<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>

CC BY-SA 3.0
2017-08-02T07:20:20.300Z
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?
benn
CC BY-SA 3.0
2017-08-02T09:02:37.627Z
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?
Kamil S Jaron
CC BY-SA 3.0
2017-08-02T11:48:35.800Z
@IanSudbery another poor soul like me I guess, definitely a duplicate need to up my search skills
sbradbio
CC BY-SA 4.0
2019-02-15T23:07:48.663Z
There is a discussion here about how to approach this analysis using limma: https://support.bioconductor.org/p/33783/
Jay Moore
<rna-seq><software-recommendation><r>
ngs time course experiment
# 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

