<p><code>condition_t_vs_control</code> gives the effect of condition, <code>conditiont.time</code> is the interaction of condition and time. If you wanted to test the effect of time, use <code>name=&quot;time&quot;</code>.</p>

CC BY-SA 4.0
2021-10-19T16:35:51.870Z
Thank you, using which contrast I can get the difference of IT versus control but considering time goes from 24 hours to 48 hours?
Zizogolu
CC BY-SA 4.0
2021-10-19T19:22:02.617Z
Just condition_IT_vs_control, since time is compensated for by fitting all coefficients at the same time (let me know if that phrasing is confusing).
Devon Ryan
CC BY-SA 4.0
2021-10-26T16:06:41.350Z
Sorry @Devon Ryan I get  error for ddsTC <- DESeq(dds, test="LRT", reduced = ~ time + time:condition)                                                                                                       error in nbinomLRT(object, full = full, reduced = reduced, quiet = quiet,  : 
  less than one degree of freedom, perhaps full and reduced models are not in the correct order  what should I do please ?
Zizogolu
CC BY-SA 4.0
2021-10-26T16:24:57.067Z
Make sure to specify the full model, I don't know what that defaults to.
Devon Ryan
CC BY-SA 4.0
2021-10-26T17:25:38.090Z
Thank you so much for your answer, I think this is my full model                                              dds <- DESeqDataSetFromMatrix(countData=a,colData=design, design=~time + condition + time:condition)   am I right? design=~time + condition + time:condition  When I try to reduce that by removing reduced = ~ time + time:condition I get error
Zizogolu
CC BY-SA 4.0
2021-10-26T18:13:18.367Z
Then convert time to a factor. Perhaps that will solve the problem. You have enough samples that you shouldn’t run into this issue. BTW you can use the default Wald test instead and avoid the error.
Devon Ryan
CC BY-SA 4.0
2021-10-27T10:30:03.147Z
Thank you so much says that > ddsTC <- DESeq(dds,reduced = ~ time + time:condition)
Error in DESeq(dds, reduced = ~time + time:condition) : 
  'reduced' ignored when test='Wald'
>
Zizogolu
CC BY-SA 4.0
2021-10-27T11:48:31.797Z
Correct, there is no reduced formula with a Wald test, you just extract the appropriate coefficients with lfcShrink()
Devon Ryan
CC BY-SA 4.0
2021-10-12T20:22:38.123Z
2021-10-27T12:13:37.103Z
2021-10-27T12:13:37.103Z
Devon Ryan
<p>I have a treatment and control in two time points like this</p>
<pre><code> &gt; design
                                 X condition time
    1       CTRL_24_hrs_replicate1   control   24
    2       CTRL_24_hrs_replicate2   control   24
    3       CTRL_24_hrs_replicate3   control   24
    4  treatment_24_hrs_replicate1        t   24
    5  treatment_24_hrs_replicate2        t   24
    6  treatment_24_hrs_replicate3        t   24
    7       CTRL_48_hrs_replicate1   control   48
    8       CTRL_48_hrs_replicate2   control   48
    9       CTRL_48_hrs_replicate3   control   48
    10 treatment_48_hrs_replicate1        t   48
    11 treatment_48_hrs_replicate2        t   48
    12 treatment_48_hrs_replicate3        t   48
    &gt; 

                                               
    
</code></pre>
<p>I want to test between between treatment and control considering time point 2 hours to 4 hours</p>
<p>I have done like this</p>
<pre><code>dds &lt;- DESeqDataSetFromMatrix(countData=a,colData=design, design=~time + condition + time:condition)

ddsTC &lt;- DESeq(dds, test=&quot;LRT&quot;, reduced = ~ time + time:condition)

&gt; resultsNames(ddsTC)
[1] &quot;Intercept&quot;                &quot;condition_t_vs_control&quot;  
[3] &quot;time&quot;                     &quot;conditiont.time&quot; 

