<p>Your design is <code>~ Age</code> so you can only test how Age affects each gene and it is assumed that the effect is linear. If you would like to test the effect of sub type on gene expression you'll have to introduce it to the design e.g. <code>~ Age + Sub.Type</code> (or whatever the column name is). This will not test how the effect of Age is different between between the subtypes, if you would like to test this you will have to include the interaction term in the design formula: <code>~ Age + Sub.Type + Age:Sub.Type</code>, this design have a lot of parameters and you will need a lot of samples to have a reliable test.</p>
<p>Hope it helped</p>

CC BY-SA 4.0
2022-06-28T17:50:50.207Z
thank you after reading other similar queries i had a bit of clarity, one more question if I m testing only the age what goes into the intercept in case of factor or category I normally set a reference level  in case of numerical variable what is turned into intercept is it the lowest age is turned into intercept?
kcm
CC BY-SA 4.0
2022-06-29T05:57:58.830Z
Age is treated as linear so Age=0 goes to the intercept and the Age coefficient times the age is the Age effect for each individual.
asafpr
CC BY-SA 4.0
2022-06-29T06:44:32.987Z
`Age=0` this although i don't have it in my data but what I understand is the lowest age is turned into intercept?
kcm
CC BY-SA 4.0
2022-06-29T07:13:39.730Z
The intercept is the "background" effect without the effect of the covariates in the formula, it's the beta0 in the formula y = beta0 + Age*beta1
asafpr
CC BY-SA 4.0
2022-06-29T07:30:52.733Z
"background" I would request what would you mean by that in biological term can you explain ?I understand the beta0 when age is 0 but what it means biologically that would be really helpful
kcm
CC BY-SA 4.0
2022-06-29T08:00:23.790Z
That the gene expression level at birth is the intercept value. Is it biologically true? I doubt it but that's the model.
asafpr
CC BY-SA 4.0
2022-06-29T08:15:14.953Z
thank you for making it easier to understand .
kcm
CC BY-SA 4.0
2022-06-28T12:34:26.077Z
2022-06-28T12:34:26.077Z
asafpr
<p>This is regarding the single factor design For example if I have Age or other continuous numerical variable how to provide that into the design formula.</p>
<p>For this <a href="https://support.bioconductor.org/p/130612/#9145075" rel="nofollow noreferrer">post</a> do i need to 'You could dichotomise your continuous variables into meaningful groups' or it can go without it?</p>
<pre><code>My simple design 


dds &lt;- DESeqDataSetFromMatrix(countData=rpkm_ordered, colData=coldata, design= ~ Age)

dds &lt;- estimateSizeFactors(dds)

dds &lt;- estimateDispersions(dds)

dds  &lt;- DESeq(dds, parallel = TRUE)
</code></pre>
<p>Here in case of metadata/coldata I m giving a single numerical value which is Blast percentage. In case of sample I have like 5 sub-types from M0 to  M5.</p>
<p>Now for the interpretation part How do I interpret the result?</p>
<p>Would it be as such</p>
<p>the expression differences between the my subtypes due to 'Age'</p>
<p>I'm bit confused since in my coldata I'm not providing any information regarding my subtypes. So when I run this</p>
<pre><code>resultsNames(dds)  I get this 


[1] &quot;Intercept&quot;     &quot;Age&quot;
</code></pre>
<p>So if I would like to know if there is a difference between which subtype due to this Age variable how do i know or my question is violating the statistical design and rationale here?</p>
<p>To know the differences in subtype I have do which is proving the <code>FAB</code> which are basically my sub-types information where I have tested pairwise.</p>
<p>I would like to know if I give any numerical variable to my design how do I interpret output?</p>
<p>Any suggestion or help would be really appreciated.</p>

<deseq2>
deseq2 single factor design output interpretation
# deseq2 single factor design output interpretation

(Asked by: username_0 on 2022-06-24)

This is regarding the single factor design For example if I have Age or other continuous numerical variable how to provide that into the design formula.
For this post do i need to 'You could dichotomise your continuous variables into meaningful groups' or it can go without it?
My simple design 


dds <- DESeqDataSetFromMatrix(countData=rpkm_ordered, colData=coldata, design= ~ Age)

dds <- estimateSizeFactors(dds)

dds <- estimateDispersions(dds)

dds  <- DESeq(dds, parallel = TRUE)

Here in case of metadata/coldata I m giving a single numerical value which is Blast percentage. In case of sample I have like 5 sub-types from M0 to  M5.
Now for the interpretation part How do I interpret the result?
Would it be as such
the expression differences between the my subtypes due to 'Age'
I'm bit confused since in my coldata I'm not providing any information regarding my subtypes. So when I run this
resultsNames(dds)  I get this 


[1] "Intercept"     "Age"

So if I would like to know if there is a difference between which subtype due to this Age variable how do i know or my question is violating the statistical design and rationale here?
To know the differences in subtype I have do which is proving the FAB which are basically my sub-types information where I have tested pairwise.
I would like to know if I give any numerical variable to my design how do I interpret output?
Any suggestion or help would be really appreciated.

**Tags:** deseq2

---

## 1 answers

### Answer by username_1 on 2022-06-28. Score: 2 | Accepted answer

Your design is `~ Age` so you can only test how Age affects each gene and it is assumed that the effect is linear. If you would like to test the effect of sub type on gene expression you'll have to introduce it to the design e.g. `~ Age + Sub.Type` (or whatever the column name is). This will not test how the effect of Age is different between between the subtypes, if you would like to test this you will have to include the interaction term in the design formula: `~ Age + Sub.Type + Age:Sub.Type`, this design have a lot of parameters and you will need a lot of samples to have a reliable test.

Hope it helped

> **Comments:**
> **username_0**: thank you after reading other similar queries i had a bit of clarity, one more question if I m testing only the age what goes into the intercept in case of factor or category I normally set a reference level  in case of numerical variable what is turned into intercept is it the lowest age is turned into intercept?  
> **username_1**: Age is treated as linear so Age=0 goes to the intercept and the Age coefficient times the age is the Age effect for each individual.  
> **username_0**: `Age=0` this although i don't have it in my data but what I understand is the lowest age is turned into intercept?  
> **username_1**: The intercept is the "background" effect without the effect of the covariates in the formula, it's the beta0 in the formula y = beta0 + Age*beta1  
> **username_0**: "background" I would request what would you mean by that in biological term can you explain ?I understand the beta0 when age is 0 but what it means biologically that would be really helpful  
> **username_1**: That the gene expression level at birth is the intercept value. Is it biologically true? I doubt it but that's the model.  
> **username_0**: thank you for making it easier to understand .  

---

