[BioC] how to combine microarray data and phenotype data into a least squares analysis?
b.otto at uke.uni-hamburg.de
Tue Sep 30 15:21:27 CEST 2008
Hi Martin, Hi Jim,
as far as I remember, there was the time course example in the limma docu
for using lm().
Von: bioconductor-bounces at stat.math.ethz.ch
[mailto:bioconductor-bounces at stat.math.ethz.ch] Im Auftrag von James W.
Gesendet: Tuesday, September 30, 2008 2:26 PM
An: Martin Bonke
Cc: bioconductor at stat.math.ethz.ch
Betreff: Re: [BioC] how to combine microarray data and phenotype data into a
least squares analysis?
Martin Bonke wrote:
> Hello everyone,
> I am looking for some help in setting up an analysis protocol in R for my
> microarray dataset. My knowledge of R is still somewhat rudimentary, but,
> having worked with it for about half a year now I do understand the basics
> and can get most of the packages that I've needed to work. However, the
> week I've been stumped on a certain analysis that I would like to perform
> my results.
> My dataset consists of microarrays of RNAi experiments that affect the
> cycle. Part of the results is a phenotypic analysis, where I have the
> percentages of cells in the different stages of the cell cycle. Now I
> like to link this phenotypic data with the microarray data and find out
> whether the expression of genes is linked with a certain stage of the cell
> cycle. So, currently I have the matrix of all my microarray data, where
> columns are the experiments, and the rows are the genes, and the values
> their log-fold differences compared to wild type. I also have vectors that
> contain for each experiment the percentage of cells in a specific stage of
> the cell cycle (a vector for G1, one for G2, etc).
> Now I am quite at a loss on how to link these two together, I was
> to use a least squares analysis and I've been trying make lsfit() work for
> my data, but so far without luck. The documentation with these functions
> generally is rather hard to understand for me and finding descriptive
> on how to do something like this has been very unsuccessful so far,
> because I am not really sure how this would be named.
I doubt you want to use lsfit() for this analysis. More likely you
simply want to fit a linear model to your data. You could use lm() for
this (note that the help page for lsfit() even told you that you
probably want lm()), but it would be much easier to use limma.
I'm not sure if there is an example of a 'standard' linear model in the
limma User's Guide, but the only real difference between most of the
examples there and what you will want to do is to use your cell cycle
data directly rather than converting to factors first.
Read the limma User's Guide and let us know if you still have questions.
> I hope that someone out here understands what I am trying to do and
> can give me a hint or two on what I should be looking into.
> Many thanks in advance.
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