<p>In summary you basically have noisy data and to simply answer your question it depends on the genetic diversity of the samples you are dealing with. Note, from my recollection this was confusing to start with because the <em>"replicates"</em> column was labeled as <em>"treatment"</em>, but I could be wrong.  </p>

<hr>

<p>Revised answer, firstly there is zero stationarity - thus its a straight time-series.</p>

<p><strong>Summary</strong> As a time series </p>

<ul>
<li>The statistical noise between A and D is lower than the difference in phenotypic expression between each and all othe replicates in the analysis ABCD - FOR DAY 0 VERSUS DAY 
4, so they cluster and are distinct. </li>
<li>The statistical noise between B and C is greater than the phenotypic variation between their expression profiles for DAY 0 to DAY 4. I suspect they are genetically very similar. Thats the conclusion, you can spice it up with different analysis, but I'd leave it because might find a lack of statistical significance in your core observations (A and D).</li>
<li>Within the time-points the data is very noisy and doesn't appear to show a distinct signal. Thats okay, this is essentially a time series.</li>
<li>Sample D is distinct in all scenarios (which is good)</li>
</ul>

<p><strong>Result</strong> To reiterate and expand a bit, the result you have is that sample A and D biological replicates are consisent within the analysis you are performing, i.e. day 0 verses day 4 (below). Thus they cluster together and this is proof of distinct phenotypes above the statistical noise (A with A and D with D). An intra-time point dendrogram would have a different result. The replicates samples B and C are not consistent and the interpretation is that the phenotypic expression profiles are so similar they are essentially identical within the boundaries of statistical noise. </p>

<p><strong>Analysis</strong> The dendrogram is combines day 0 to day 4 analysis with day 0 to day 0 analysis and day 4 to day 4 analysis. HOWEVER, the clustering algorithm will correct for this and separate based on the largest distance (day 0 versus day 4). The signal that you get in your dendrogram is the largest signal in the data set which is day 0 vs day 4, and this is how UPGMA clustering works (I probably have previous posts on the statistical algorithm of UPGMA). So actually that is fine.</p>

<p><strong>Advice</strong> With data this noisy I'd go no further in attempting to deploy critical probablity (a <em>p</em> value)  because this data could be the result of a complex distribution. This would require a statistician. Personally 
 I'd upload the PCA as a separate post, but its just my opinion, because UPGMA versus eigenvalue based PCA are statistically quite different approaches. </p>

CC BY-SA 4.0
2020-05-11T14:07:21.533Z
Thank you. X-axis is just the same as the y (flipped). This is a all-versus-all pairwise comparison.
MrMagistaPolo
CC BY-SA 4.0
2020-05-11T15:50:07.463Z
Hang on, none of the conditions are replications. Each experiment is different?
M__
CC BY-SA 4.0
2020-05-11T16:27:57.097Z
It doesn't look bad, the clustering does not seem to be driven by batch (`replicate`). `time` has a strong effect and it is also clear that `D` has rather distinct biology than others. Do you also have a PCA or an MDS plot? Such a plot where condition is represented as colors and time as shapes for example can help getting the full picture? And if your design matrix enables, you can add all of these variables in your formula to control for confounders.
haci
CC BY-SA 4.0
2020-05-11T20:45:23.480Z
I think we need to know biologically what 4hours representsds, confirm this is not a time series and we could could expect 'stationarity'
M__
CC BY-SA 4.0
2020-05-13T06:49:33.640Z
Yes I have a PCA as well, I will upload later today. PCA shows the same effect, very strong separation by time. 
But I am not so interested in this time effect, rather I would compare A vs B for 0h or for 4h. 
For deseq I used the simple all versus all option (galaxy), so galaxy does is only aware of a condition that is the concatenation of condition+time.
Paul
CC BY-SA 4.0
2020-05-13T11:39:07.317Z
Okay I get it, I've updated.
M__
CC BY-SA 4.0
2020-05-11T13:49:42.600Z
2020-05-13T14:53:22.023Z
2020-05-13T14:53:22.023Z
M__
<p>The attached figure (from deseq2) shows the sample to sample distances of a RNAseq experiment with 4 conditions (A,B,C,D) at 2 time points (0h,4h) with 2 biological replicates.</p>

<p>I am a little bit puzzled that the biological replicates are not always the most similar pairs. In a perfect world biological replicates should be identical but I assumed that at least they should be very similar.</p>

<p>Is this something I have to worry about, or is this more or less normal? </p>

<p><a href="https://i.sstatic.net/rPOxY.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/rPOxY.png" alt="deseq2 sample2sample distance matrix"></a></p>

CC BY-SA 4.0
2020-05-13T18:04:18.753Z
This depends on the magnitude of biological variation and technical variation in your data.
Phoenix Mu
<rna-seq><deseq2>
Should biological replicates be the most similar pairs in an RNAseq experiment?
# Should biological replicates be the most similar pairs in an RNAseq experiment?

