Dear Gordon,

many thanks for your answer !!

Clearly, I need to get some more information about the Intercept terms,
which is probably a classical parameters in statistics and linear modeling
(whom I'm not familiar with ...).


Also, Could you please give me your opinion using the secodn way of
designing the design matrix summarized below ?

f <- paste(target$mutation,target$**exp,sep="")
design <- model.matrix(~0+f)
colnames(design) <- levels(f)
using such a design matrix, can I use correctly the following contrast
matrix to get the gene differentially expressed between mutant and wild
type ?
cont.matrix <- makeContrasts(mu_vs_wt = (mu2+mu1) - (wt2+wt1),levels=design)


again many thanks for your time
--
Dave

2012/4/26 Gordon K Smyth <smyth@wehi.edu.au>

> Dear Dave,
>
> This is a common type of design, with two groups and a batch effect.  To
> find genes DE in mu vs wt adjusting for exp differences:
>
>  mutation <- factor(target$mutation)
>  mutation <- relevel(mutation, ref="wt")
>  exp <- factor(target$exp)
>  design <- model.matrix(~exp+mutation)
>  fit <- lmFit(x,design)
>  fit <- eBayes(fit)
>  topTable(fit, coef=3)
>
> Note that the design matrix has only 3 columns, not four as yours do. The
> batch effect requires only one extra column.
>
> Your design matrix has a redundant column because you've defined the
> intercept term twice.  The first and second columns (wt and mu) add up to
> the intercept column, and so do the 3rd and 4th columns (exp1 and exp2).
>
> You say that limma failed on your design matrix, but really it didn't. It
> correctly removed the redundant column from your design matrix, and gave
> you a warning to let you know.
>
> Best wishes
> Gordon
>
> ---------------- original message ----------------
> [BioC] Design and not estimable coefficients
> Dave Canvhet dcanvhet at gmail.com
> Wed Apr 25 18:08:59 CEST 2012
>
> Dear all,
>
> I have the following target
>
> target
>
> filename mutation exp
> sample1.cel   wt     1
> sample2.cel   wt     1
> sample3.cel  mu     1
> sample4.cel  mu     1
> sample5.cel   wt     2
> sample6.cel   mu     2
> sample7.cel  mu     2
> sample8.cel  mu     2
>
>
> I'm intersting in mu vs wt, but taking into account the exp factor (which
> seems having most impact on signal as reveal by a PCA : PC1 seems to be
> assoiated to exp)
>
> So I've set the following design so as to model the resulting signal as a
> combination of these two factor:
>
> design = matrix(0, ncol=4, nrow = 8)
> design[which(target$mutation == "wt"),1] = 1
> design[which(target$mutation == "mu"),2] = 1
> design[which(target$exp == 1),3] = 1
> design[which(target$exp == 2),4] = 1
> colnames(design) = c("wt","mu","exp1","exp2")
>
>
> design
>     wt mu exp1 exp2
> [1,]  1  0    1    0
> [2,]  1  0    1    0
> [3,]  0  1    1    0
> [4,]  0  1    1    0
> [5,]  1  0    0    1
> [6,]  0  1    0    1
> [7,]  0  1    0    1
> [8,]  0  1    0    1
>
> fit = lmFit(x, design)
> but it failed (even before doinf any contrast matrix), raising the
> following error :
> Coefficients not estimable: exp2
>
> Can you please tell me what is wrong is such design ?
>
>
> SO I've used the approach described in Limma User guide :
> f <- paste(target$mutation,target$**exp,sep="")
> f <- factor(f)
> [1] wt1 wt1 mu1 mu1 wt2 mu2 mu2 mu2
> Levels: mu1 mu2 wt1 wt2
>
>
> design <- model.matrix(~0+f)
> colnames(design) <- levels(f)
> design
>
>  mu1 mu2 wt1 wt2
> 1   0   0   1   0
> 2   0   0   1   0
> 3   1   0   0   0
> 4   1   0   0   0
> 5   0   0   0   1
> 6   0   1   0   0
> 7   0   1   0   0
> 8   0   1   0   0
> attr(,"assign")
> [1] 1 1 1 1
> attr(,"contrasts")
> attr(,"contrasts")$f
> [1] "contr.treatment"
>
> using such a design matrix, can I use correctly the following contrast
> matrix to get the gene differentially expressed between mutant and wild
> type ?
> cont.matrix <- makeContrasts(mu_vs_wt = (mu2+mu1) -
> (wt2+wt1),levels=design)
>
>
> many thanks for you answer.
>
> ==
> Dave Canvhet
>
> ______________________________**______________________________**__________
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