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 past
week I've been stumped on a certain analysis that I would like to perform on
my results.

 

My dataset consists of microarrays of RNAi experiments that affect the cell
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 would
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 the
columns are the experiments, and the rows are the genes, and the values are
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 suggested
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 guides
on how to do something like this has been very unsuccessful so far, probably
because I am not really sure how this would be named. 

I hope that someone out here understands what I am trying to do and perhaps
can give me a hint or two on what I should be looking into. 

 

Many thanks in advance.

Martin

 


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