(Asked by: username_0 on 2020-05-11)

The attached figure (from deseq2) shows the sample to sample distances of a RNAseq experiment with 4 conditions (A,B,C,D) at 2 time points (0h,4h) with 2 biological replicates.

I am a little bit puzzled that the biological replicates are not always the most similar pairs. In a perfect world biological replicates should be identical but I assumed that at least they should be very similar.

Is this something I have to worry about, or is this more or less normal?

[![deseq2 sample2sample distance matrix](https://i.sstatic.net/rPOxY.png)](https://i.sstatic.net/rPOxY.png)

**Tags:** rna-seq, deseq2
> **Comments:**
> **username_1**: This depends on the magnitude of biological variation and technical variation in your data.  

---

## 1 answers

### Answer by username_2 on 2020-05-11. Score: 1

In summary you basically have noisy data and to simply answer your question it depends on the genetic diversity of the samples you are dealing with. Note, from my recollection this was confusing to start with because the *"replicates"* column was labeled as *"treatment"*, but I could be wrong.

---

Revised answer, firstly there is zero stationarity - thus its a straight time-series.

**Summary** As a time series

* The statistical noise between A and D is lower than the difference in phenotypic expression between each and all othe replicates in the analysis ABCD - FOR DAY 0 VERSUS DAY
  4, so they cluster and are distinct.
* The statistical noise between B and C is greater than the phenotypic variation between their expression profiles for DAY 0 to DAY 4. I suspect they are genetically very similar. Thats the conclusion, you can spice it up with different analysis, but I'd leave it because might find a lack of statistical significance in your core observations (A and D).
* Within the time-points the data is very noisy and doesn't appear to show a distinct signal. Thats okay, this is essentially a time series.
* Sample D is distinct in all scenarios (which is good)

**Result** To reiterate and expand a bit, the result you have is that sample A and D biological replicates are consisent within the analysis you are performing, i.e. day 0 verses day 4 (below). Thus they cluster together and this is proof of distinct phenotypes above the statistical noise (A with A and D with D). An intra-time point dendrogram would have a different result. The replicates samples B and C are not consistent and the interpretation is that the phenotypic expression profiles are so similar they are essentially identical within the boundaries of statistical noise.

**Analysis** The dendrogram is combines day 0 to day 4 analysis with day 0 to day 0 analysis and day 4 to day 4 analysis. HOWEVER, the clustering algorithm will correct for this and separate based on the largest distance (day 0 versus day 4). The signal that you get in your dendrogram is the largest signal in the data set which is day 0 vs day 4, and this is how UPGMA clustering works (I probably have previous posts on the statistical algorithm of UPGMA). So actually that is fine.

**Advice** With data this noisy I'd go no further in attempting to deploy critical probablity (a *p* value) because this data could be the result of a complex distribution. This would require a statistician. Personally
I'd upload the PCA as a separate post, but its just my opinion, because UPGMA versus eigenvalue based PCA are statistically quite different approaches.

> **Comments:**
> **username_0**: Thank you. X-axis is just the same as the y (flipped). This is a all-versus-all pairwise comparison.  
> **username_2**: Hang on, none of the conditions are replications. Each experiment is different?  
> **username_3**: It doesn't look bad, the clustering does not seem to be driven by batch (`replicate`). `time` has a strong effect and it is also clear that `D` has rather distinct biology than others. Do you also have a PCA or an MDS plot? Such a plot where condition is represented as colors and time as shapes for example can help getting the full picture? And if your design matrix enables, you can add all of these variables in your formula to control for confounders.  
> **username_2**: I think we need to know biologically what 4hours representsds, confirm this is not a time series and we could could expect 'stationarity'  
> **username_4**: Yes I have a PCA as well, I will upload later today. PCA shows the same effect, very strong separation by time.  But I am not so interested in this time effect, rather I would compare A vs B for 0h or for 4h.  For deseq I used the simple all versus all option (galaxy), so galaxy does is only aware of a condition that is the concatenation of condition+time.  
> **username_2**: Okay I get it, I've updated.  

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