Gordon,

The reason I ask is because I get an error if I attempt to run a design
formula of (~group + group:subject + group:time) and I run
estimateGLMCommonDisp(dge, design) I get the error:

Error in glmFit.default(y, design = design, dispersion = dispersion,
offset = offset,  :
  Design matrix not of full rank.  The following coefficients not estimable:


The mailing list post I am referring to, with the same error, is at the
following link:
https://stat.ethz.ch/pipermail/bioconductor/2012-November/049055.html

Am I simply writing the design formula incorrectly to still account for the
subject variation?

Regards,
Charles




On Thu, Jul 4, 2013 at 6:49 PM, Gordon K Smyth <smyth@wehi.edu.au> wrote:

> Dear Charles,
>
> There is no requirement in edgeR for equal group sizes, and never has
> been.  I am puzzled why you might think there is such an assumption. edgeR
> always allows you to use all the available data that is scientifically
> meaningful.
>
> You say that you read "the initial posting that lead to this section of
> the manual and it said to drop the samples that don't have equal numbers"
> but I do not know what you are refering to.  I have never seen such advice.
>
> Best wishes
> Gordon
>
>  Date: Wed, 3 Jul 2013 09:49:30 -0500
>> From: Charles Determan Jr <deter088@umn.edu>
>> To: Bioconductor mailing list <bioconductor@r-project.org>
>> Subject: [BioC] edgeR uneven group sizes
>>
>> Hello,
>>
>> I recently had a question regarding repeated measures RNA-seq analysis.
>> This has been thoroughly answered through an extension of the edgeR manual
>> section 3.5. However this has lead to me towards another question as I
>> attempted to extend such concepts to another experiment wherein the sample
>> size in each group is different.  For example, here is a dataframe modified
>> from the edgeR user manual concerning between and within subjects
>> comparisons (Section 3.5) and another containing specific times points to
>> explain my point, both dataframes re-numbered as recommended by the manual.
>>
>>  targets
>>>
>>    Disease Patient Treatment
>> 1   Healthy    1        None
>> 2   Healthy    1        Hormone
>> 3   Healthy    2        None
>> 4   Healthy    2        Hormone
>> 5   Healthy    3        None
>> 6   Healthy    3        Hormone
>> 7   Disease1  1       None
>> 8   Disease1  1       Hormone
>> 9   Disease1  2       None
>> 10 Disease1  2       Hormone
>> 11 Disease2  1       None
>> 12 Disease2  1       Hormone
>> 13 Disease2  2       None
>> 14 Disease2  2       Hormone
>> 15 Disease2  3       None
>> 16 Disease2  3       Hormone
>>
>>  sample_data
>>>
>>    Condition Subject Time
>> 1   control    1        0hr
>> 2   control    1        1hr
>> 3   control    1        2hr
>> 4   control    2        0hr
>> 5   control    2        1hr
>> 6   control    2        2hr
>> 7   control    3        0hr
>> 8   control    3        1hr
>> 9   control    3        2hr
>> 10 control    4        0hr
>> 11 control    4        1hr
>> 12 control    4        2hr
>> 13 Disease  1        0hr
>> 14 Disease  1        1hr
>> 15 Disease  1        2hr
>> 16 Disease  2        0hr
>> 17 Disease  2        1hr
>> 18 Disease  2        2hr
>>
>> I have read the initial posting that lead to this section of the manual
>> and it said to drop the samples that don't have equal numbers.  Now this
>> doesn't seem to be a big deal if only dropping from one group a sample or
>> two but could potentially be a problem such as above where dropping four or
>> six samples seems more of a sacrifice.  I begin to think of experiments
>> which (assuming repeated/dependent samples) group numbers very more
>> significantly as a result of difficulty acquiring samples. Are there any
>> recommendations from the community regarding such a situation?  All I have
>> found assumes that the samples within each group are equal.
>>
>> Regards,
>> --
>> Charles Determan
>> Integrated Biosciences PhD Candidate
>> University of Minnesota
>>
>
>
> ______________________________**______________________________**__________
> The information in this email is confidential and inte...{{dropped:17}}

