Thanks, Moshe and James

Any question about confounding factor if you or anybody can help me.

Actually, I have many different phenotype of samples here, such as weight,
age, cholesterol. I want to find the differential expressed genes between
case and control (say:high blood pressure vs health ). I found some
significant genes when I adjust the age, weight. However, when I adjust the
cholesterol level, these genes became not significant (*P* values larger).
So either cholesterol is confounding factor which I should control , or
these differential expressed genes control the cholesterol level which I
should not adjust. I am not sure which assumption is right. I am thinking
it should be the last one.

2012/4/4 Moshe Olshansky <olshansky@wehi.edu.au>

> You are right.  My mistake. Sorry...
>
> > Hi, Moshe,
> >
> > I still did not get it. How to use your method?
> >
> > lmFit<-(x,design) which x allow missing values, but design didn't.
> >
> > usually, I want to adjust batch effects, I will include it in design
> > matrix, i.e.
> >                case  control  batch
> >                  1        0           1
> >                   0       1           1
> >                   1       0           2
> > which x is expression matrix, and then I will get the differential
> > expressed gene between case and control with adjusting batch.
> >
> > In this case, I just want to simply use "weight" instead of  "batch" in
> > design matrix. But unfortunately, you can't have missing values in design
> > matrix which I think it's very often if we have many samples.
> >
> > 2012/4/4 Moshe Olshansky <olshansky@wehi.edu.au>
> >
> >> Why do you want to include weight in the design matrix?
> >> It may be more reasonable to include weight in the linear model, i.e.
> >> expression ~ condition + weight and then your design matrix will have
> >> two
> >> columns (if there are two conditions): the first column will contain 0's
> >> and 1's depending on the condition (group) of the patient while the
> >> second
> >> one will be identically 1. And then the weights of the patients will be
> >> in
> >> the values (i.e. the second argument to lmFit) which I believe allows
> >> missing values.
> >> But this implicitly assumes that the weight (or some function of it -
> >> you
> >> can use any transformation you like) contributes additively (and
> >> linearly)
> >> to the expression (or it's logarithm).
> >>
> >> Moshe.
> >>
> >> > Hi,
> >> >
> >> > I am using Limma to get differential expressed genes between case and
> >> > control samples. In addition, I want to adjust the "weight" of
> >> patients
> >> by
> >> > including it in the "design" matrix. However, one weight value of a
> >> > patient
> >> > is missing, so I have a "NA" in design matrix. When I run lmFit, I got
> >> the
> >> > error message :Error in qr.default(x) : NA/NaN/Inf in foreign function
> >> > call
> >> > (arg 1).
> >> >
> >> > Can limma handle the missing value in design matrix or I have to
> >> remove
> >> > this sample ?
> >> >
> >> > Thanks,
> >> >
> >> > Chen
> >> >
> >> >       [[alternative HTML version deleted]]
> >> >
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