[BioC] limma question: direct two-color design & modeling individual subject effects

Paul Shannon pshannon at systemsbiology.org
Wed May 2 01:44:28 CEST 2007

Hi Gordon,

Thanks very much for your reply.  Your suggestion makes sense -- 
that I should explore the use of lm () on one gene's MA$M values, until I can
fit the experimental factors I care about, and only then return to
limma.  When I do get that far, I'll probably be back with another
question or two. :}

You asked a couple of questions about some confusing aspects of my
plot at http://gaggle/pshannon/tmp/7346.png (which I have since 

  > ... you haven't explained exactly what is in your picture.  The
  > data values on the y-axis don't appear to be the M-values you used
  > to fit the linear model, because we don't see the up-down pattern
  > we'd expect to see from dye-swaps. 

I failed to explain that I multiplied all of the measured dye-swaps
(cy5/cy3) by -1 so that their ratios would be easy to compare to the
(cy3/cy5) ratios.

 > How have you obtained "fitted values"?

I multiplied the model by the fitted coefficients, summed the resultant vector,
and corrected for dye swap:

    # corrector = 1 or -1
    corrector * sum (model [slide,] * efit$coef [row,]))

> Note that M-values are already log-ratios, so it doesn't make sense
> to write "log2M".

Good point...

Thanks again for your reply!  I'd reached the end of my own resources; now I can
get to work again.


 - Paul

  > lmFit() simply does least squares regression.  It gives the same coefficients that you would get
  > from lm() for each gene.  I suggest that you extract the M-value data for one gene, and experiment
  > with fitting the data using lm(), until you're satisfied that you understand the parametrization
  > and fitted values.

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