<p>The answer is simply you use both sets of values in a one-way paired ANOVA. Note that certain criteria need to be in place for ANOVA, notably the Fmax test.</p>
<p>Looking at the data 2-way ANOVA might be needed and that is more complicated, or else 2 one-way ANOVAs: I don't understand 'ctl2' and 'tube2' data. Is this a single or multiple drugs?</p>
<p>Wilcoxon signed-rank test is the first choice test because there are no prior assumptions required, which replaces both the T-test and the 1-way ANOVA.</p>
<p>Whatever you do, taking the mean and performing a T-test is not a good idea. You asked why ... because the variation between the values is critical to the significance of the test and is simply demonstrated:</p>
<p>Tube 1 value</p>
<pre><code>tube1 time X = 11
tube1 time y = 9
tube 1 mean = 10
</code></pre>
<p>Tube 2 value</p>
<pre><code>tube 2 time x = 19
tube 2 time y = 1
tube 2 mean = 10
</code></pre>
<p>Thus both would appear to have no change - they are both 10 - and yet the variation between them means they observe very different behaviours. Thats why.</p>

CC BY-SA 4.0
2022-05-14T16:37:49.737Z
2022-05-14T16:47:35.557Z
2022-05-14T16:47:35.557Z
M__
<p>I have duplicate drug efficacy experiments performed six months apart. I would like to know what is the right way to perform statistical analysis to understand fold changes after treatment with a drug.</p>
<p>Say I have</p>
<pre><code>ctrl1       tube1       ctrl2       tube2
7,732637722 10,71639307 5,454318175 8,052899428
6,69630905  8,762916793 7,441582994 9,855106201
10,35624396 11,73716043 8,976524001 11,0975511
4,29150392  4,848632578 5,901132154 7,918630661
</code></pre>
<p>and the same experiment but 6 months after (numbers are slightly different)</p>
<p>Could I take the mean between samples divided by two can work out? If not - why?</p>
<p>I would like to have statistically significant values for futher analysis.
Thanks.</p>

CC BY-SA 4.0
2022-05-14T02:53:55.480Z
What do you want to show? Taking the mean is rarely a good move except in the final analysis
M__
CC BY-SA 4.0
2022-05-14T08:09:19.193Z
I want to adjust values between the same experiments, to be able to analyze them via t-test for example. It's just these two batches are 6-months different.
Lara
<r><batch-effects>
What is the best way to perform batch ajustment between two experiments?
# What is the best way to perform batch ajustment between two experiments?

(Asked by: username_0 on 2022-05-13)

I have duplicate drug efficacy experiments performed six months apart. I would like to know what is the right way to perform statistical analysis to understand fold changes after treatment with a drug.
Say I have
ctrl1       tube1       ctrl2       tube2
7,732637722 10,71639307 5,454318175 8,052899428
6,69630905  8,762916793 7,441582994 9,855106201
10,35624396 11,73716043 8,976524001 11,0975511
4,29150392  4,848632578 5,901132154 7,918630661

and the same experiment but 6 months after (numbers are slightly different)
Could I take the mean between samples divided by two can work out? If not - why?
I would like to have statistically significant values for futher analysis.
Thanks.

**Tags:** r, batch-effects
> **Comments:**
> **username_1**: What do you want to show? Taking the mean is rarely a good move except in the final analysis  
> **username_0**: I want to adjust values between the same experiments, to be able to analyze them via t-test for example. It's just these two batches are 6-months different.  

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## 1 answers

### Answer by username_1 on 2022-05-14. Score: 0

The answer is simply you use both sets of values in a one-way paired ANOVA. Note that certain criteria need to be in place for ANOVA, notably the Fmax test.
Looking at the data 2-way ANOVA might be needed and that is more complicated, or else 2 one-way ANOVAs: I don't understand 'ctl2' and 'tube2' data. Is this a single or multiple drugs?
Wilcoxon signed-rank test is the first choice test because there are no prior assumptions required, which replaces both the T-test and the 1-way ANOVA.
Whatever you do, taking the mean and performing a T-test is not a good idea. You asked why ... because the variation between the values is critical to the significance of the test and is simply demonstrated:
Tube 1 value
tube1 time X = 11
tube1 time y = 9
tube 1 mean = 10

Tube 2 value
tube 2 time x = 19
tube 2 time y = 1
tube 2 mean = 10

Thus both would appear to have no change - they are both 10 - and yet the variation between them means they observe very different behaviours. Thats why.


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