I'm sorry to trouble you guys. I have a doubt about my design matrix.
I have RNA-seq data for 2 different genotype of trees with
0hour(control) and after treatment 3hours,24hours,and 48hours. The
experiment design like following:
Treatment
Tree H1 Ctrl 3hrs 24hrs 48hrs
Tree H2 Ctrl 3hrs 24hrs 48hrs
Tree L1 Ctrl 3hrs 24hrs 48hrs
Tree L2 Ctrl 3hrs 24hrs 48hrs
I have assigned 2 factor vectors as:
"tree" --> vector for the trees.
"trtTime" --> vector for the control and after treatment time.
I would like to study which genes/tags that are differential expressed
in these H and L trees across the after treatment time points.
Can I assign my design matrix in this way:
design <- model.matrix(~trtTime+tree) OR design
<-model.matrix(~tree+trtTime)
I may wrong in this case as I'm not a statistician nor R programming
geek. Thus, could someone kindly please light me?
I appreciate very much for your help.
Best regards,
KJ Lim
[[alternative HTML version deleted]]
I'm sorry to trouble you guys. I have a doubt about my design matrix.
I have RNA-seq data for 2 different genotype of trees with
0hour(control) and after treatment 3hours,24hours,and 48hours. The
experiment design like following:
Treatment
Tree H1 Ctrl 3hrs 24hrs 48hrs
Tree H2 Ctrl 3hrs 24hrs 48hrs
Tree L1 Ctrl 3hrs 24hrs 48hrs
Tree L2 Ctrl 3hrs 24hrs 48hrs
I have assigned 2 factor vectors as:
"tree" --> vector for the trees.
"trtTime" --> vector for the control and after treatment time.
I would like to study which genes/tags that are differential expressed
in these H and L trees across the after treatment time points.
Can I assign my design matrix in this way:
design <- model.matrix(~trtTime+tree) OR design
<-model.matrix(~tree+trtTime)
I may wrong in this case as I'm not a statistician nor R programming
geek. Thus, could someone kindly please light me?
I appreciate very much for your help.
Best regards,
KJ Lim
[[alternative HTML version deleted]]
An embedded and charset-unspecified text was scrubbed...
Name: not available
URL: <https: stat.ethz.ch="" pipermail="" bioconductor="" attachments="" 20120526="" 2fcc5dfb="" attachment-0001.pl="">
Dear KJ Lim,
You seem to have posted almost the same question to this list on three
separate occasions a week apart, and it isn't clear whether you've
taken
much notice of the help you got from Mark Robinson the first time. So
it
is indeed troubling to us guys.
Rather than trying to design a complete analysis for you, can I refer
you
to advice I gave to another poster recently:
https://www.stat.math.ethz.ch/pipermail/bioconductor/2012-May/045700.h
tml
I suggest that you try using the "longer method", explained in the
second
half of my message to that poster, to setup your design matrix, after
which you will be to draw any comparisons between the treatments that
seem
sensible to you. Of course you will need to adapt it somewhat to your
data. This is probably better than trying to use model formula in R
that
might not be clear to you.
Best wishes
Gordon
> Date: Sat, 26 May 2012 14:06:29 +0300
> From: KJ Lim <jinkeanlim at="" gmail.com="">
> To: bioconductor at r-project.org
> Cc: bioconductor at stat.math.ethz.ch
> Subject: [BioC] edgeR: design matrix
>
> I'm sorry to trouble you guys. I have a doubt about my design
matrix.
>
> I have RNA-seq data for 2 different genotype of trees with
> 0hour(control) and after treatment 3hours,24hours,and 48hours. The
> experiment design like following:
>
> Treatment
> Tree H1 Ctrl 3hrs 24hrs 48hrs
> Tree H2 Ctrl 3hrs 24hrs 48hrs
> Tree L1 Ctrl 3hrs 24hrs 48hrs
> Tree L2 Ctrl 3hrs 24hrs 48hrs
>
> I have assigned 2 factor vectors as:
> "tree" --> vector for the trees.
