<p>I don't really understand the table, particularly the 'p-value' in the header. More generally I don't really understand the experiment nor the data.</p>
<p>As a general rule the Wilcoxon would be a reasonable guess of the test using raw data. This is non-parametric version of the T-test without the rigid assumptions of parametrics. Control mice go in one column and subjects in the other. Scipy.stats has the test, its certainly in R (which I don't really use).</p>
<pre><code>print (scipy.stats.wilcoxon(x = [1,2,3,4,5], y=[1,3,5,6,7], zero_method='wilcox', correction=False, alternative='two-sided', mode='auto', *, axis=0, nan_policy='propagate'))
</code></pre>
<p>There you go, filled out with some example data.</p>

CC BY-SA 4.0
2022-02-24T20:37:09.033Z
2022-02-24T20:45:37.867Z
2022-02-24T20:45:37.867Z
M__
<p>I have two populations of mice, say RED and GREEN; there are 5 pairs treatment/control in each.</p>
<p>The table is the following:</p>
<pre><code>gene_name  p-value(treatVSctrl) RED1_ctrl RED1_treat RED2_ctrl RED2_treat ... GRN1_ctrl GRN1_treat etc... 
gene1      1.3                  4         10         5         10             6.2       9.75
gene2      2.8                  3         6          6         12             3         6  
gene3      4.4                  5         12         4         8              7         10
</code></pre>
<p>I need to compare two groups to have an idea of the expression of different genes after treatment.
I also need to find a difference between RED and GREEN group in terms of genes expression</p>
<p>The thing is I cannot compare mice one by one just measuring the fold change, I need to consider p-value. What is the most correct way to perform this analysis?</p>

CC BY-SA 4.0
2022-02-24T20:08:53.950Z
Its an easy comparison but I was confused by the header p-values. I can't help there needs to be raw quantification. I am not aware of a stat that can take an unknown 'p-value' and compare to other p-values at a given level of significance
M__
CC BY-SA 4.0
2022-02-24T20:13:39.903Z
Thank you. Do you mean there must be a p-value for each pair?
Mark
CC BY-SA 4.0
2022-02-24T20:25:40.070Z
No the header in the first column says " p-value(treatVSctrl..." . I don't understand the quantification, but the column header is stating this is a p-value.
M__
CC BY-SA 4.0
2022-02-24T20:50:22.983Z
Agree the "consider the p-value" instruction is confusing. The first column clearly doesn't represent p-values, as p-value are probabilities and must be in the range [0, 1].
Nuclear Hoagie
CC BY-SA 4.0
2022-02-25T15:14:18.963Z
@NuclearHoagie it could represent a percentage, i.e. (0 to 1.0) * 100 , e.g. this could be a pairwise T-test. Alot of values are hovering around the 5% mark. Where there are mixed results it would be a reason for the OP to ask how to move forward.
M__
<differential-expression><p-values>
What is the best way to compare treated/control samples?
# What is the best way to compare treated/control samples?

(Asked by: username_0 on 2022-02-24)

I have two populations of mice, say RED and GREEN; there are 5 pairs treatment/control in each.
The table is the following:
gene_name  p-value(treatVSctrl) RED1_ctrl RED1_treat RED2_ctrl RED2_treat ... GRN1_ctrl GRN1_treat etc... 
gene1      1.3                  4         10         5         10             6.2       9.75
gene2      2.8                  3         6          6         12             3         6  
gene3      4.4                  5         12         4         8              7         10

I need to compare two groups to have an idea of the expression of different genes after treatment.
I also need to find a difference between RED and GREEN group in terms of genes expression
The thing is I cannot compare mice one by one just measuring the fold change, I need to consider p-value. What is the most correct way to perform this analysis?

**Tags:** differential-expression, p-values
> **Comments:**
> **username_1**: Its an easy comparison but I was confused by the header p-values. I can't help there needs to be raw quantification. I am not aware of a stat that can take an unknown 'p-value' and compare to other p-values at a given level of significance  
> **username_0**: Thank you. Do you mean there must be a p-value for each pair?  
> **username_1**: No the header in the first column says " p-value(treatVSctrl..." . I don't understand the quantification, but the column header is stating this is a p-value.  
> **username_2**: Agree the "consider the p-value" instruction is confusing. The first column clearly doesn't represent p-values, as p-value are probabilities and must be in the range [0, 1].  
> **username_1**: @NuclearHoagie it could represent a percentage, i.e. (0 to 1.0) * 100 , e.g. this could be a pairwise T-test. Alot of values are hovering around the 5% mark. Where there are mixed results it would be a reason for the OP to ask how to move forward.  

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## 1 answers

### Answer by username_1 on 2022-02-24. Score: 3

I don't really understand the table, particularly the 'p-value' in the header. More generally I don't really understand the experiment nor the data.
As a general rule the Wilcoxon would be a reasonable guess of the test using raw data. This is non-parametric version of the T-test without the rigid assumptions of parametrics. Control mice go in one column and subjects in the other. Scipy.stats has the test, its certainly in R (which I don't really use).
print (scipy.stats.wilcoxon(x = [1,2,3,4,5], y=[1,3,5,6,7], zero_method='wilcox', correction=False, alternative='two-sided', mode='auto', *, axis=0, nan_policy='propagate'))

There you go, filled out with some example data.


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