[BioC] Differential drug effect on clinical groups

James W. MacDonald jmacdon at uw.edu
Wed Jun 20 18:35:58 CEST 2012


Hi Dave,

On 6/20/2012 12:07 PM, Dave Canvhet wrote:
> Hi James,
>
>     This isn't really clear, and I might be way off base with this
>     answer, but it looks to me like you are after an interaction term.
>     If I were to restate, I would say that you are looking for genes
>     that react differently to treatment between the long lived
>     integrine positive samples and the short lived integrine negative
>     samples.
>
>
> This is exactly what I want, so thanks to your clear restate.
>
>     If true, this isn't difficult to set up, although I wouldn't do it
>     the way you are. Personally, I would combine the samples into four
>     types, based on life and integrine (where for brevity, life is
>     long/short and integrine is +/-):
>
>     long+
>     short+
>     long-
>     short-
>
>     Now your interaction as I understand it will only utilize the
>     long+ and short- samples, so you would restrict your samples to
>     just those samples that fulfill those criteria. Then you could
>     make a lifeinteg factor that is long+ and short- and create a
>     design matrix 
>
>
>     design <- model.matrix(~drug*lifeinteg)
>
>
>
> OK I still to progress on the differences between interaction model 
> and additive model (with which I'm more familiar)
> Do you think it will be useful to set up an Intercept ?
> design <- model.matrix(~0+drug*lifeinteg)

There won't be a difference. As an example:

 > drug <- factor(rep(1:2, 4))
 > lifeinteg <- factor(rep(1:2, each = 4))
 > model.matrix(~drug*lifeinteg)
   (Intercept) drug2 lifeinteg2 drug2:lifeinteg2
1           1     0          0                0
2           1     1          0                0
3           1     0          0                0
4           1     1          0                0
5           1     0          1                0
6           1     1          1                1
7           1     0          1                0
8           1     1          1                1
attr(,"assign")
[1] 0 1 2 3
attr(,"contrasts")
attr(,"contrasts")$drug
[1] "contr.treatment"

attr(,"contrasts")$lifeinteg
[1] "contr.treatment"

 > model.matrix(~0+drug*lifeinteg)
   drug1 drug2 lifeinteg2 drug2:lifeinteg2
1     1     0          0                0
2     0     1          0                0
3     1     0          0                0
4     0     1          0                0
5     1     0          1                0
6     0     1          1                1
7     1     0          1                0
8     0     1          1                1
attr(,"assign")
[1] 1 1 2 3
attr(,"contrasts")
attr(,"contrasts")$drug
[1] "contr.treatment"

attr(,"contrasts")$lifeinteg
[1] "contr.treatment"

So the interaction term will be drug2:lifeinteg2 regardless of how you 
specify the model.

Best,

Jim


>
> Again many for your time and your help.
>
> Bests
> --
> Dave
>
>
>
>
>     and the lifeinteg2 coefficient is the interaction, and gives you
>     the genes that react differently to the drug based on being long+
>     or short-.
>
>     Best,
>
>     Jim
>
>
>
>
>
>         I've set up my design matrix (target is below):
>
>         drug = as.factor(targetATH$drug)
>
>         integr  = as.factor(targetATH$integrin)
>
>         lifetime = as.factor(targetATH$lifetime)
>
>         design = model.matrix(~drug+integr+lifetime)
>
>         I can't figure out how to set up the correct contrast matrix
>         to get the
>         coefficient I want.
>         I would be very grateful if you could give any pieces of
>         advices for that.
>         I hope I have enough sample to get enough power to detect some
>         genes.
>
>
>         many thanks by advance, best regards,
>         --
>         Dave
>
>
>          target :
>
>             targetATH
>
>                FileName drug lifetime integrin
>         1   sample1.cel    Y        S        +
>         2   sample2.cel    Y        S        +
>         3   sample3.cel    Y        S        +
>         4   sample4.cel    Y        S        +
>         5   sample5.cel    Y        L        +
>         6   sample6.cel    Y        L        +
>         7   sample7.cel    Y        L        +
>         8   sample8.cel    Y        L        +
>         9   sample9.cel    Y        S        -
>         10 sample10.cel    Y        S        -
>         11 sample11.cel    Y        S        -
>         12 sample12.cel    Y        S        -
>         13 sample13.cel    Y        L        -
>         14 sample14.cel    Y        L        -
>         15 sample15.cel    Y        L        -
>         16 sample16.cel    Y        L        -
>         17 sample17.cel    N        S        +
>         18 sample18.cel    N        S        +
>         19 sample19.cel    N        S        +
>         20 sample20.cel    N        S        +
>         21 sample21.cel    N        L        +
>         22 sample22.cel    N        L        +
>         23 sample23.cel    N        L        +
>         24 sample24.cel    N        L        +
>         25 sample25.cel    N        S        -
>         26 sample26.cel    N        S        -
>         27 sample27.cel    N        S        -
>         28 sample28.cel    N        S        -
>         29 sample29.cel    N        L        -
>         30 sample30.cel    N        L        -
>         31 sample31.cel    N        L        -
>         32 sample32.cel    N        L        -
>
>                [[alternative HTML version deleted]]
>
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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



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