[BioC] Limma lmFit function and spot quality weights

Benoit benoit.loup at jouy.inra.fr
Mon Jul 27 13:41:06 CEST 2009

I'm using Limma to assess differential expression on double colour 
microarray data and have a question about the lmFit function.
When I fit linear model using lmFit, as I understood, the function uses 
the weights extracted from the MA object when present and/or specified.
Thus, I tried fitting with and without the spot quality weights and I 
found different results (not very surprising in fact).
In fact, when I used weights, zero weighted spots seemed to be removed 
from the analysis and it's here that I have a problem.

For my experiment, I compare two groups (control vs treated) in a 
classical design experiment "Two Groups: Common Reference" as describe 
in the Limma documentation.

/alternative without weights : fit=lmFit(MA,design,weights=NULL)/

The difference between the analysis with and without weights is that 
when I use weights new genes highly differentially expressed appeared.
When I control these genes, in fact they correspond to spots that are 
flagged (0) on the majority of the arrays (i.e. only one weight at 1 for 
the control and one weight at 1 for the treated). Thus for these genes 
the comparison is performed only one "control array" versus one "treated 
So is it possible to specify to lmFit that there must be a minimum of 
"1" weights or a maximum "0" weights per groups of array ?

Thank you for any help you can bring me.


Benoit Loup, PhD
UMR Biologie du Développement et Reproduction
Différenciation des Gonades et Perturbations
INRA – Domaine de Vilvert
Bâtiment Jacques Poly
78350 Jouy en Josas

Tel: 33 1 34 65 25 38
Fax: 33 1 34 65 22 41
E-mail: benoit.loup at jouy.inra.fr

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