 
</code></pre>
<p>What is the difference of the results of these codes</p>
<pre><code>results(ddsTC, name=&quot;condition_t_vs_control&quot;, test=&quot;Wald&quot;)
</code></pre>
<p><strong>versus</strong></p>
<pre><code>results(ddsTC, name=&quot;conditiont.time&quot;, test=&quot;Wald&quot;)
</code></pre>
<p>Actually I want to know what <code>condition_t_vs_control</code> gives and what <code>conditiont.time</code> gives</p>
<p>Thank you so much for any intuition</p>

<rna-seq><differential-expression><deseq2>
Different contrasts in DESeq2
# Different contrasts in DESeq2

(Asked by: username_0 on 2021-10-11)

I have a treatment and control in two time points like this
 > design
                                 X condition time
    1       CTRL_24_hrs_replicate1   control   24
    2       CTRL_24_hrs_replicate2   control   24
    3       CTRL_24_hrs_replicate3   control   24
    4  treatment_24_hrs_replicate1        t   24
    5  treatment_24_hrs_replicate2        t   24
    6  treatment_24_hrs_replicate3        t   24
    7       CTRL_48_hrs_replicate1   control   48
    8       CTRL_48_hrs_replicate2   control   48
    9       CTRL_48_hrs_replicate3   control   48
    10 treatment_48_hrs_replicate1        t   48
    11 treatment_48_hrs_replicate2        t   48
    12 treatment_48_hrs_replicate3        t   48
    > 

                                               
    

I want to test between between treatment and control considering time point 2 hours to 4 hours
I have done like this
dds <- DESeqDataSetFromMatrix(countData=a,colData=design, design=~time + condition + time:condition)

ddsTC <- DESeq(dds, test="LRT", reduced = ~ time + time:condition)

> resultsNames(ddsTC)
[1] "Intercept"                "condition_t_vs_control"  
[3] "time"                     "conditiont.time" 

 

What is the difference of the results of these codes
results(ddsTC, name="condition_t_vs_control", test="Wald")

versus
results(ddsTC, name="conditiont.time", test="Wald")

Actually I want to know what condition_t_vs_control gives and what conditiont.time gives
Thank you so much for any intuition

**Tags:** rna-seq, differential-expression, deseq2

---

## 1 answers

### Answer by username_1 on 2021-10-12. Score: 3

`condition_t_vs_control` gives the effect of condition, `conditiont.time` is the interaction of condition and time. If you wanted to test the effect of time, use `name="time"`.

> **Comments:**
> **username_0**: Thank you, using which contrast I can get the difference of IT versus control but considering time goes from 24 hours to 48 hours?  
> **username_1**: Just condition_IT_vs_control, since time is compensated for by fitting all coefficients at the same time (let me know if that phrasing is confusing).  
> **username_0**: Sorry @Devon Ryan I get  error for ddsTC <- DESeq(dds, test="LRT", reduced = ~ time + time:condition)                                                                                                       error in nbinomLRT(object, full = full, reduced = reduced, quiet = quiet,  :    less than one degree of freedom, perhaps full and reduced models are not in the correct order  what should I do please ?  
> **username_1**: Make sure to specify the full model, I don't know what that defaults to.  
> **username_0**: Thank you so much for your answer, I think this is my full model                                              dds <- DESeqDataSetFromMatrix(countData=a,colData=design, design=~time + condition + time:condition)   am I right? design=~time + condition + time:condition  When I try to reduce that by removing reduced = ~ time + time:condition I get error  
> **username_1**: Then convert time to a factor. Perhaps that will solve the problem. You have enough samples that you shouldn’t run into this issue. BTW you can use the default Wald test instead and avoid the error.  
> **username_0**: Thank you so much says that > ddsTC <- DESeq(dds,reduced = ~ time + time:condition) Error in DESeq(dds, reduced = ~time + time:condition) :    'reduced' ignored when test='Wald' >  
> **username_1**: Correct, there is no reduced formula with a Wald test, you just extract the appropriate coefficients with lfcShrink()  

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