<p>This data is genuinely paired data and you have stated values are a mean. Thus the raw data can be analysed in your previous questions on the same theme described here:</p>
<p><a href="https://bioinformatics.stackexchange.com/questions/18660/what-is-the-best-way-to-compare-treated-control-samples">What is the best way to compare treated/control samples?</a></p>
<p>And here:</p>
<p><a href="https://bioinformatics.stackexchange.com/questions/18732/what-is-the-best-way-to-compare-differences-in-deg-expression-between-two-popula">What is the best way to compare differences in DEG expression between two populations?</a></p>
<p>If this is 'ANOVAerable' (its not a word, but if ANOVA is appropriate) than a two-way ANOVA would provide all the answers (however see below regarding the trend in the data). This analysis will perform a 'A' versus 'B' and each 'gen' compared to the other, as well as the interaction between A/B and 'gen'. You would need the raw data, you can't do anything with means (unless you are assuming the distribution). The distribution within each 'gen' is important (if its normal - conforms to the normal distribution, you're in business).</p>
<p>There is nothing further to add and I do feel we need to draw this series of questions to a close.</p>
<p>A regression model could be explored because there is a clear trend in both data sets, but I'm uncertain it will be significant. Within ANOVA however this might trap the outlier, but that is more complicated (you standardise against the trend thus 'gen1' is comparable to 'gen5' and therein 'gen4' can be identified as an outlier). This was touched on in the answer to the previous question (ols and python)</p>

CC BY-SA 4.0
2022-03-14T01:54:08.297Z
2022-03-14T03:18:42.937Z
2022-03-14T03:18:42.937Z
M__
<p>I need a statistically significant &quot;decision&quot; that DEGs from group A are different (or not) from group B (after drug treatment).
For that, I have a mean fold change delta, calculated as the mean of 5 different treated samples minus the mean of the same, but untreated samples.</p>
<pre><code>        A.delta    B.delta
gen1    4.9182385  4.5472662
gen2    3.6354761  3.4474844
gen3    3.7592630  3.2838886
gen4    3.9623264  2.8189166
gen5    3.4742171  3.1231050
</code></pre>
<p>In my vision, there is not much difference in fold change delta between A and B for the <code>gen1</code> for example (4.91 is not very far from 4.54). Same for <code>gen2</code>. While for <code>gen4</code> there might be something interesting.
What kind of plot may be the most useful for that kind of comparison?</p>

CC BY-SA 4.0
2022-03-14T01:47:03.780Z
I do feel we've been here twice before with this question https://bioinformatics.stackexchange.com/questions/18732/what-is-the-best-way-to-compare-differences-in-deg-expression-between-two-popula and this one https://bioinformatics.stackexchange.com/questions/18660/what-is-the-best-way-to-compare-treated-control-samples both appear very similar questions concerning mouse models. Originally labelled RED and GRN
M__
CC BY-SA 4.0
2022-03-14T09:58:52.760Z
If the answers you got before not satisfy you, you can always leave there a comment, or edit your question and clearly indicate what about the problem is that you can't use the proposed solutions.
Kamil S Jaron
<differential-expression>
How to compare differential expression of same genes between two groups?
# How to compare differential expression of same genes between two groups?

(Asked by: username_0 on 2022-03-13)

I need a statistically significant "decision" that DEGs from group A are different (or not) from group B (after drug treatment).
For that, I have a mean fold change delta, calculated as the mean of 5 different treated samples minus the mean of the same, but untreated samples.
        A.delta    B.delta
gen1    4.9182385  4.5472662
gen2    3.6354761  3.4474844
gen3    3.7592630  3.2838886
gen4    3.9623264  2.8189166
gen5    3.4742171  3.1231050

In my vision, there is not much difference in fold change delta between A and B for the gen1 for example (4.91 is not very far from 4.54). Same for gen2. While for gen4 there might be something interesting.
What kind of plot may be the most useful for that kind of comparison?

**Tags:** differential-expression
> **Comments:**
> **username_1**: I do feel we've been here twice before with this question https://bioinformatics.stackexchange.com/questions/18732/what-is-the-best-way-to-compare-differences-in-deg-expression-between-two-popula and this one https://bioinformatics.stackexchange.com/questions/18660/what-is-the-best-way-to-compare-treated-control-samples both appear very similar questions concerning mouse models. Originally labelled RED and GRN  
> **username_2**: If the answers you got before not satisfy you, you can always leave there a comment, or edit your question and clearly indicate what about the problem is that you can't use the proposed solutions.  

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## 1 answers

### Answer by username_1 on 2022-03-14. Score: 0 | Accepted answer

This data is genuinely paired data and you have stated values are a mean. Thus the raw data can be analysed in your previous questions on the same theme described here:

[What is the best way to compare treated/control samples?](https://bioinformatics.stackexchange.com/questions/18660/what-is-the-best-way-to-compare-treated-control-samples)

And here:

[What is the best way to compare differences in DEG expression between two populations?](https://bioinformatics.stackexchange.com/questions/18732/what-is-the-best-way-to-compare-differences-in-deg-expression-between-two-popula)

If this is 'ANOVAerable' (its not a word, but if ANOVA is appropriate) than a two-way ANOVA would provide all the answers (however see below regarding the trend in the data). This analysis will perform a 'A' versus 'B' and each 'gen' compared to the other, as well as the interaction between A/B and 'gen'. You would need the raw data, you can't do anything with means (unless you are assuming the distribution). The distribution within each 'gen' is important (if its normal - conforms to the normal distribution, you're in business).

There is nothing further to add and I do feel we need to draw this series of questions to a close.

A regression model could be explored because there is a clear trend in both data sets, but I'm uncertain it will be significant. Within ANOVA however this might trap the outlier, but that is more complicated (you standardise against the trend thus 'gen1' is comparable to 'gen5' and therein 'gen4' can be identified as an outlier). This was touched on in the answer to the previous question (ols and python)


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