> "trtTime" --> vector for the control and after treatment time.
>
> I would like to study which genes/tags that are differential
expressed
> in these H and L trees across the after treatment time points.
>
> Can I assign my design matrix in this way:
>
> design <- model.matrix(~trtTime+tree) OR design
<-model.matrix(~tree+trtTime)
>
> I may wrong in this case as I'm not a statistician nor R programming
> geek. Thus, could someone kindly please light me?
>
> I appreciate very much for your help.
>
> Best regards,
> KJ Lim
______________________________________________________________________
The information in this email is confidential and
intend...{{dropped:4}}
Dear Prof Gordon Smyth,
I'm sorry for the triple postings.
I may have configured wrongly for my mailing list setting as I don't
see my
post was in mailing list loop. Thus, I resubmitted my post again. I
subscribing this mailing list via Gmane tool.
The suggestion of Prof Mark Robinson is helpful and I appreciated.
I will have a look on the post you have referred to. Thanks for your
time
and suggestion.
Once again, I'm sorry for the triple postings. I'm sorry for any
inconvenience caused.
Best regards,
KJ Lim
On 28 May 2012 12:02, Gordon K Smyth <smyth@wehi.edu.au> wrote:
> Dear KJ Lim,
>
> You seem to have posted almost the same question to this list on
three
> separate occasions a week apart, and it isn't clear whether you've
taken
> much notice of the help you got from Mark Robinson the first time.
So it
> is indeed troubling to us guys.
>
> Rather than trying to design a complete analysis for you, can I
refer you
> to advice I gave to another poster recently:
>
> https://www.stat.math.ethz.ch/**pipermail/bioconductor/2012-**
> May/045700.html<https: www.stat.math.ethz.ch="" pipermail="" bioconductor="" 2012-may="" 045700.html="">
>
> I suggest that you try using the "longer method", explained in the
second
> half of my message to that poster, to setup your design matrix,
after which
> you will be to draw any comparisons between the treatments that seem
> sensible to you. Of course you will need to adapt it somewhat to
your
> data. This is probably better than trying to use model formula in R
that
> might not be clear to you.
>
> Best wishes
> Gordon
>
> Date: Sat, 26 May 2012 14:06:29 +0300
>> From: KJ Lim <jinkeanlim@gmail.com>
>> To: bioconductor@r-project.org
>> Cc: bioconductor@stat.math.ethz.ch
>> Subject: [BioC] edgeR: design matrix
>>
>> I'm sorry to trouble you guys. I have a doubt about my design
matrix.
>>
>> I have RNA-seq data for 2 different genotype of trees with
>> 0hour(control) and after treatment 3hours,24hours,and 48hours. The
>> experiment design like following:
>>
>> Treatment
>> Tree H1 Ctrl 3hrs 24hrs 48hrs
>> Tree H2 Ctrl 3hrs 24hrs 48hrs
>> Tree L1 Ctrl 3hrs 24hrs 48hrs
>> Tree L2 Ctrl 3hrs 24hrs 48hrs
>>
>> I have assigned 2 factor vectors as:
>> "tree" --> vector for the trees.
>> "trtTime" --> vector for the control and after treatment time.
>>
>> I would like to study which genes/tags that are differential
expressed
>> in these H and L trees across the after treatment time points.
>>
>> Can I assign my design matrix in this way:
>>
>> design <- model.matrix(~trtTime+tree) OR design
>> <-model.matrix(~tree+trtTime)
>>
>> I may wrong in this case as I'm not a statistician nor R
programming
>> geek. Thus, could someone kindly please light me?
>>
>> I appreciate very much for your help.
>>
>> Best regards,
>> KJ Lim
>>
>
> ______________________________**______________________________**____
______
> The information in this email is confidential and
inte...{{dropped:10}}