'm wrong?
  
  <https://www.bioconductor.org/packages/release/bioc/vignettes/edgeR/inst/doc/edgeRUsersGuide.pdf>
  
  **Tags:** rna-seq, normalization, edger
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2019-09-06. Score: 2 | Accepted answer
  
  > If I'm interested to get normalized gene counts, can I go ahead and
  > multiple each individual's norm.factor into its gene counts? For
  > example, the expected count for IND1 for Gene1 is 100 and it's
  > norm.factor is 0.80, can I say that the normalized gene count is
  > 100\*0.80=80?
  
  No; you divide, but if you poke around, you can probably find a way to get edgeR to return the normalized counts.
  
  > I think since normalization uses per person library size, each
  > person's normalization is independent of the others.
  
  Not true for the default normalization used in edgeR, TMM. CPM normalization is independently determined for each sample.
  
  In general, normalize everything together, unless you think that the algorithm assumptions might be violated by including all of them together.
  
  > **Comments:**
  > **username_0**: I was thinking to get the quantile normalization as on the question link:  https://bioinformatics.stackexchange.com/questions/2586/how-to-apply-upperquartile-normalization-on-rsem-expected-counts      Thank you for your kind reply.  :)  
  > **username_0**: I got what I was looking for  thank you @swbarnes  :) `poke around thing helped` :D  
  
  ---
  
  