[BioC] Limma; a kind of extended paired analyses with or without treatment

[James W. MacDonald]

Ugh. Jumped the gun. This does *not* require you to fit a random effects 
model, as you have done every treatment to cells from each patient. You 
can just block on Sample and then make your comparisons.

In other words, if you add Sample to your design matrix, you will in 
effect be removing the patient-specific effect. Something like

design <- model.matrix(~0+treatment*time+sample)

Best,

Jim



On 10/11/2012 2:27 PM, john herbert wrote:
> Thanks James,
> This does not have time course but judging by your answer, I can just
> add this in, in place of, say, tissue.
>
> Kind regards,
>
> John.
>
> On Thu, Oct 11, 2012 at 7:23 PM, James W. MacDonald<jmacdon at uw.edu>  wrote:
>> Hi John,
>>
>>
>> On 10/11/2012 2:15 PM, john herbert wrote:
>>> Dear all.
>>> I have been pondering about constructing a design matrix based on the
>>> Limma user guide, where I combine a time course with a paired
>>> analyses. The targets file looks like;
>>>
>>> Sample  treatment       time
>>> 1       control 24
>>> 1       control 72
>>> 1       control 0
>>> 1       treatment       24
>>> 1       treatment       72
>>> 2       control 24
>>> 2       control 72
>>> 2       control 0
>>> 2       treatment       24
>>> 2       treatment       72
>>> 3       control 24
>>> 3       control 72
>>> 3       control 0
>>> 3       treatment       24
>>> 3       treatment       72
>>>
>>> Sample number refers to an individuals cancer cells, treatment refers
>>> to added drug or not and numbers are in hours (time elapsed). So it is
>>> a kind of paired, as patient variability is to be considered. The
>>> control sample at 0 is the same as treatment at time 0 as these are
>>> the same cells without any time/treatment.
>>>
>>> Please could someone help me understand how I can construct a design
>>> matrix and to understand how I can extract differently expressed genes
>>> that come about due to time, due to treatment and interaction of them
>>> both.
>>>
>>> Any pointers appreciated, though I am trying to see if the examples in
>>> the manual can be applied to this scenario.
>>
>> See the multi-level experiment example in the user guide, starting on p. 47.
>>
>> Best,
>>
>> Jim
>>
>>> Thank you.
>>>
>>> John.
>>>
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>>
>> --
>> James W. MacDonald, M.S.
>> Biostatistician
>> University of Washington
>> Environmental and Occupational Health Sciences
>> 4225 Roosevelt Way NE, # 100
>> Seattle WA 98105-6099
>>

-- 
James W. MacDonald, M.S.
Biostatistician
University of Washington
Environmental and Occupational Health Sciences
4225 Roosevelt Way NE, # 100
Seattle WA 98105-6099