Body: <p>I have a question regarding the RNA-Seq analysis.</p>
  
  <p>We have a time course data regarding a mouse model wt and mutant treated with a drug (10uM) and the taken down at different time points: 8 time points and did RNA-seq on these samples currently we have tpm values and raw gene counts from rna seq. Also we have only one sample for each time point. we would like to see for changes in genes across different time point but not sure statistical method should we use to determine this, should we consider for example a single time point wt vs mt, or see trends across wt and mt individually 
  I know that there are some bioc packages for NGS analysis (edgeR,
  DESeq,etc). Is any package or method good for one-sample-per-time-point
  data?</p>
  
Comments.ContentLicense: CC BY-SA 3.0
Comments.CreationDate: 2017-08-02T07:20:20.300Z
Comments.Text: For "one-sample-per-time-point data" there isn't any good statistical test available. There are tricks (such as described in edgeR manual page 21), but there is no sound statistical ways to analyze these poor designs. I wonder why you choose such a poor design? And not ask a statistician or bioinformatician first before you do the (expensive) NGS?
Comments.UserDisplayName: benn
Comments.ContentLicense: CC BY-SA 3.0
Comments.CreationDate: 2017-08-02T09:02:37.627Z
Comments.Text: I would recommend to do at least MDA plots to see if there is visible condition / time signal. However, I agree that 1 replicate is very bad idea. Is there chance to get more replicates?
Comments.UserDisplayName: Kamil S Jaron
Comments.ContentLicense: CC BY-SA 3.0
Comments.CreationDate: 2017-08-02T11:48:35.800Z
Comments.Text: @IanSudbery another poor soul like me I guess, definitely a duplicate need to up my search skills
Comments.UserDisplayName: sbradbio
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-02-15T23:07:48.663Z
Comments.Text: There is a discussion here about how to approach this analysis using limma: https://support.bioconductor.org/p/33783/
Comments.UserDisplayName: Jay Moore
Tags: <rna-seq><software-recommendation><r>
Title: ngs time course experiment
ThreadText: # ngs time course experiment
  
  (Asked by: username_0 on 2017-08-02)
  
  I have a question regarding the RNA-Seq analysis.
  
  We have a time course data regarding a mouse model wt and mutant treated with a drug (10uM) and the taken down at different time points: 8 time points and did RNA-seq on these samples currently we have tpm values and raw gene counts from rna seq. Also we have only one sample for each time point. we would like to see for changes in genes across different time point but not sure statistical method should we use to determine this, should we consider for example a single time point wt vs mt, or see trends across wt and mt individually
  I know that there are some bioc packages for NGS analysis (edgeR,
  DESeq,etc). Is any package or method good for one-sample-per-time-point
  data?
  
  **Tags:** rna-seq, software-recommendation, r
  
  > **Comments:**
  > **username_1**: For "one-sample-per-time-point data" there isn't any good statistical test available. There are tricks (such as described in edgeR manual page 21), but there is no sound statistical ways to analyze these poor designs. I wonder why you choose such a poor design? And not ask a statistician or bioinformatician first before you do the (expensive) NGS?  
  > **username_2**: I would recommend to do at least MDA plots to see if there is visible condition / time signal. However, I agree that 1 replicate is very bad idea. Is there chance to get more replicates?  
  > **username_0**: @IanSudbery another poor soul like me I guess, definitely a duplicate need to up my search skills  
  > **username_3**: There is a discussion here about how to approach this analysis using limma: https://support.bioconductor.org/p/33783/  
  
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  ## 0 answers
  
  