Body: <p>We have created a model to integrate several OMICs data, but we realized that the maximum TPM values of RNA-Seq data were so big that had unexpected effects on our results. We hypothesized that this might be due to the negative binomial distribution inherent to this OMIC's layer. </p>
  
  <p>To tackle this circumstance, we proposed to introduce as input the values obtained after using <a href="https://rdrr.io/bioc/limma/man/voom.html" rel="nofollow noreferrer">voom()</a> (<a href="http://www.statsci.org/smyth/pubs/VoomPreprint.pdf" rel="nofollow noreferrer">paper</a>) in our count data. The reason behind that decision was that voom would transform the input data to be more accurately introduced to a linear model. There are, though, several issues/arguments against doing so:</p>
  
  <ol>
  <li>The results obtained through voom() contain negative values, which do not make sense in our model. I tried to correct that by adding to all the results from a specific sample the absolute minimum value of that sample, but I am not confident whether that is a good way forward.</li>
  <li>Original 0 values (non-expressed) turn into different negative values across samples (-6 in Sample 1, -4 in Sample 2), creating a difference that did not exist to begin with. </li>
  <li>Some sources do not recommend to log-transform count data <a href="https://www.r-bloggers.com/do-not-log-transform-count-data-bitches/" rel="nofollow noreferrer">[1]</a>, including data from negative binomial distributions <a href="https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/j.2041-210X.2010.00021.x" rel="nofollow noreferrer">[2]</a>.</li>
  </ol>
  
  <p>I am not really versed into distribution transformations, so any comment would be appreciated.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-11-10T22:47:13.343Z
Comments.Text: 1. What sort of integration are you after? 2. Have you tried just using normalized counts?
Comments.UserDisplayName: Devon Ryan
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-11-11T10:33:32.823Z
Comments.Text: The integration is supposed to give a better picture of transcriptomics as a whole, which would finally lead to specify the final destiny of a transcript based on its post-transcriptional regulation (whether it will translate or not). Till now we tried using TPM values to account for the different sequencing depths, as we have not used it yet to compare across conditions, but its behaviour across time. (Sorry, I erroneously flagged your comment while trying to upvote it :P)
Comments.UserDisplayName: Hart Radev
Tags: <rna-seq><r><normalization><multi-omics>
Title: Should I use log2-CPM values (voom-limma) as input for my model?
ThreadText: # Should I use log2-CPM values (voom-limma) as input for my model?
  
  (Asked by: username_0 on 2019-11-10)
  
  We have created a model to integrate several OMICs data, but we realized that the maximum TPM values of RNA-Seq data were so big that had unexpected effects on our results. We hypothesized that this might be due to the negative binomial distribution inherent to this OMIC's layer.
  
  To tackle this circumstance, we proposed to introduce as input the values obtained after using [voom()](https://rdrr.io/bioc/limma/man/voom.html) ([paper](http://www.statsci.org/smyth/pubs/VoomPreprint.pdf)) in our count data. The reason behind that decision was that voom would transform the input data to be more accurately introduced to a linear model. There are, though, several issues/arguments against doing so:
  
  1. The results obtained through voom() contain negative values, which do not make sense in our model. I tried to correct that by adding to all the results from a specific sample the absolute minimum value of that sample, but I am not confident whether that is a good way forward.
  2. Original 0 values (non-expressed) turn into different negative values across samples (-6 in Sample 1, -4 in Sample 2), creating a difference that did not exist to begin with.
  3. Some sources do not recommend to log-transform count data [[1]](https://www.r-bloggers.com/do-not-log-transform-count-data-bitches/), including data from negative binomial distributions [[2]](https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/j.2041-210X.2010.00021.x).
  
  I am not really versed into distribution transformations, so any comment would be appreciated.
  
  **Tags:** rna-seq, r, normalization, multi-omics
  
  > **Comments:**
  > **username_1**: 1. What sort of integration are you after? 2. Have you tried just using normalized counts?  
  > **username_0**: The integration is supposed to give a better picture of transcriptomics as a whole, which would finally lead to specify the final destiny of a transcript based on its post-transcriptional regulation (whether it will translate or not). Till now we tried using TPM values to account for the different sequencing depths, as we have not used it yet to compare across conditions, but its behaviour across time. (Sorry, I erroneously flagged your comment while trying to upvote it :P)  
  
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  ## 0 answers
  
  