Dear Bioconductors,
Please can anyone advise on this?
One colour array; I have 8 mice. Each mouse can have a genotype of WT
or KO.
For each mouse, there are 2 samples, one with treatment and one
control.
Is this a combined paired and factorial design and if so, how can I
construct a design matrix?
There is also Male and Female (half mice of each group).
Thank you for any advice on this.
Kind regards,
John.
Dear John,
If all the mice were the same sex, then it would be a 2x2 factorial.
Having both male and female, you have some difficult choices to make,
depending on whether the treatment or the KO can have sex-linked
effects.
In the simplest case that the answer is no in both cases, then you
might
make a design matrix from
design <- model.matrix(~sex+genotype+genotype:treatment)
in order to test for treatment effects within each genotype.
In any case, designing an analysis depends on the questions you want
to
answer as well as the treatments you have.
Best wishes
Gordon
> Date: Wed, 2 May 2012 15:34:46 +0100
> From: john herbert <arraystruggles at="" gmail.com="">
> To: <bioconductor at="" stat.math.ethz.ch="">
> Subject: [BioC] Limma analyses;paired and/or factorial design?
>
> Dear Bioconductors,
> Please can anyone advise on this?
>
> One colour array; I have 8 mice. Each mouse can have a genotype of
WT or KO.
>
> For each mouse, there are 2 samples, one with treatment and one
control.
>
> Is this a combined paired and factorial design and if so, how can I
> construct a design matrix?
>
> There is also Male and Female (half mice of each group).
>
> Thank you for any advice on this.
>
> Kind regards,
>
> John.
______________________________________________________________________
The information in this email is confidential and
intend...{{dropped:4}}
If each mouse receives both treatments, this is a split plot design
with genotype:gender as the whole plot factor and treatment as the
subplot treatment. You need to have a block effect for mouse. Since
these are 1 color arrays, you can simply put in mouse as block.
--Naomi
At 08:00 PM 5/3/2012, Gordon K Smyth wrote:
>Dear John,
>
>If all the mice were the same sex, then it would be a 2x2 factorial.
>
>Having both male and female, you have some difficult choices to
>make, depending on whether the treatment or the KO can have
>sex-linked effects. In the simplest case that the answer is no in
>both cases, then you might make a design matrix from
>
> design <- model.matrix(~sex+genotype+genotype:treatment)
>
>in order to test for treatment effects within each genotype.
>
>In any case, designing an analysis depends on the questions you want
>to answer as well as the treatments you have.
>
>Best wishes
>Gordon
>
>>Date: Wed, 2 May 2012 15:34:46 +0100
>>From: john herbert <arraystruggles at="" gmail.com="">
>>To: <bioconductor at="" stat.math.ethz.ch="">
>>Subject: [BioC] Limma analyses;paired and/or factorial design?
>>
>>Dear Bioconductors,
>>Please can anyone advise on this?
>>
>>One colour array; I have 8 mice. Each mouse can have a genotype of
WT or KO.
>>
>>For each mouse, there are 2 samples, one with treatment and one
control.
>>
>>Is this a combined paired and factorial design and if so, how can I
>>construct a design matrix?
>>
>>There is also Male and Female (half mice of each group).
>>
>>Thank you for any advice on this.
>>
>>Kind regards,
>>
>>John.
>
>_____________________________________________________________________
_
>The information in this email is confidential and
inten...{{dropped:11}}
Hi Naomi and John,
True enough. Obviously I don't read carefully enough.
If each mouse gets both treatments, then it's a paired (or split-plot
or
repeated measures or multilevel) design. If all the mice were the
same
genotype and sex, then the design would be simply:
design <- model.matrix(~mouse+treatment)
as in Section 8.3 of the limma User's Guide.
However, with sex and genotype differences it can be complicated,
depending on what John wants to test, as the experiment has multiple
levels of variation (mouse and array). If John simply wants to test
for a
treatment effect within each genotype, then the design might be:
design <- model.matrix(~mouse+treatmentA+treatmentB)
where treatmentA=1 for genotype A mice receiving treatment and zero
otherwise, and similarly for treatmentB. This assumes no interaction
between treatment and sex.
If however John wants to test for baseline differences between
genotypes
or between sexes, then it is necesssary to recover information from
the
between-mice strata, so mouse needs to be entered as a random blocking
factor.
Best wishes
Gordon
On Thu, 3 May 2012, Naomi Altman wrote:
> If each mouse receives both treatments, this is a split plot design
with
> genotype:gender as the whole plot factor and treatment as the
subplot
> treatment. You need to have a block effect for mouse. Since these
are 1
> color arrays, you can simply put in mouse as block.
>
> --Naomi
>
> At 08:00 PM 5/3/2012, Gordon K Smyth wrote:
>> Dear John,
>>
>> If all the mice were the same sex, then it would be a 2x2
factorial.
>>
>> Having both male and female, you have some difficult choices to
make,
>> depending on whether the treatment or the KO can have sex-linked
effects.
>> In the simplest case that the answer is no in both cases, then you
might
>> make a design matrix from
>>
>> design <- model.matrix(~sex+genotype+genotype:treatment)
>>
>> in order to test for treatment effects within each genotype.
>>
>> In any case, designing an analysis depends on the questions you
want to
>> answer as well as the treatments you have.
>>
>> Best wishes
>> Gordon
>>
>>> Date: Wed, 2 May 2012 15:34:46 +0100
>>> From: john herbert <arraystruggles at="" gmail.com="">
>>> To: <bioconductor at="" stat.math.ethz.ch="">
>>> Subject: [BioC] Limma analyses;paired and/or factorial design?
>>>
>>> Dear Bioconductors,
>>> Please can anyone advise on this?
>>>
>>> One colour array; I have 8 mice. Each mouse can have a genotype of
WT or
>>> KO.
>>>
>>> For each mouse, there are 2 samples, one with treatment and one
control.
>>>
>>> Is this a combined paired and factorial design and if so, how can
I
>>> construct a design matrix?
>>>
>>> There is also Male and Female (half mice of each group).
>>>
>>> Thank you for any advice on this.
>>>
>>> Kind regards,
>>>
>>> John.
______________________________________________________________________
The information in this email is confidential and
intend...{{dropped:4}}